1
|
Jiang X, Zhou R, Jiang F, Yan Y, Zhang Z, Wang J. Construction of diagnostic models for the progression of hepatocellular carcinoma using machine learning. Front Oncol 2024; 14:1401496. [PMID: 38812780 PMCID: PMC11133637 DOI: 10.3389/fonc.2024.1401496] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/15/2024] [Accepted: 04/29/2024] [Indexed: 05/31/2024] Open
Abstract
Liver cancer is one of the most prevalent forms of cancer worldwide. A significant proportion of patients with hepatocellular carcinoma (HCC) are diagnosed at advanced stages, leading to unfavorable treatment outcomes. Generally, the development of HCC occurs in distinct stages. However, the diagnostic and intervention markers for each stage remain unclear. Therefore, there is an urgent need to explore precise grading methods for HCC. Machine learning has emerged as an effective technique for studying precise tumor diagnosis. In this research, we employed random forest and LightGBM machine learning algorithms for the first time to construct diagnostic models for HCC at various stages of progression. We categorized 118 samples from GSE114564 into three groups: normal liver, precancerous lesion (including chronic hepatitis, liver cirrhosis, dysplastic nodule), and HCC (including early stage HCC and advanced HCC). The LightGBM model exhibited outstanding performance (accuracy = 0.96, precision = 0.96, recall = 0.96, F1-score = 0.95). Similarly, the random forest model also demonstrated good performance (accuracy = 0.83, precision = 0.83, recall = 0.83, F1-score = 0.83). When the progression of HCC was categorized into the most refined six stages: normal liver, chronic hepatitis, liver cirrhosis, dysplastic nodule, early stage HCC, and advanced HCC, the diagnostic model still exhibited high efficacy. Among them, the LightGBM model exhibited good performance (accuracy = 0.71, precision = 0.71, recall = 0.71, F1-score = 0.72). Also, performance of the LightGBM model was superior to that of the random forest model. Overall, we have constructed a diagnostic model for the progression of HCC and identified potential diagnostic characteristic gene for the progression of HCC.
Collapse
Affiliation(s)
- Xin Jiang
- Innovation Center for Cancer Research, Clinical Oncology School of Fujian Medical University, Fujian Cancer Hospital, Fuzhou, China
- Fujian Key Laboratory of Advanced Technology for Cancer Screening and Early Diagnosis, Fuzhou, China
| | - Ruilong Zhou
- Faculty of Medicine, The Chinese University of Hong Kong, Hong Kong, Hong Kong SAR, China
| | - Fengle Jiang
- Innovation Center for Cancer Research, Clinical Oncology School of Fujian Medical University, Fujian Cancer Hospital, Fuzhou, China
- Fujian Key Laboratory of Advanced Technology for Cancer Screening and Early Diagnosis, Fuzhou, China
| | - Yanan Yan
- Innovation Center for Cancer Research, Clinical Oncology School of Fujian Medical University, Fujian Cancer Hospital, Fuzhou, China
- Fujian Key Laboratory of Advanced Technology for Cancer Screening and Early Diagnosis, Fuzhou, China
| | - Zheting Zhang
- Innovation Center for Cancer Research, Clinical Oncology School of Fujian Medical University, Fujian Cancer Hospital, Fuzhou, China
- Fujian Key Laboratory of Advanced Technology for Cancer Screening and Early Diagnosis, Fuzhou, China
| | - Jianmin Wang
- Innovation Center for Cancer Research, Clinical Oncology School of Fujian Medical University, Fujian Cancer Hospital, Fuzhou, China
- Fujian Key Laboratory of Advanced Technology for Cancer Screening and Early Diagnosis, Fuzhou, China
| |
Collapse
|
2
|
Bo Z, Song J, He Q, Chen B, Chen Z, Xie X, Shu D, Chen K, Wang Y, Chen G. Application of artificial intelligence radiomics in the diagnosis, treatment, and prognosis of hepatocellular carcinoma. Comput Biol Med 2024; 173:108337. [PMID: 38547656 DOI: 10.1016/j.compbiomed.2024.108337] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/28/2023] [Revised: 03/04/2024] [Accepted: 03/17/2024] [Indexed: 04/17/2024]
Abstract
Hepatocellular carcinoma (HCC) is the most common type of primary liver cancer, with an increasing incidence and poor prognosis. In the past decade, artificial intelligence (AI) technology has undergone rapid development in the field of clinical medicine, bringing the advantages of efficient data processing and accurate model construction. Promisingly, AI-based radiomics has played an increasingly important role in the clinical decision-making of HCC patients, providing new technical guarantees for prediction, diagnosis, and prognostication. In this review, we evaluated the current landscape of AI radiomics in the management of HCC, including its diagnosis, individual treatment, and survival prognosis. Furthermore, we discussed remaining challenges and future perspectives regarding the application of AI radiomics in HCC.
Collapse
Affiliation(s)
- Zhiyuan Bo
- Department of Hepatobiliary Surgery, The First Affiliated Hospital of Wenzhou Medical University, Wenzhou, China
| | - Jiatao Song
- Department of Hepatobiliary Surgery, The First Affiliated Hospital of Wenzhou Medical University, Wenzhou, China
| | - Qikuan He
- Department of Hepatobiliary Surgery, The First Affiliated Hospital of Wenzhou Medical University, Wenzhou, China
| | - Bo Chen
- Department of Hepatobiliary Surgery, The First Affiliated Hospital of Wenzhou Medical University, Wenzhou, China
| | - Ziyan Chen
- Department of Hepatobiliary Surgery, The First Affiliated Hospital of Wenzhou Medical University, Wenzhou, China
| | - Xiaozai Xie
- Department of Hepatobiliary Surgery, The First Affiliated Hospital of Wenzhou Medical University, Wenzhou, China
| | - Danyang Shu
- Department of Hepatobiliary Surgery, The First Affiliated Hospital of Wenzhou Medical University, Wenzhou, China
| | - Kaiyu Chen
- Department of Hepatobiliary Surgery, The First Affiliated Hospital of Wenzhou Medical University, Wenzhou, China.
| | - Yi Wang
- Department of Epidemiology and Biostatistics, School of Public Health and Management, Wenzhou Medical University, Wenzhou, China.
| | - Gang Chen
- Department of Hepatobiliary Surgery, The First Affiliated Hospital of Wenzhou Medical University, Wenzhou, China; Zhejiang-Germany Interdisciplinary Joint Laboratory of Hepatobiliary-Pancreatic Tumor and Bioengineering, the First Affiliated Hospital of Wenzhou Medical University, Wenzhou, Zhejiang, China.
| |
Collapse
|
3
|
Sridhar GR, Siva Prasad AV, Lakshmi G. Scope and caveats: Artificial intelligence in gastroenterology. Artif Intell Gastroenterol 2024; 5:91607. [DOI: 10.35712/aig.v5.i1.91607] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 01/23/2024] [Revised: 02/18/2024] [Accepted: 03/29/2024] [Indexed: 04/29/2024] Open
Abstract
The use of Artificial intelligence (AI) has evolved from its mid-20th century origins to playing a pivotal tool in modern medicine. It leverages digital data and computational hardware for diverse applications, including diagnosis, prognosis, and treatment responses in gastrointestinal and hepatic conditions. AI has had an impact in diagnostic techniques, particularly endoscopy, ultrasound, and histopathology. AI encompasses machine learning, natural language processing, and robotics, with machine learning being central. This involves sophisticated algorithms capable of managing complex datasets, far surpassing traditional statistical methods. These algorithms, both supervised and unsupervised, are integral for interpreting large datasets. In liver diseases, AI's non-invasive diagnostic applications, particularly in non-alcoholic fatty liver disease, and its role in characterizing hepatic lesions is promising. AI aids in distinguishing between normal and cirrhotic livers and improves the accuracy of lesion characterization and prognostication of hepatocellular carcinoma. AI enhances lesion identification during endoscopy, showing potential in the diagnosis and management of early-stage esophageal carcinoma. In peptic ulcer disease, AI technologies influence patient management strategies. AI is useful in colonoscopy, particularly in detecting smaller colonic polyps. However, its applicability in non-academic settings requires further validation. Addressing these issues is vital for harnessing the potential of AI. In conclusion, while AI offers transformative possibilities in gastroenterology, careful integration and balancing of technical possibilities with ethical and practical application, is essential for optimal use.
Collapse
Affiliation(s)
| | - Atmakuri V Siva Prasad
- Department of Gastroenterology, Institute of Gastroenterology, Visakhapatnam 530003, India
| | - Gumpeny Lakshmi
- Department of Internal Medicine, Gayatri Vidya Parishad Institute of Healthcare & Medical Technology, Visakhapatnam 530048, India
| |
Collapse
|
4
|
Mai Y, Liao C, Wang S, Zhou X, Meng L, Chen C, Qin Y, Deng G. High glucose-induced NCAPD2 upregulation promotes malignant phenotypes and regulates EMT via the Wnt/β-catenin signaling pathway in HCC. Am J Cancer Res 2024; 14:1685-1711. [PMID: 38726276 PMCID: PMC11076239 DOI: 10.62347/hynz9211] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/18/2023] [Accepted: 03/29/2024] [Indexed: 05/12/2024] Open
Abstract
Diabetes mellitus (DM) is recognized as a risk factor for hepatocellular carcinoma (HCC). High glucose levels have been implicated in inducing epithelial-mesenchymal transition (EMT), contributing to the progression of various cancers. However, the molecular crosstalk remains unclear. This study aimed to elucidate the molecular mechanisms linking DM to HCC. Initially, the expression of NCAPD2 in HCC cells and patients was measured. A series of functional in vitro assays to examine the effects of NCAPD2 on the malignant behaviors and EMT of HCC under high glucose conditions were then conducted. Furthermore, the impacts of NCAPD2 knockdown on HCC proliferation and the β-catenin pathway were investigated in vivo. In addition, bioinformatics methods were performed to analyze the mechanisms and pathways involving NCAPD2, as well as its association with immune infiltration and drug sensitivity. The findings indicated that NCAPD2 was overexpressed in HCC, particularly in patients with DM, and its aberrant upregulation was linked to poor prognosis. In vitro experiments demonstrated that high glucose upregulated NCAPD2 expression, enhancing proliferation, invasion, and EMT, while knockdown of NCAPD2 reversed these effects. In vivo studies suggested that NCAPD2 knockdown might suppress HCC growth via the β-catenin pathway. Functional enrichment analysis revealed that NCAPD2 was involved in cell cycle regulation and primarily interacted with NCAPG, SMC4, and NCAPH. Additionally, NCAPD2 was positively correlated with EMT and the Wnt/β-catenin pathway, whereas knockdown of NCAPD2 inhibited the Wnt/β-catenin pathway. Moreover, NCAPD2 expression was significantly associated with immune cell infiltration, immune checkpoints, and drugs sensitivity. In conclusion, our study identified NCAPD2 as a novel oncogene in HCC and as a potential therapeutic target for HCC patients with DM.
Collapse
Affiliation(s)
- Yuhua Mai
- Department of Endocrinology, The First Affiliated Hospital of Guangxi Medical UniversityNanning 530021, Guangxi, China
| | - Chuanjie Liao
- Department of Oncology, The First Affiliated Hospital of Guangxi Medical UniversityNanning 530021, Guangxi, China
- Key Laboratory of Early Prevention and Treatment for Regional High Frequency Tumor (Guangxi Medical University), Ministry of EducationNanning 530021, Guangxi, China
| | - Shengyu Wang
- Department of Gastrointestinal Surgery, The First Affiliated Hospital of Guangxi Medical UniversityNanning 530021, Guangxi, China
| | - Xin Zhou
- Department of Hepatobiliary Surgery, The First Affiliated Hospital of Guangxi Medical UniversityNanning 530021, Guangxi, China
| | - Liheng Meng
- Department of Endocrinology, The First Affiliated Hospital of Guangxi Medical UniversityNanning 530021, Guangxi, China
| | - Cuihong Chen
- Department of Endocrinology, The First Affiliated Hospital of Guangxi Medical UniversityNanning 530021, Guangxi, China
| | - Yingfen Qin
- Department of Endocrinology, The First Affiliated Hospital of Guangxi Medical UniversityNanning 530021, Guangxi, China
| | - Ganlu Deng
- Department of Oncology, The First Affiliated Hospital of Guangxi Medical UniversityNanning 530021, Guangxi, China
- Key Laboratory of Early Prevention and Treatment for Regional High Frequency Tumor (Guangxi Medical University), Ministry of EducationNanning 530021, Guangxi, China
| |
Collapse
|
5
|
Gonvers S, Martins-Filho SN, Hirayama A, Calderaro J, Phillips R, Uldry E, Demartines N, Melloul E, Park YN, Paradis V, Thung SN, Alves V, Sempoux C, Labgaa I. Macroscopic Characterization of Hepatocellular Carcinoma: An Underexploited Source of Prognostic Factors. J Hepatocell Carcinoma 2024; 11:707-719. [PMID: 38605975 PMCID: PMC11007400 DOI: 10.2147/jhc.s447848] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/02/2023] [Accepted: 02/29/2024] [Indexed: 04/13/2024] Open
Abstract
The macroscopic appearance of a tumor such as hepatocellular carcinoma (HCC) may be defined as its phenotype which is de facto dictated by its genotype. Therefore, macroscopic characteristics of HCC are unlikely random but rather reflect genomic traits of cancer, presumably acting as a valuable source of information that can be retrieved and exploited to infer prognosis. This review aims to provide a comprehensive overview of the available data on the prognostic value of macroscopic characterization in HCC. A total of 57 studies meeting eligible criteria were identified, including patients undergoing liver resection (LR; 47 studies, 83%) or liver transplant (LT; 9 studies, 16%). The following macroscopic variables were investigated: tumor size (n = 42 studies), number of nodules (n = 28), vascular invasion (n = 24), bile duct invasion (n = 6), growth pattern (n = 15), resection margin (n = 11), tumor location (n = 6), capsule (n = 2) and satellite (n = 1). Although the selected studies provided insightful data with notable prognostic performances, a lack of standardization and substantial gaps were noted in the report and the analysis of gross findings. This topic remains incompletely covered. While the available studies underscored the value of macroscopic variables in HCC prognostication, important lacks were also observed. Macroscopic characterization of HCC is likely an underexploited source of prognostic factors that must be actively explored by future multidisciplinary research.
Collapse
Affiliation(s)
- Stéphanie Gonvers
- Department of Visceral Surgery, Lausanne University Hospital (CHUV), Lausanne, Switzerland
- Faculty of Biology & Medicine (FBM), University of Lausanne (UNIL), Lausanne, Switzerland
| | | | - André Hirayama
- Department of Pathology, Faculdade de Medicina FMUSP, Universidade de Sao Paulo, Sao Paulo, Brazil
| | - Julien Calderaro
- Department of Pathology, APHP, Henri Mondor University Hospital, Creteil, Val-de-Marne, France
| | - Rebecca Phillips
- Department of Laboratory Medicine and Pathology, University of Alberta, Edmonton, Alberta, Canada
| | - Emilie Uldry
- Department of Visceral Surgery, Lausanne University Hospital (CHUV), Lausanne, Switzerland
- Faculty of Biology & Medicine (FBM), University of Lausanne (UNIL), Lausanne, Switzerland
| | - Nicolas Demartines
- Department of Visceral Surgery, Lausanne University Hospital (CHUV), Lausanne, Switzerland
- Faculty of Biology & Medicine (FBM), University of Lausanne (UNIL), Lausanne, Switzerland
| | - Emmanuel Melloul
- Department of Visceral Surgery, Lausanne University Hospital (CHUV), Lausanne, Switzerland
- Faculty of Biology & Medicine (FBM), University of Lausanne (UNIL), Lausanne, Switzerland
| | - Young Nyun Park
- Department of Pathology, Severance Hospital, Yonsei University College of Medicine, Seoul, Republic of Korea
| | - Valérie Paradis
- Department of Pathology, APHP, Beaujon University Hospital, Clichy, France
| | - Swan N Thung
- Department of Pathology, Molecular and Cell Based Medicine, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Venancio Alves
- Department of Pathology, Faculdade de Medicina FMUSP, Universidade de Sao Paulo, Sao Paulo, Brazil
| | - Christine Sempoux
- Faculty of Biology & Medicine (FBM), University of Lausanne (UNIL), Lausanne, Switzerland
- Department of Pathology, Lausanne University Hospital (CHUV), Lausanne, Switzerland
| | - Ismail Labgaa
- Department of Visceral Surgery, Lausanne University Hospital (CHUV), Lausanne, Switzerland
- Faculty of Biology & Medicine (FBM), University of Lausanne (UNIL), Lausanne, Switzerland
| |
Collapse
|
6
|
Zhang P, Gao C, Huang Y, Chen X, Pan Z, Wang L, Dong D, Li S, Qi X. Artificial intelligence in liver imaging: methods and applications. Hepatol Int 2024; 18:422-434. [PMID: 38376649 DOI: 10.1007/s12072-023-10630-w] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 06/15/2023] [Accepted: 12/18/2023] [Indexed: 02/21/2024]
Abstract
Liver disease is regarded as one of the major health threats to humans. Radiographic assessments hold promise in terms of addressing the current demands for precisely diagnosing and treating liver diseases, and artificial intelligence (AI), which excels at automatically making quantitative assessments of complex medical image characteristics, has made great strides regarding the qualitative interpretation of medical imaging by clinicians. Here, we review the current state of medical-imaging-based AI methodologies and their applications concerning the management of liver diseases. We summarize the representative AI methodologies in liver imaging with focusing on deep learning, and illustrate their promising clinical applications across the spectrum of precise liver disease detection, diagnosis and treatment. We also address the current challenges and future perspectives of AI in liver imaging, with an emphasis on feature interpretability, multimodal data integration and multicenter study. Taken together, it is revealed that AI methodologies, together with the large volume of available medical image data, might impact the future of liver disease care.
Collapse
Affiliation(s)
- Peng Zhang
- Institute for TCM-X, MOE Key Laboratory of Bioinformatics, Bioinformatics Division, BNRIST, Department of Automation, Tsinghua University, Beijing, China
| | - Chaofei Gao
- Institute for TCM-X, MOE Key Laboratory of Bioinformatics, Bioinformatics Division, BNRIST, Department of Automation, Tsinghua University, Beijing, China
| | - Yifei Huang
- Department of Gastroenterology, The Third Affiliated Hospital of Sun Yat-sen University, Guangzhou, China
| | - Xiangyi Chen
- Institute for TCM-X, MOE Key Laboratory of Bioinformatics, Bioinformatics Division, BNRIST, Department of Automation, Tsinghua University, Beijing, China
| | - Zhuoshi Pan
- Institute for TCM-X, MOE Key Laboratory of Bioinformatics, Bioinformatics Division, BNRIST, Department of Automation, Tsinghua University, Beijing, China
| | - Lan Wang
- Institute for TCM-X, MOE Key Laboratory of Bioinformatics, Bioinformatics Division, BNRIST, Department of Automation, Tsinghua University, Beijing, China
| | - Di Dong
- CAS Key Laboratory of Molecular Imaging, Beijing Key Laboratory of Molecular Imaging, Institute of Automation, Chinese Academy of Sciences, Beijing, 100190, China
| | - Shao Li
- Institute for TCM-X, MOE Key Laboratory of Bioinformatics, Bioinformatics Division, BNRIST, Department of Automation, Tsinghua University, Beijing, China.
| | - Xiaolong Qi
- Center of Portal Hypertension, Department of Radiology, Zhongda Hospital, Medical School, Nurturing Center of Jiangsu Province for State Laboratory of AI Imaging & Interventional Radiology, Southeast University, Nanjing, China.
| |
Collapse
|
7
|
Shen Y, Huang J, Jia L, Zhang C, Xu J. Bioinformatics and machine learning driven key genes screening for hepatocellular carcinoma. Biochem Biophys Rep 2024; 37:101587. [PMID: 38107663 PMCID: PMC10724547 DOI: 10.1016/j.bbrep.2023.101587] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/23/2023] [Revised: 11/01/2023] [Accepted: 11/17/2023] [Indexed: 12/19/2023] Open
Abstract
Liver cancer, a global menace, ranked as the sixth most prevalent and third deadliest cancer in 2020. The challenge of early diagnosis and treatment, especially for hepatocellular carcinoma (HCC), persists due to late-stage detections. Understanding HCC's complex pathogenesis is vital for advancing diagnostics and therapies. This study combines bioinformatics and machine learning, examining HCC comprehensively. Three datasets underwent meticulous scrutiny, employing various analytical tools such as Gene Ontology (GO) function and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analysis, protein interaction assessment, and survival analysis. These rigorous investigations uncovered twelve pivotal genes intricately linked with HCC's pathophysiological intricacies. Among them, CYP2C8, CYP2C9, EPHX2, and ESR1 were significantly positively correlated with overall patient survival, while AKR1B10 and NQO1 displayed a negative correlation. Moreover, the Adaboost prediction model yielded an 86.8 % accuracy, showcasing machine learning's potential in deciphering complex dataset patterns for clinically relevant predictions. These findings promise to contribute valuable insights into the elusive mechanisms driving liver cancer (HCC). They hold the potential to guide the development of more precise diagnostic methods and treatment strategies in the future. In the fight against this global health challenge, unraveling HCC's intricacies is of paramount importance.
Collapse
Affiliation(s)
- Ye Shen
- Department of Radiology, Wujin Hospital Affiliated with Jiangsu University, Changzhou, 213002, China
| | - Juanjie Huang
- Department of General Surgery, Dongguan Qingxi Hospital, Dongguan, 523660, China
| | - Lei Jia
- International Health Medicine Innovation Center, Shenzhen University, ShenZhen, 518060, China
| | - Chi Zhang
- Huaxia Eye Hospital of Foshan, Huaxia Eye Hospital Group, Foshan, Guangdong, 528000, China
| | - Jianxing Xu
- Department of Radiology, Wujin Hospital Affiliated with Jiangsu University, Changzhou, 213002, China
- Department of Radiology, The Wujin Clinical College of Xuzhou Medical University, Changzhou, 213002, China
| |
Collapse
|
8
|
Xia T, Zhao B, Li B, Lei Y, Song Y, Wang Y, Tang T, Ju S. MRI-Based Radiomics and Deep Learning in Biological Characteristics and Prognosis of Hepatocellular Carcinoma: Opportunities and Challenges. J Magn Reson Imaging 2024; 59:767-783. [PMID: 37647155 DOI: 10.1002/jmri.28982] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/20/2023] [Revised: 08/15/2023] [Accepted: 08/16/2023] [Indexed: 09/01/2023] Open
Abstract
Hepatocellular carcinoma (HCC) is the fifth most common malignancy and the third leading cause of cancer-related death worldwide. HCC exhibits strong inter-tumor heterogeneity, with different biological characteristics closely associated with prognosis. In addition, patients with HCC often distribute at different stages and require diverse treatment options at each stage. Due to the variability in tumor sensitivity to different therapies, determining the optimal treatment approach can be challenging for clinicians prior to treatment. Artificial intelligence (AI) technology, including radiomics and deep learning approaches, has emerged as a unique opportunity to improve the spectrum of HCC clinical care by predicting biological characteristics and prognosis in the medical imaging field. The radiomics approach utilizes handcrafted features derived from specific mathematical formulas to construct various machine-learning models for medical applications. In terms of the deep learning approach, convolutional neural network models are developed to achieve high classification performance based on automatic feature extraction from images. Magnetic resonance imaging offers the advantage of superior tissue resolution and functional information. This comprehensive evaluation plays a vital role in the accurate assessment and effective treatment planning for HCC patients. Recent studies have applied radiomics and deep learning approaches to develop AI-enabled models to improve accuracy in predicting biological characteristics and prognosis, such as microvascular invasion and tumor recurrence. Although AI-enabled models have demonstrated promising potential in HCC with biological characteristics and prognosis prediction with high performance, one of the biggest challenges, interpretability, has hindered their implementation in clinical practice. In the future, continued research is needed to improve the interpretability of AI-enabled models, including aspects such as domain knowledge, novel algorithms, and multi-dimension data sources. Overcoming these challenges would allow AI-enabled models to significantly impact the care provided to HCC patients, ultimately leading to their deployment for clinical use. LEVEL OF EVIDENCE: 5 TECHNICAL EFFICACY: Stage 2.
Collapse
Affiliation(s)
- Tianyi Xia
- Jiangsu Key Laboratory of Molecular and Functional Imaging, Department of Radiology, Zhongda Hospital, School of Medicine, Southeast University, Nanjing, China
| | - Ben Zhao
- Jiangsu Key Laboratory of Molecular and Functional Imaging, Department of Radiology, Zhongda Hospital, School of Medicine, Southeast University, Nanjing, China
| | - Binrong Li
- Jiangsu Key Laboratory of Molecular and Functional Imaging, Department of Radiology, Zhongda Hospital, School of Medicine, Southeast University, Nanjing, China
| | - Ying Lei
- Jiangsu Key Laboratory of Molecular and Functional Imaging, Department of Radiology, Zhongda Hospital, School of Medicine, Southeast University, Nanjing, China
| | - Yang Song
- MR Scientific Marketing, Siemens Healthineers Ltd., Shanghai, China
| | - Yuancheng Wang
- Jiangsu Key Laboratory of Molecular and Functional Imaging, Department of Radiology, Zhongda Hospital, School of Medicine, Southeast University, Nanjing, China
| | - Tianyu Tang
- Jiangsu Key Laboratory of Molecular and Functional Imaging, Department of Radiology, Zhongda Hospital, School of Medicine, Southeast University, Nanjing, China
| | - Shenghong Ju
- Jiangsu Key Laboratory of Molecular and Functional Imaging, Department of Radiology, Zhongda Hospital, School of Medicine, Southeast University, Nanjing, China
| |
Collapse
|
9
|
Oh JH, Sinn DH. Multidisciplinary approach for hepatocellular carcinoma patients: current evidence and future perspectives. JOURNAL OF LIVER CANCER 2024; 24:47-56. [PMID: 38527905 PMCID: PMC10990674 DOI: 10.17998/jlc.2024.02.27] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 02/10/2024] [Revised: 02/22/2024] [Accepted: 02/26/2024] [Indexed: 03/27/2024]
Abstract
Management of hepatocellular carcinoma (HCC) is challenging due to the complex relationship between underlying liver disease, tumor burden, and liver function. HCC is also notorious for its high recurrence rate even after curative treatment for early-stage tumor. Liver transplantation can substantially alter patient prognosis, but donor availability varies by each patient which further complicates treatment decision. Recent advancements in HCC treatments have introduced numerous potentially efficacious treatment modalities. However, high level evidence comparing the risks and benefits of these options is limited. In this complex situation, multidisciplinary approach or multidisciplinary team care has been suggested as a valuable strategy to help cope with escalating complexity in HCC management. Multidisciplinary approach involves collaboration among medical and health care professionals from various academic disciplines to provide comprehensive care. Although evidence suggests that multidisciplinary care can enhance outcomes of HCC patients, robust data from randomized controlled trials are currently lacking. Moreover, the implementation of a multidisciplinary approach necessitates increased medical resources compared to conventional cancer care. This review summarizes the current evidence on the role of multidisciplinary approach in HCC management and explores potential future directions.
Collapse
Affiliation(s)
- Joo Hyun Oh
- Department of Medicine, Nowon Eulji Medical Center, Eulji University, Eulji University School of Medicine, Seoul, Korea
| | - Dong Hyun Sinn
- Department of Medicine, Samsung Medical Center, Sungkyunkwan University School of Medicine, Seoul, Korea
| |
Collapse
|
10
|
Wang F, Yan CY, Qin Y, Wang ZM, Liu D, He Y, Yang M, Wen L, Zhang D. Multiple Machine-Learning Fusion Model Based on Gd-EOB-DTPA-Enhanced MRI and Aminotransferase-to-Platelet Ratio and Gamma-Glutamyl Transferase-to-Platelet Ratio to Predict Microvascular Invasion in Solitary Hepatocellular Carcinoma: A Multicenter Study. J Hepatocell Carcinoma 2024; 11:427-442. [PMID: 38440051 PMCID: PMC10911084 DOI: 10.2147/jhc.s449737] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/05/2023] [Accepted: 02/20/2024] [Indexed: 03/06/2024] Open
Abstract
Background Currently, it is still confused whether preoperative aminotransferase-to-platelet ratio (APRI) and gamma-glutamyl transferase-to-platelet ratio (GPR) can predict microvascular invasion (MVI) in solitary hepatocellular carcinoma (HCC). We aimed to develop and validate a machine-learning integration model for predicting MVI using APRI, GPR and gadoxetic acid disodium (Gd-EOB-DTPA) enhanced MRI. Methods A total of 314 patients from XinQiao Hospital of Army Medical University were divided chronologically into training set (n = 220) and internal validation set (n = 94), and recurrence-free survival was determined to follow up after surgery. Seventy-three patients from Chongqing University Three Gorges Hospital and Luzhou People's Hospital served as external validation set. Overall, 387 patients with solitary HCC were analyzed as whole dataset set. Least absolute shrinkage and selection operator, tenfold cross-validation and multivariate logistic regression were used to gradually filter features. Six machine-learning models and an ensemble of the all models (ENS) were built. The area under the receiver operating characteristic curve (AUC) and decision curve analysis were used to evaluate model's performance. Results APRI, GPR, HBPratio3 ([liver SI‒tumor SI]/liver SI), PLT, peritumoral enhancement, non-smooth margin and peritumoral hypointensity were independent risk factors for MVI. Six machine-learning models showed good performance for predicting MVI in training set (AUCs range, 0.793-0.875), internal validation set (0.715-0.832), external validation set (0.636-0.746) and whole dataset set (0.756-0.850). The ENS achieved the highest AUCs (0.879 vs 0.858 vs 0.839 vs 0.851) in four cohorts with excellent calibration and more net benefit. Subgroup analysis indicated that ENS obtained excellent AUCs (0.900 vs 0.809 vs 0.865 vs 0.908) in HCC >5cm, ≤5cm, ≤3cm and ≤2cm cohorts. Kaplan‒Meier survival curves indicated that ENS achieved excellent stratification for MVI status. Conclusion The APRI and GPR may be new potential biomarkers for predicting MVI of HCC. The ENS achieved optimal performance for predicting MVI in different sizes HCC and may aid in the individualized selection of surgical procedures.
Collapse
Affiliation(s)
- Fei Wang
- Department of Radiology, XinQiao Hospital of Army Medical University, Chongqing, 400037, People’s Republic of China
- Department of Medical Imaging, Luzhou People’s Hospital, Luzhou, 646000, People’s Republic of China
| | - Chun Yue Yan
- Department of Emergency Medicine, Luzhou People’s Hospital, Luzhou, 646000, People’s Republic of China
| | - Yuan Qin
- Department of Radiology, Chongqing University Three Gorges Hospital, Chongqing, 404031, People’s Republic of China
| | - Zheng Ming Wang
- Department of Radiology, XinQiao Hospital of Army Medical University, Chongqing, 400037, People’s Republic of China
| | - Dan Liu
- Department of Radiology, XinQiao Hospital of Army Medical University, Chongqing, 400037, People’s Republic of China
| | - Ying He
- Department of Radiology, XinQiao Hospital of Army Medical University, Chongqing, 400037, People’s Republic of China
| | - Ming Yang
- Department of Medical Imaging, Luzhou People’s Hospital, Luzhou, 646000, People’s Republic of China
| | - Li Wen
- Department of Radiology, XinQiao Hospital of Army Medical University, Chongqing, 400037, People’s Republic of China
| | - Dong Zhang
- Department of Radiology, XinQiao Hospital of Army Medical University, Chongqing, 400037, People’s Republic of China
| |
Collapse
|
11
|
Ramírez-Mejía MM, Méndez-Sánchez N. From prediction to prevention: Machine learning revolutionizes hepatocellular carcinoma recurrence monitoring. World J Gastroenterol 2024; 30:631-635. [PMID: 38515945 PMCID: PMC10950631 DOI: 10.3748/wjg.v30.i7.631] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 11/13/2023] [Revised: 12/12/2023] [Accepted: 01/22/2024] [Indexed: 02/21/2024] Open
Abstract
In this editorial, we comment on the article by Zhang et al entitled Development of a machine learning-based model for predicting the risk of early postoperative recurrence of hepatocellular carcinoma. Hepatocellular carcinoma (HCC), which is characterized by high incidence and mortality rates, remains a major global health challenge primarily due to the critical issue of postoperative recurrence. Early recurrence, defined as recurrence that occurs within 2 years posttreatment, is linked to the hidden spread of the primary tumor and significantly impacts patient survival. Traditional predictive factors, including both patient- and treatment-related factors, have limited predictive ability with respect to HCC recurrence. The integration of machine learning algorithms is fueled by the exponential growth of computational power and has revolutionized HCC research. The study by Zhang et al demonstrated the use of a groundbreaking preoperative prediction model for early postoperative HCC recurrence. Chall-enges persist, including sample size constraints, issues with handling data, and the need for further validation and interpretability. This study emphasizes the need for collaborative efforts, multicenter studies and comparative analyses to validate and refine the model. Overcoming these challenges and exploring innovative approaches, such as multi-omics integration, will enhance personalized oncology care. This study marks a significant stride toward precise, effi-cient, and personalized oncology practices, thus offering hope for improved patient outcomes in the field of HCC treatment.
Collapse
Affiliation(s)
- Mariana Michelle Ramírez-Mejía
- Plan of Combined Studies in Medicine, Faculty of Medicine, National Autonomous University of Mexico, Distrito Federal 04510, Mexico
- Liver Research Unit, Medica Sur Clinic & Foundation, Distrito Federal 14050, Mexico
| | - Nahum Méndez-Sánchez
- Liver Research Unit, Medica Sur Clinic & Foundation, Distrito Federal 14050, Mexico
- Faculty of Medicine, National Autonomous University of Mexico, Distrito Federal 04510, Mexico
| |
Collapse
|
12
|
Floreani A, Gabbia D, De Martin S. Current Perspectives on the Molecular and Clinical Relationships between Primary Biliary Cholangitis and Hepatocellular Carcinoma. Int J Mol Sci 2024; 25:2194. [PMID: 38396870 PMCID: PMC10888596 DOI: 10.3390/ijms25042194] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/18/2024] [Revised: 02/07/2024] [Accepted: 02/09/2024] [Indexed: 02/25/2024] Open
Abstract
Primary biliary cholangitis (PBC) is an autoimmune liver disease characterised by the immune-mediated destruction of small and medium intrahepatic bile ducts, with variable outcomes and progression. This review summarises the state of the art regarding the risk of neoplastic progression in PBC patients, with a particular focus on the molecular alterations present in PBC and in hepatocellular carcinoma (HCC), which is the most frequent liver cancer in these patients. Major risk factors are male gender, viral infections, e.g., HBV and HCV, non-response to UDCA, and high alcohol intake, as well as some metabolic-associated factors. Overall, HCC development is significantly more frequent in patients with advanced histological stages, being related to liver cirrhosis. It seems to be of fundamental importance to unravel eventual dysfunctional molecular pathways in PBC patients that may be used as biomarkers for HCC development. In the near future, this will possibly take advantage of artificial intelligence-designed algorithms.
Collapse
Affiliation(s)
- Annarosa Floreani
- University of Padova, 35122 Padova, Italy;
- Scientific Consultant IRCCS Negrar, 37024 Verona, Italy
| | - Daniela Gabbia
- Department of Pharmaceutical and Pharmacological Sciences, University of Padova, 35131 Padova, Italy;
| | - Sara De Martin
- Department of Pharmaceutical and Pharmacological Sciences, University of Padova, 35131 Padova, Italy;
| |
Collapse
|
13
|
S Alshuhri M, Al-Musawi SG, Al-Alwany AA, Uinarni H, Rasulova I, Rodrigues P, Alkhafaji AT, Alshanberi AM, Alawadi AH, Abbas AH. Artificial intelligence in cancer diagnosis: Opportunities and challenges. Pathol Res Pract 2024; 253:154996. [PMID: 38118214 DOI: 10.1016/j.prp.2023.154996] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 10/20/2023] [Revised: 11/20/2023] [Accepted: 11/27/2023] [Indexed: 12/22/2023]
Abstract
Since cancer is one of the world's top causes of death, early diagnosis is critical to improving patient outcomes. Artificial intelligence (AI) has become a viable technique for cancer diagnosis by using machine learning algorithms to examine large volumes of data for accurate and efficient diagnosis. AI has the potential to alter the way cancer is detected fundamentally. Still, it has several disadvantages, such as requiring a large amount of data, technological limitations, and ethical concerns. This overview looks at the possibilities and restrictions of AI in cancer detection, as well as current applications and possible future developments. We can better understand how to use AI to improve patient outcomes and reduce cancer mortality rates by looking at its potential for cancer detection.
Collapse
Affiliation(s)
- Mohammed S Alshuhri
- Radiology and Medical Imaging Department, College of Applied Medical Sciences, Prince Sattam bin Abdulaziz University, Kharj, Saudi Arabia
| | | | | | - Herlina Uinarni
- Department of Anatomy, School of Medicine and Health Sciences Atma Jaya Catholic University of Indonesia, Indonesia; Radiology department of Pantai Indah Kapuk Hospital Jakarta, Jakarta, Indonesia.
| | - Irodakhon Rasulova
- School of Humanities, Natural & Social Sciences, New Uzbekistan University, 54 Mustaqillik Ave., Tashkent 100007, Uzbekistan; Department of Public Health, Samarkand State Medical University, Amir Temur Street 18, Samarkand, Uzbekistan
| | - Paul Rodrigues
- Department of Computer Engineering, College of Computer Science, King Khalid University, Al-Faraa, Abha, Asir, Kingdom of Saudi Arabia
| | | | - Asim Muhammed Alshanberi
- Department of Community Medicine & Pilgrim Healthcare, Umm Alqura University, Makkah 24382, Saudi Arabia; General Medicine Practice Program, Batterjee Medical College, Jeddah 21442, Saudi Arabia
| | - Ahmed Hussien Alawadi
- College of Technical Engineering, the Islamic University, Najaf, Iraq; College of Technical Engineering, the Islamic University of Al Diwaniyah, Iraq; College of Technical Engineering, the Islamic University of Babylon, Iraq
| | - Ali Hashim Abbas
- College of Technical Engineering, Imam Ja'afar Al-Sadiq University, Al-Muthanna 66002, Iraq
| |
Collapse
|
14
|
He J, Wu F, Li J, Deng Q, Chen J, Li P, Jiang X, Yang K, Xu S, Jiang Z, Li X, Jiang Z. Tumor suppressor CLCA1 inhibits angiogenesis via TGFB1/SMAD/VEGF cascade and sensitizes hepatocellular carcinoma cells to Sorafenib. Dig Liver Dis 2024; 56:176-186. [PMID: 37230858 DOI: 10.1016/j.dld.2023.05.010] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 02/01/2023] [Revised: 04/18/2023] [Accepted: 05/05/2023] [Indexed: 05/27/2023]
Abstract
BACKGROUND Hepatocellular carcinoma (HCC) is a highly vascularized tumor with a poor prognosis. Novel vascular-related therapeutic targets and prognostic markers remain urgently needed. AIMS To investigate the role and mechanism of CLCA1 in hepatocellular carcinoma. METHODS Immunofluorescence, Co-immunoprecipitation and rescue experiment were used to determine the specific mechanisms of CLCA1. Chemosensitivity assay was used to measure the impact of CLCA1 on Sorafenib. RESULTS CLCA1 was dramatically downregulated in hepatocellular carcinoma cell lines and tissues. Ectopic expression of CLCA1 induced cell apoptosis and G0/G1 phase arrest while suppressed cell growth, inhibited migration and invasion, reversal of epithelial mesenchymal transition in vitro and reduced xenograft tumor growth in vivo. Mechanistically, CLCA1 could co-localize and interact with TGFB1, thereby suppressing HCC angiogenesis through the TGFB1/SMAD/VEGF signaling cascade in vitro and in vivo. Moreover, CLCA1 also enhanced the sensitivity of HCC cells to the first-line targeted therapy, Sorafenib. CONCLUSION CLCA1 sensitizes HCC cells to Sorafenib and suppresses hepatocellular carcinoma angiogenesis through downregulating TGFB1 signaling cascade. This newly identified CLCA1 signaling pathway may help guide the anti-angiogenesis therapies for hepatocellular carcinoma. We also support the possibility of CLCA1 being a prognostic biomarker for hepatocellular carcinoma.
Collapse
Affiliation(s)
- Jin He
- Department of Gastroenterology, The First Affiliated Hospital of Chongqing Medical University, Chongqing 400016, China
| | - Fan Wu
- Department of Gastroenterology, The First Affiliated Hospital of Chongqing Medical University, Chongqing 400016, China
| | - Junfeng Li
- Department of Gastroenterology, The First Affiliated Hospital of Chongqing Medical University, Chongqing 400016, China
| | - Qianxi Deng
- Department of Gastroenterology, The First Affiliated Hospital of Chongqing Medical University, Chongqing 400016, China
| | - Jun Chen
- Department of Gastroenterology, The First Affiliated Hospital of Chongqing Medical University, Chongqing 400016, China
| | - Pengtao Li
- Department of Neurosurgery, Peking Union Medical College Hospital, Beijing 100044, China
| | - Xianyao Jiang
- Department of Otorhinolaryngology Head and Neck Surgery, Hainan General Hospital, Haikou 570100, China
| | - Kun Yang
- Department of Gastroenterology, The First Affiliated Hospital of Chongqing Medical University, Chongqing 400016, China
| | - Shuman Xu
- Department of Gastroenterology, The First Affiliated Hospital of Chongqing Medical University, Chongqing 400016, China
| | - Zhongxiang Jiang
- Department of Gastroenterology, The First Affiliated Hospital of Chongqing Medical University, Chongqing 400016, China
| | - Xiaoqing Li
- Department of Gastroenterology, The First Affiliated Hospital of Chongqing Medical University, Chongqing 400016, China
| | - Zheng Jiang
- Department of Gastroenterology, The First Affiliated Hospital of Chongqing Medical University, Chongqing 400016, China.
| |
Collapse
|
15
|
Wiest IC, Gilbert S, Kather JN. From research to reality: The role of artificial intelligence applications in HCC care. Clin Liver Dis (Hoboken) 2024; 23:e0136. [PMID: 38567094 PMCID: PMC10986906 DOI: 10.1097/cld.0000000000000136] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 11/24/2023] [Accepted: 01/12/2024] [Indexed: 04/04/2024] Open
Affiliation(s)
- Isabella C. Wiest
- Else Kroener Fresenius Center for Digital Health, Medical Faculty Carl Gustav Carus, TUD Dresden University of Technology, Dresden, Germany
- Department of Medicine II, Medical Faculty Mannheim, Heidelberg University, Mannheim, Germany
| | - Stephen Gilbert
- Else Kroener Fresenius Center for Digital Health, Medical Faculty Carl Gustav Carus, TUD Dresden University of Technology, Dresden, Germany
| | - Jakob N. Kather
- Else Kroener Fresenius Center for Digital Health, Medical Faculty Carl Gustav Carus, TUD Dresden University of Technology, Dresden, Germany
- Medical Oncology, National Center for Tumor Diseases (NCT), University Hospital Heidelberg, Heidelberg, Germany
- Department of Medicine I, University Hospital Dresden, Dresden, Germany
| |
Collapse
|
16
|
Lu MY, Huang CF, Hung CH, Tai C, Mo LR, Kuo HT, Tseng KC, Lo CC, Bair MJ, Wang SJ, Huang JF, Yeh ML, Chen CT, Tsai MC, Huang CW, Lee PL, Yang TH, Huang YH, Chong LW, Chen CL, Yang CC, Yang S, Cheng PN, Hsieh TY, Hu JT, Wu WC, Cheng CY, Chen GY, Zhou GX, Tsai WL, Kao CN, Lin CL, Wang CC, Lin TY, Lin C, Su WW, Lee TH, Chang TS, Liu CJ, Dai CY, Kao JH, Lin HC, Chuang WL, Peng CY, Tsai CW, Chen CY, Yu ML. Artificial intelligence predicts direct-acting antivirals failure among hepatitis C virus patients: A nationwide hepatitis C virus registry program. Clin Mol Hepatol 2024; 30:64-79. [PMID: 38195113 PMCID: PMC10776298 DOI: 10.3350/cmh.2023.0287] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 08/02/2023] [Revised: 11/02/2023] [Accepted: 11/20/2023] [Indexed: 01/11/2024] Open
Abstract
BACKGROUND/AIMS Despite the high efficacy of direct-acting antivirals (DAAs), approximately 1-3% of hepatitis C virus (HCV) patients fail to achieve a sustained virological response. We conducted a nationwide study to investigate risk factors associated with DAA treatment failure. Machine-learning algorithms have been applied to discriminate subjects who may fail to respond to DAA therapy. METHODS We analyzed the Taiwan HCV Registry Program database to explore predictors of DAA failure in HCV patients. Fifty-five host and virological features were assessed using multivariate logistic regression, decision tree, random forest, eXtreme Gradient Boosting (XGBoost), and artificial neural network. The primary outcome was undetectable HCV RNA at 12 weeks after the end of treatment. RESULTS The training (n=23,955) and validation (n=10,346) datasets had similar baseline demographics, with an overall DAA failure rate of 1.6% (n=538). Multivariate logistic regression analysis revealed that liver cirrhosis, hepatocellular carcinoma, poor DAA adherence, and higher hemoglobin A1c were significantly associated with virological failure. XGBoost outperformed the other algorithms and logistic regression models, with an area under the receiver operating characteristic curve of 1.000 in the training dataset and 0.803 in the validation dataset. The top five predictors of treatment failure were HCV RNA, body mass index, α-fetoprotein, platelets, and FIB-4 index. The accuracy, sensitivity, specificity, positive predictive value, and negative predictive value of the XGBoost model (cutoff value=0.5) were 99.5%, 69.7%, 99.9%, 97.4%, and 99.5%, respectively, for the entire dataset. CONCLUSION Machine learning algorithms effectively provide risk stratification for DAA failure and additional information on the factors associated with DAA failure.
Collapse
Affiliation(s)
- Ming-Ying Lu
- School of Medicine and Doctoral Program of Clinical and Experimental Medicine, College of Medicine and Center of Excellence for Metabolic Associated Fatty Liver Disease, National Sun Yat-sen University, Kaohsiung, Taiwan
- Hepatobiliary Division, Department of Internal Medicine and Hepatitis Center, Kaohsiung Medical University Hospital, Kaohsiung Medical University, Kaohsiung, Taiwan
- Hepatitis Research Center, College of Medicine and Center for Liquid Biopsy and Cohort Research, Kaohsiung Medical University, Kaohsiung, Taiwan
| | - Chung-Feng Huang
- Hepatobiliary Division, Department of Internal Medicine and Hepatitis Center, Kaohsiung Medical University Hospital, Kaohsiung Medical University, Kaohsiung, Taiwan
- Hepatitis Research Center, College of Medicine and Center for Liquid Biopsy and Cohort Research, Kaohsiung Medical University, Kaohsiung, Taiwan
- Ph.D. Program in Translational Medicine, College of Medicine, Kaohsiung Medical University, Kaohsiung, and Academia Sinica, Taipei, Taiwan
| | - Chao-Hung Hung
- Division of Hepatogastroenterology, Department of Internal Medicine, Kaohsiung Chang Gung Memorial Hospital, Kaohsiung, Taiwan
| | - Chi‐Ming Tai
- Division of Gastroenterology and Hepatology, Department of Internal Medicine, E-Da Hospital, I-Shou University, Kaohsiung, Taiwan
- School of Medicine for International Students, College of Medicine, I-Shou University, Kaohsiung, Taiwan
| | - Lein-Ray Mo
- Division of Gastroenterology, Tainan Municipal Hospital (Managed By Show Chwan Medical Care Corporation), Tainan, Taiwan
| | - Hsing-Tao Kuo
- School of Medicine and Doctoral Program of Clinical and Experimental Medicine, College of Medicine and Center of Excellence for Metabolic Associated Fatty Liver Disease, National Sun Yat-sen University, Kaohsiung, Taiwan
- Division of Gastroenterology and Hepatology, Department of Internal Medicine, Chi Mei Medical Center, Yongkang District, Tainan, Taiwan
| | - Kuo-Chih Tseng
- Department of Internal Medicine, Dalin Tzu Chi Hospital, Buddhist Tzu Chi Medical Foundation, Chiayi, Taiwan
- School of Medicine, Tzuchi University, Hualien, Taiwan
| | - Ching-Chu Lo
- Division of Gastroenterology, Department of Internal Medicine, St. Martin De Porres Hospital, Chiayi, Taiwan
| | - Ming-Jong Bair
- Division of Gastroenterology, Department of Internal Medicine, Taitung Mackay Memorial Hospital, Taitung, Taiwan
- Mackay Medical College, New Taipei City, Taiwan
| | - Szu-Jen Wang
- Division of Gastroenterology, Department of Internal Medicine, Yuan’s General Hospital, Kaohsiung, Taiwan
| | - Jee-Fu Huang
- Hepatobiliary Division, Department of Internal Medicine and Hepatitis Center, Kaohsiung Medical University Hospital, Kaohsiung Medical University, Kaohsiung, Taiwan
- Hepatitis Research Center, College of Medicine and Center for Liquid Biopsy and Cohort Research, Kaohsiung Medical University, Kaohsiung, Taiwan
| | - Ming-Lun Yeh
- Hepatobiliary Division, Department of Internal Medicine and Hepatitis Center, Kaohsiung Medical University Hospital, Kaohsiung Medical University, Kaohsiung, Taiwan
- Hepatitis Research Center, College of Medicine and Center for Liquid Biopsy and Cohort Research, Kaohsiung Medical University, Kaohsiung, Taiwan
| | - Chun-Ting Chen
- Division of Gastroenterology, Department of Internal Medicine, Tri-Service General Hospital, National Defense Medical Center, Taipei, Taiwan
- Division of Gastroenterology, Department of Internal Medicine, Tri-Service General Hospital Penghu Branch, National Defense Medical Center, Taipei, Taiwan
| | - Ming-Chang Tsai
- School of Medicine, Chung Shan Medical University, Department of Internal Medicine, Chung Shan Medical University Hospital, Taichung, Taiwan
| | - Chien-Wei Huang
- Division of Gastroenterology, Kaohsiung Armed Forces General Hospital, Kaohsiung, Taiwan
| | - Pei-Lun Lee
- Division of Gastroenterology and Hepatology, Department of Internal Medicine, Chi Mei Medical Center, Liouying, Tainan, Taiwan
| | | | - Yi-Hsiang Huang
- Division of Gastroenterology and Hepatology, Department of Medicine, Taipei Veterans General Hospital, Taipei, Taiwan
- Institute of Clinical Medicine, School of Medicine, National Yang-Ming Chiao Tung University, Taipei, Taiwan
| | - Lee-Won Chong
- Division of Hepatology and Gastroenterology, Department of Internal Medicine, Shin Kong Wu Ho-Su Memorial Hospital, Taipei, Taiwan
- School of Medicine, Fu-Jen Catholic University, New Taipei City, Taiwan
| | - Chien-Lin Chen
- Department of Medicine, Hualien Tzu Chi Hospital, Buddhist Tzu Chi Medical Foundation and Tzu Chi University, Hualien, Taiwan
| | - Chi-Chieh Yang
- Department of Gastroenterology, Division of Internal Medicine, Show Chwan Memorial Hospital, Changhua, Taiwan
| | - Sheng‐Shun Yang
- Division of Gastroenterology and Hepatology, Department of Internal Medicine, Taichung Veterans General Hospital, Taichung, Taiwan
| | - Pin-Nan Cheng
- Division of Gastroenterology and Hepatology, Department of Internal Medicine, National Cheng Kung University Hospital, College of Medicine, National Cheng Kung University, Tainan, Taiwan
| | - Tsai-Yuan Hsieh
- Division of Gastroenterology, Department of Internal Medicine, Tri-Service General Hospital, National Defense Medical Center, Taipei, Taiwan
| | - Jui-Ting Hu
- Liver Center, Cathay General Hospital, Taipei, Taiwan
| | - Wen-Chih Wu
- Wen-Chih Wu Clinic, Fengshan, Kaohsiung, Taiwan
| | - Chien-Yu Cheng
- Division of Infectious Diseases, Department of Internal Medicine, Taoyuan General Hospital, Ministry of Health and Welfare, Taoyuan, Taiwan
| | - Guei-Ying Chen
- Penghu Hospital, Ministry of Health and Welfare, Penghu, Taiwan
| | | | - Wei-Lun Tsai
- Kaohsiung Veterans General Hospital, Kaohsiung, Taiwan
| | - Chien-Neng Kao
- National Taiwan University Hospital Hsin-Chu Branch, Hsinchu, Taiwan
| | - Chih-Lang Lin
- Liver Research Unit, Department of Hepato-Gastroenterology and Community Medicine Research Center, Chang Gung Memorial Hospital at Keelung, College of Medicine, Chang Gung University, Keelung, Taiwan
| | - Chia-Chi Wang
- Taipei Tzu Chi Hospital, Buddhist Tzu Chi Medical Foundation and School of Medicine, Tzu Chi University, Taipei, Taiwan
| | - Ta-Ya Lin
- Cishan Hospital, Ministry of Health and Welfare, Kaohsiung, Taiwan
| | - Chih‐Lin Lin
- Department of Gastroenterology, Renai Branch, Taipei City Hospital, Taipei, Taiwan
| | - Wei-Wen Su
- Department of Gastroenterology and Hepatology, Changhua Christian Hospital, Changhua, Taiwan
| | - Tzong-Hsi Lee
- Division of Gastroenterology and Hepatology, Far Eastern Memorial Hospital, New Taipei City, Taiwan
| | - Te-Sheng Chang
- Division of Hepatogastroenterology, Department of Internal Medicine, Chang Gung Memorial Hospital, Chiayi, Taiwan and College of Medicine, Chang Gung University, Taoyuan, Taiwan
| | - Chun-Jen Liu
- Hepatitis Research Center and Department of Internal Medicine, National Taiwan University Hospital, Taipei, Taiwan
| | - Chia-Yen Dai
- Hepatobiliary Division, Department of Internal Medicine and Hepatitis Center, Kaohsiung Medical University Hospital, Kaohsiung Medical University, Kaohsiung, Taiwan
- Hepatitis Research Center, College of Medicine and Center for Liquid Biopsy and Cohort Research, Kaohsiung Medical University, Kaohsiung, Taiwan
| | - Jia-Horng Kao
- Hepatitis Research Center and Department of Internal Medicine, National Taiwan University Hospital, Taipei, Taiwan
| | - Han-Chieh Lin
- Division of Gastroenterology and Hepatology, Department of Medicine, Taipei Veterans General Hospital, Taipei, Taiwan
- Institute of Clinical Medicine, School of Medicine, National Yang-Ming Chiao Tung University, Taipei, Taiwan
| | - Wan-Long Chuang
- Hepatobiliary Division, Department of Internal Medicine and Hepatitis Center, Kaohsiung Medical University Hospital, Kaohsiung Medical University, Kaohsiung, Taiwan
- Hepatitis Research Center, College of Medicine and Center for Liquid Biopsy and Cohort Research, Kaohsiung Medical University, Kaohsiung, Taiwan
| | - Cheng-Yuan Peng
- Center for Digestive Medicine, Department of Internal Medicine, China Medical University Hospital, Taichung, Taiwan
- School of Medicine, China Medical University, Taichung, Taiwan
| | - Chun-Wei- Tsai
- Department of Computer Science and Engineering, National Sun Yat-sen University, Kaohsiung, Taiwan
| | - Chi-Yi Chen
- Division of Gastroenterology and Hepatology, Department of Medicine, Ditmanson Medical Foundation Chiayi Christian Hospital, Chiayi, Taiwan
| | - Ming-Lung Yu
- School of Medicine and Doctoral Program of Clinical and Experimental Medicine, College of Medicine and Center of Excellence for Metabolic Associated Fatty Liver Disease, National Sun Yat-sen University, Kaohsiung, Taiwan
- Hepatobiliary Division, Department of Internal Medicine and Hepatitis Center, Kaohsiung Medical University Hospital, Kaohsiung Medical University, Kaohsiung, Taiwan
- Hepatitis Research Center, College of Medicine and Center for Liquid Biopsy and Cohort Research, Kaohsiung Medical University, Kaohsiung, Taiwan
- Division of Hepatogastroenterology, Department of Internal Medicine, Kaohsiung Chang Gung Memorial Hospital, Kaohsiung, Taiwan
| | - TACR Study Group
- School of Medicine and Doctoral Program of Clinical and Experimental Medicine, College of Medicine and Center of Excellence for Metabolic Associated Fatty Liver Disease, National Sun Yat-sen University, Kaohsiung, Taiwan
- Hepatobiliary Division, Department of Internal Medicine and Hepatitis Center, Kaohsiung Medical University Hospital, Kaohsiung Medical University, Kaohsiung, Taiwan
- Hepatitis Research Center, College of Medicine and Center for Liquid Biopsy and Cohort Research, Kaohsiung Medical University, Kaohsiung, Taiwan
- Ph.D. Program in Translational Medicine, College of Medicine, Kaohsiung Medical University, Kaohsiung, and Academia Sinica, Taipei, Taiwan
- Division of Hepatogastroenterology, Department of Internal Medicine, Kaohsiung Chang Gung Memorial Hospital, Kaohsiung, Taiwan
- Division of Gastroenterology and Hepatology, Department of Internal Medicine, E-Da Hospital, I-Shou University, Kaohsiung, Taiwan
- School of Medicine for International Students, College of Medicine, I-Shou University, Kaohsiung, Taiwan
- Division of Gastroenterology, Tainan Municipal Hospital (Managed By Show Chwan Medical Care Corporation), Tainan, Taiwan
- Division of Gastroenterology and Hepatology, Department of Internal Medicine, Chi Mei Medical Center, Yongkang District, Tainan, Taiwan
- Department of Internal Medicine, Dalin Tzu Chi Hospital, Buddhist Tzu Chi Medical Foundation, Chiayi, Taiwan
- School of Medicine, Tzuchi University, Hualien, Taiwan
- Division of Gastroenterology, Department of Internal Medicine, St. Martin De Porres Hospital, Chiayi, Taiwan
- Division of Gastroenterology, Department of Internal Medicine, Taitung Mackay Memorial Hospital, Taitung, Taiwan
- Mackay Medical College, New Taipei City, Taiwan
- Division of Gastroenterology, Department of Internal Medicine, Yuan’s General Hospital, Kaohsiung, Taiwan
- Division of Gastroenterology, Department of Internal Medicine, Tri-Service General Hospital, National Defense Medical Center, Taipei, Taiwan
- Division of Gastroenterology, Department of Internal Medicine, Tri-Service General Hospital Penghu Branch, National Defense Medical Center, Taipei, Taiwan
- School of Medicine, Chung Shan Medical University, Department of Internal Medicine, Chung Shan Medical University Hospital, Taichung, Taiwan
- Division of Gastroenterology, Kaohsiung Armed Forces General Hospital, Kaohsiung, Taiwan
- Division of Gastroenterology and Hepatology, Department of Internal Medicine, Chi Mei Medical Center, Liouying, Tainan, Taiwan
- Lotung Poh-Ai Hospital, Yilan, Taiwan
- Division of Gastroenterology and Hepatology, Department of Medicine, Taipei Veterans General Hospital, Taipei, Taiwan
- Institute of Clinical Medicine, School of Medicine, National Yang-Ming Chiao Tung University, Taipei, Taiwan
- Division of Hepatology and Gastroenterology, Department of Internal Medicine, Shin Kong Wu Ho-Su Memorial Hospital, Taipei, Taiwan
- School of Medicine, Fu-Jen Catholic University, New Taipei City, Taiwan
- Department of Medicine, Hualien Tzu Chi Hospital, Buddhist Tzu Chi Medical Foundation and Tzu Chi University, Hualien, Taiwan
- Department of Gastroenterology, Division of Internal Medicine, Show Chwan Memorial Hospital, Changhua, Taiwan
- Division of Gastroenterology and Hepatology, Department of Internal Medicine, Taichung Veterans General Hospital, Taichung, Taiwan
- Division of Gastroenterology and Hepatology, Department of Internal Medicine, National Cheng Kung University Hospital, College of Medicine, National Cheng Kung University, Tainan, Taiwan
- Liver Center, Cathay General Hospital, Taipei, Taiwan
- Wen-Chih Wu Clinic, Fengshan, Kaohsiung, Taiwan
- Division of Infectious Diseases, Department of Internal Medicine, Taoyuan General Hospital, Ministry of Health and Welfare, Taoyuan, Taiwan
- Penghu Hospital, Ministry of Health and Welfare, Penghu, Taiwan
- Zhou Guoxiong Clinic, Penghu, Taiwan
- Kaohsiung Veterans General Hospital, Kaohsiung, Taiwan
- National Taiwan University Hospital Hsin-Chu Branch, Hsinchu, Taiwan
- Liver Research Unit, Department of Hepato-Gastroenterology and Community Medicine Research Center, Chang Gung Memorial Hospital at Keelung, College of Medicine, Chang Gung University, Keelung, Taiwan
- Taipei Tzu Chi Hospital, Buddhist Tzu Chi Medical Foundation and School of Medicine, Tzu Chi University, Taipei, Taiwan
- Cishan Hospital, Ministry of Health and Welfare, Kaohsiung, Taiwan
- Department of Gastroenterology, Renai Branch, Taipei City Hospital, Taipei, Taiwan
- Department of Gastroenterology and Hepatology, Changhua Christian Hospital, Changhua, Taiwan
- Division of Gastroenterology and Hepatology, Far Eastern Memorial Hospital, New Taipei City, Taiwan
- Division of Hepatogastroenterology, Department of Internal Medicine, Chang Gung Memorial Hospital, Chiayi, Taiwan and College of Medicine, Chang Gung University, Taoyuan, Taiwan
- Hepatitis Research Center and Department of Internal Medicine, National Taiwan University Hospital, Taipei, Taiwan
- Center for Digestive Medicine, Department of Internal Medicine, China Medical University Hospital, Taichung, Taiwan
- School of Medicine, China Medical University, Taichung, Taiwan
- Department of Computer Science and Engineering, National Sun Yat-sen University, Kaohsiung, Taiwan
- Division of Gastroenterology and Hepatology, Department of Medicine, Ditmanson Medical Foundation Chiayi Christian Hospital, Chiayi, Taiwan
| |
Collapse
|
17
|
Wang Z, Shi P, Huang P, Xu C, He Y, Lei W. Identification of secretory pathway-related genes based on Random Forest algorithm to predict the prognosis and immunotherapy response of hepatocellular carcinoma. J Gene Med 2024; 26:e3593. [PMID: 37730948 DOI: 10.1002/jgm.3593] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/07/2023] [Revised: 07/31/2023] [Accepted: 08/25/2023] [Indexed: 09/22/2023] Open
Abstract
BACKGROUND The dysfunction of secretory pathways may represent biomarkers or therapeutic targets of cancer. The hepatocellular carcinoma (HCC) phenotype was studied in relation to the genes in the secretory pathway and to screen for a combination of genes that may be a viable therapeutic target for HCC and connected to the pathophysiological features of the tumor. METHODS Using the HCC information from The Cancer Genome Atlas, somatic mutation and prognostic association analysis were performed on the secretory pathway genes. Based on prognostic genes in the secretory pathway, the samples were consensus clustered, and a Random Forest model was built. The clinical characteristics, tumor mutation burden, functional status and potential responses to immunotherapy and tumor suppressor medications of various subtypes and risk groups were discussed. RESULTS Of the 84 genes for secretory pathway, 32 were prognostic genes related to HCC, which divided HCC into two categories: C1 and C2. By comparing the two types of HCC samples, it was found that the survival outcome of C1 was inferior, with stronger adaptive and innate immunity, but less sensitive to immunotherapy than C2. The constructed prognostic signature included seven of the 32 prognostic genes in the secretory pathway, which showed significant correlation with the prognosis, somatic mutation, biological pathway status, potential response to immunotherapy and sensitivity of 72 tumor suppressor drugs from different HCC cohorts, and had a feasible prognostic effect for 31 types of cancer and immunotherapy cohorts. CONCLUSIONS In this study, HCC was divided into two molecular subtypes according to prognostic genes in the secretory pathway, and seven of them were combined into one signature, which produced significant results in evaluating the prognosis of different HCC cohorts, pan-cancer cohorts and immunotherapy cohorts, and had potential guiding significance for prophylactic immunotherapy in patients with HCC.
Collapse
Affiliation(s)
- Zanzhi Wang
- Department of Emergency Medicine, Nanfang Hospital, Southern Medical University, Guangzhou, China
| | - Pengwei Shi
- Department of Emergency Medicine, Nanfang Hospital, Southern Medical University, Guangzhou, China
| | - Peng Huang
- Department of Emergency Medicine, Nanfang Hospital, Southern Medical University, Guangzhou, China
| | - Chun Xu
- Department of Emergency Medicine, Nanfang Hospital, Southern Medical University, Guangzhou, China
| | - Yaoquan He
- Department of Emergency Medicine, Nanfang Hospital, Southern Medical University, Guangzhou, China
| | - Wenxiong Lei
- Department of Emergency Medicine, Nanfang Hospital, Southern Medical University, Guangzhou, China
| |
Collapse
|
18
|
Zhang CS, Zeng ZM, Zhuo MY, Luo JR, Zhuang XH, Xu JN, Zeng J, Ma J, Lin HF. Anlotinib Combined With Toripalimab as First-Line Therapy for Unresectable Hepatocellular Carcinoma: A Prospective, Multicenter, Phase II Study. Oncologist 2023; 28:e1239-e1247. [PMID: 37329569 PMCID: PMC10712726 DOI: 10.1093/oncolo/oyad169] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/01/2023] [Accepted: 05/01/2023] [Indexed: 06/19/2023] Open
Abstract
BACKGROUND For patients with unresectable hepatocellular carcinoma (HCC), the first-line therapeutic options are still relatively limited, and treatment outcomes remain poor. We aimed to assess the efficacy and safety of anlotinib combined with toripalimab as first-line therapy for unresectable HCC. METHODS In this single-arm, multicenter, phase II study (ALTER-H-003), patients with advanced HCC without previous systemic anticancer therapy were recruited. Eligible patients were given anlotinib (12 mg on days 1-14) combined with toripalimab (240 mg on day 1) in a 3-week cycle. The primary endpoint was the objective response rate (ORR) by immune-related Response Evaluation Criteria in Solid Tumours (irRECIST)/RECIST v1.1 and modified RECIST (mRECIST). Secondary endpoints included disease control rate (DCR), duration of response (DoR), progression-free survival (PFS), overall survival (OS), and safety. RESULTS Between January 2020 and Jul 2021, 31 eligible patients were treated and included in the full analysis set. At data cutoff (January 10, 2023), the ORR was 29.0% (95% CI: 12.1%-46.0%) by irRECIST/RECIST v1.1, and 32.3% (95% CI: 14.8%-49.7%) by mRECIST criteria, respectively. Confirmed DCR and median DoR by irRECIST/RECIST v1.1 and mRECIST criteria were 77.4 % (95% CI: 61.8%-93.0%) and not reached (range: 3.0-22.5+ months), respectively. Median PFS was 11.0 months (95% CI: 3.4-18.5 months) and median OS was 18.2 months (95% CI: 15.8-20.5 months). Of the 31 patients assessed for adverse events (AEs), the most common grade ≥ 3 treatment-related AEs were hand-foot syndrome (9.7%, 3/31), hypertension (9.7%, 3/31), arthralgia (9.7%, 3/31), abnormal liver function (6.5%, 2/31), and decreased neutrophil counts (6.5%, 2/31). CONCLUSIONS Anlotinib combined with toripalimab showed promising efficacy and manageable safety in Chinese patients with unresectable HCC in the first-line setting. This combination therapy may offer a potential new therapeutic approach for patients with unresectable HCC.
Collapse
Affiliation(s)
- Cheng-Sheng Zhang
- Department of Medical Oncology, The Second Affiliated Hospital of Hainan Medical University, Haikou, People’s Republic of China
| | - Zhi-Ming Zeng
- Department of Medical Oncology, The First Affiliated Hospital of Guangxi Medical University, Nanning, People’s Republic of China
| | - Man-Yun Zhuo
- Department of Medical Oncology, The Second Affiliated Hospital of Hainan Medical University, Haikou, People’s Republic of China
| | - Jing-Ru Luo
- Department of Medical Oncology, The Second Affiliated Hospital of Hainan Medical University, Haikou, People’s Republic of China
| | - Xiao-Hong Zhuang
- Department of Medical Oncology, The Second Affiliated Hospital of Hainan Medical University, Haikou, People’s Republic of China
| | - Jun-Nv Xu
- Department of Medical Oncology, The Second Affiliated Hospital of Hainan Medical University, Haikou, People’s Republic of China
| | - Jie Zeng
- Department of Medical Oncology, The First Affiliated Hospital of Guangxi Medical University, Nanning, People’s Republic of China
| | - Jie Ma
- Department of Medical Oncology, The First Affiliated Hospital of Guangxi Medical University, Nanning, People’s Republic of China
| | - Hai-Feng Lin
- Department of Medical Oncology, The Second Affiliated Hospital of Hainan Medical University, Haikou, People’s Republic of China
| |
Collapse
|
19
|
Su Y, Lu Y, An H, Liu J, Ye F, Shen J, Ni Z, Huang B, Lin J. MicroRNA-204-5p Inhibits Hepatocellular Carcinoma by Targeting the Regulator of G Protein Signaling 20. ACS Pharmacol Transl Sci 2023; 6:1817-1828. [PMID: 38093845 PMCID: PMC10714421 DOI: 10.1021/acsptsci.3c00114] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/09/2023] [Revised: 10/08/2023] [Accepted: 10/12/2023] [Indexed: 03/14/2024]
Abstract
Although the oncogenic roles of regulator of G protein signaling 20 (RGS20) and its upstream microRNAs (miRNAs) have been reported, their involvement in hepatocellular carcinoma (HCC) remains unexplored. We utilized the starBase, miRDB, TargetScan, and mirDIP databases, along with a dual-luciferase reporter assay and cDNA chip analysis to identify miRNAs targeting RGS20. miR-204-5p was selected for further experiments to confirm its direct targeting and downregulation of the RGS20 expression. To study the miR-204-5p/RGS20 axis in HCC, RGS20 and miR-204-5p were increased in PLC/PRF/5/Hep3B cells, and the viability, hyperplasia, apoptosis, cell cycle, and invasion/migration of the cells were assessed. RGS20 exhibited optimism, while miR-204-5p exhibited pessimism in tumors. miR-204-5p directly targeted RGS20 and downregulated its expression, whereas high RGS20 expression indicated a poor prognosis. Transfection of miR-204-5p inhibited the hyperplasia, migration, and invasion of HCC cells, but promoted apoptosis and influenced the levels of cyclin-dependent kinase 2 (CDK2), cyclin E1, B-cell lymphoma-2 (Bcl-2), Bax, and cleaved caspase-3/8. These effects were reversed by overexpression of RGS20. We recognized miR-204-5p as an upstream regulator targeting RGS20, thereby inhibiting HCC progression by downregulating RGS20 expression. RGS20 may prove to be a potential target for HCC treatment, and miR-204-5p might seem like to be a potential miRNA in gene therapy.
Collapse
Affiliation(s)
- Yanqing Su
- Department
of Pharmacy, Xiamen Children’s Hospital, Xiamen, Fujian 361006, China
- Academy
of Integrative Medicine of Fujian University of Traditional Chinese
Medicine, Fuzhou, Fujian 350122, China
| | - Yao Lu
- Academy
of Integrative Medicine of Fujian University of Traditional Chinese
Medicine, Fuzhou, Fujian 350122, China
- Hebei
Provincial Hospital of Traditional Chinese Medicine, Shijiazhuang, Hebei 050011, China
| | - Honglin An
- Academy
of Integrative Medicine of Fujian University of Traditional Chinese
Medicine, Fuzhou, Fujian 350122, China
| | - Jinhong Liu
- Academy
of Integrative Medicine of Fujian University of Traditional Chinese
Medicine, Fuzhou, Fujian 350122, China
- Fujian
Key Laboratory of Integrative Medicine on Geriatrics, Fujian University of Traditional Chinese Medicine, Fuzhou, Fujian 350122, China
- Key
Laboratory of Integrative Medicine of Fujian Province University, Fujian University of Traditional Chinese Medicine, Fuzhou, Fujian 350122, China
| | - Feimin Ye
- Academy
of Integrative Medicine of Fujian University of Traditional Chinese
Medicine, Fuzhou, Fujian 350122, China
| | - Jiayu Shen
- Academy
of Integrative Medicine of Fujian University of Traditional Chinese
Medicine, Fuzhou, Fujian 350122, China
| | - Zhuona Ni
- Academy
of Integrative Medicine of Fujian University of Traditional Chinese
Medicine, Fuzhou, Fujian 350122, China
| | - Bin Huang
- Academy
of Integrative Medicine of Fujian University of Traditional Chinese
Medicine, Fuzhou, Fujian 350122, China
- Fujian
Key Laboratory of Integrative Medicine on Geriatrics, Fujian University of Traditional Chinese Medicine, Fuzhou, Fujian 350122, China
- Key
Laboratory of Integrative Medicine of Fujian Province University, Fujian University of Traditional Chinese Medicine, Fuzhou, Fujian 350122, China
| | - Jiumao Lin
- Academy
of Integrative Medicine of Fujian University of Traditional Chinese
Medicine, Fuzhou, Fujian 350122, China
- Fujian
Key Laboratory of Integrative Medicine on Geriatrics, Fujian University of Traditional Chinese Medicine, Fuzhou, Fujian 350122, China
- Key
Laboratory of Integrative Medicine of Fujian Province University, Fujian University of Traditional Chinese Medicine, Fuzhou, Fujian 350122, China
| |
Collapse
|
20
|
Zhao J, Luo Z, Fu R, Zhou J, Chen S, Wang J, Chen D, Xie X. Disulfidptosis-related signatures for prognostic and immunotherapy reactivity evaluation in hepatocellular carcinoma. Eur J Med Res 2023; 28:571. [PMID: 38057871 DOI: 10.1186/s40001-023-01535-3] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/29/2023] [Accepted: 11/17/2023] [Indexed: 12/08/2023] Open
Abstract
BACKGROUND Hepatocellular carcinoma (HCC) is one of the most common cancers in the world and a nonnegligible health concern on a worldwide scale. Disulfidptosis is a novel mode of cell death, which is mainly caused by the collapse of the actin skeleton. Although many studies have demonstrated that various types of cell death are associated with cancer treatment, the relationship between disulfidptosis and HCC has not been elucidated. METHODS Here, we mainly applied bioinformatics methods to construct a disulfidptosis related risk model in HCC patients. Specifically, transcriptome data and clinical information were downloaded from the Gene Expression Omnibus (GEO), International Cancer Genome Consortium (ICGC) and The Cancer Genome Atlas (TCGA) database. A total of 45 co-expressed genes were extracted between the disulfidptosis-related genes (DRGs) and the differential expression genes (DEGs) of liver hepatocellular carcinoma (LIHC) in the TCGA database. The LIHC cohort was divided into two subgroups with different prognosis by k-mean consensus clustering and functional enrichment analysis was performed. Subsequently, three hub genes (CDCA8, SPP2 and RDH16) were screened by Cox regression and LASSO regression analysis. In addition, a risk signature was constructed and the HCC cohort was divided into high risk score and low risk score subgroups to compare the prognosis, clinical features and immune landscape between the two subgroups. Finally, the prognostic model of independent risk factors was constructed and verified. CONCLUSIONS High DRGs-related risk score in HCC individuals predict poor prognosis and are associated with poor immunotherapy response, which indicates that risk score assessment model can be utilized to guide clinical treatment strategy.
Collapse
Affiliation(s)
- Jiajing Zhao
- Department of General Surgery, the First Affiliated Hospital of Shantou University Medical College, Shantou, 515000, China
| | - Zeminshan Luo
- Department of General Surgery, the First Affiliated Hospital of Shantou University Medical College, Shantou, 515000, China
| | - Ruizhi Fu
- Department of General Surgery, the First Affiliated Hospital of Shantou University Medical College, Shantou, 515000, China
| | - Jinghong Zhou
- Department of General Surgery, the First Affiliated Hospital of Shantou University Medical College, Shantou, 515000, China
| | - Shubiao Chen
- Department of General Surgery, the First Affiliated Hospital of Shantou University Medical College, Shantou, 515000, China
| | - Jianjie Wang
- Department of General Surgery, the First Affiliated Hospital of Shantou University Medical College, Shantou, 515000, China
| | - Dewang Chen
- Department of General Surgery, the First Affiliated Hospital of Shantou University Medical College, Shantou, 515000, China
| | - Xiaojun Xie
- Department of General Surgery, the First Affiliated Hospital of Shantou University Medical College, Shantou, 515000, China.
| |
Collapse
|
21
|
Liu HF, Wang M, Lu YJ, Wang Q, Lu Y, Xing F, Xing W. CEMRI-Based Quantification of Intratumoral Heterogeneity for Predicting Aggressive Characteristics of Hepatocellular Carcinoma Using Habitat Analysis: Comparison and Combination of Deep Learning. Acad Radiol 2023:S1076-6332(23)00659-1. [PMID: 38057182 DOI: 10.1016/j.acra.2023.11.024] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/09/2023] [Revised: 11/13/2023] [Accepted: 11/15/2023] [Indexed: 12/08/2023]
Abstract
RATIONALE AND OBJECTIVES To explore both an intratumoral heterogeneity (ITH) model based on habitat analysis and a deep learning (DL) model based on contrast-enhanced magnetic resonance imaging (CEMRI) and validate its efficiency for predicting microvascular invasion (MVI) and pathological differentiation in hepatocellular carcinoma (HCC). METHODS CEMRI images were retrospectively obtained from 277 HCCs in 265 patients. Habitat analysis and DL features were extracted from the CEMRI images and selected with the least absolute shrinkage and selection operator approach to develop ITH and DL models, respectively, and these robust features were then integrated to design a fusion model for predicting MVI and poorly differentiated HCC (pHCC). The predictive value of the three models was assessed using the area under the receiver operating characteristic curve (AUC). RESULTS The training and validation sets comprised 221 HCCs and 56 HCCs, respectively. The ITH and DL models presented AUC values of (0.90 vs. 0.87) for predicting MVI in the training set, with AUC values of 0.86 and 0.83 in the validation set. The AUC values of the ITH model to predict pHCC were 0.90 and 0.86 in the two sets, respectively; they were 0.84 and 0.80 for the DL model. The fusion model yielded the best performance for predicting MVI and pHCC in the training set (AUC=0.95, 0.90) and in the validation set (AUC=0.89, 0.87), respectively. CONCLUSION A fusion model integrating ITH and DL features derived from CEMRI images can serve as an excellent imaging biomarker for predicting aggressive characteristics in HCC.
Collapse
Affiliation(s)
- Hai-Feng Liu
- Department of Radiology, Third Affiliated Hospital of Soochow University, No.185, Juqian ST, Tianning District, Changzhou, 213000, Jiangsu, China (H.-F.L., Y.-J.L., Q.W., Y.L., W.X.)
| | - Min Wang
- Department of Anesthesiology, The Second People's Hospital of Changzhou, Affiliated Hospital of Nanjing Medical University, Changzhou, Jiangsu, China (M.W.)
| | - Yu-Jie Lu
- Department of Radiology, Third Affiliated Hospital of Soochow University, No.185, Juqian ST, Tianning District, Changzhou, 213000, Jiangsu, China (H.-F.L., Y.-J.L., Q.W., Y.L., W.X.)
| | - Qing Wang
- Department of Radiology, Third Affiliated Hospital of Soochow University, No.185, Juqian ST, Tianning District, Changzhou, 213000, Jiangsu, China (H.-F.L., Y.-J.L., Q.W., Y.L., W.X.)
| | - Yang Lu
- Department of Radiology, Third Affiliated Hospital of Soochow University, No.185, Juqian ST, Tianning District, Changzhou, 213000, Jiangsu, China (H.-F.L., Y.-J.L., Q.W., Y.L., W.X.)
| | - Fei Xing
- Department of Radiology, Nantong Third People's Hospital, Nantong, Jiangsu, China (F.X.)
| | - Wei Xing
- Department of Radiology, Third Affiliated Hospital of Soochow University, No.185, Juqian ST, Tianning District, Changzhou, 213000, Jiangsu, China (H.-F.L., Y.-J.L., Q.W., Y.L., W.X.).
| |
Collapse
|
22
|
Carr BI, Rui F, Ince V, Yilmaz S, Zhao X, Feng Y, Li J. Comparison of patients with hepatitis B virus-associated hepatocellular carcinoma: Data from two hospitals from Turkey and China. PORTAL HYPERTENSION & CIRRHOSIS 2023; 2:165-170. [PMID: 38179146 PMCID: PMC10766430 DOI: 10.1002/poh2.60] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Subscribe] [Scholar Register] [Received: 03/30/2023] [Accepted: 07/25/2023] [Indexed: 01/06/2024]
Abstract
Aims There are many studies on the incidence of hepatitis B virus (HBV)-associated hepatocellular carcinoma (HCC), but very little is known about the HCC features in different populations. The study aimed to compare characteristics in two cohorts of patients with HBV-associated hepatocellular carcinoma, from Turkey and China. Methods Data on patients with HBV-associated HCC diagnosed by imaging or liver biopsy were retrospectively collected from Shandong Provincial Hospital (n = 578) and Inonu University Hospital (n = 359) between January 2002 and December 2020, and the liver function and HCC characteristics were compared. Continuous variables were compared using Student's t-test or Mann-Whitney U test and categorical variables were compared using the χ2 test or Fisher's exact test. Results The patients in the Turkish cohort had significantly worse Child-Pugh scores (Child-Pugh A: 38.3% vs. 87.9%; Child-Pugh B: 40.3% vs. 11.1%; Child-Pugh A: 24.1% vs. 1.0%; p < 0.001) and significantly higher levels of aspartate aminotransferase (66.5 vs. 36.0; p < 0.001), alanine aminotransferase (47.5 vs. 33.0; p < 0.001), total bilirubin (20.8 vs. 17.9; p < 0.001), and lower albumin levels (32.0 vs. 40.0; p < 0.001) than patients in Chinese cohort. The tumor characteristics showed the Barcelona Clinic Liver Cancer (BCLC) score (BCLC 1: 5.1% vs. 71.8%; BCLC 2: 48.7% vs. 24.4%; BCLC 3: 24.4% vs. 3.8%; BCLC 4: 21.8% vs. 0; all p < 0.001), maximum tumor diameter (5.0 vs. 3.5; p < 0.001), alpha-fetoprotein values (27.7 vs. 13.2; p < 0.001), and percentage of patients with portal vein tumor thrombus (33% vs. 6.1%; p < 0.001) were all significantly worse in the Turkish cohort compared with Chinese cohort. Conclusions HBV-associated HCC from the Turkish cohort had worse liver function and more aggressive clinical characteristics than patients from the Chinese cohort.
Collapse
Affiliation(s)
- Brian I. Carr
- Liver Transplantation Institute, Inonu University, Malatya, Turkey
| | - Fajuan Rui
- Department of Infectious Diseases, Nanjing Drum Tower Hospital Clinical College of Nanjing University of Chinese Medicine, Nanjing, Jiangsu, China
| | - Volkan Ince
- Liver Transplantation Institute, Inonu University, Malatya, Turkey
| | - Sezai Yilmaz
- Liver Transplantation Institute, Inonu University, Malatya, Turkey
| | - Xinya Zhao
- Department of Radiology, Shandong Provincial Hospital Affiliated to Shandong University, Jinan, Shandong, China
| | - Yuemin Feng
- Department of Gastroenterology, Shandong Provincial Hospital Affiliated to Shandong University, Jinan, Shandong, China
| | - Jie Li
- Department of Infectious Diseases, Nanjing Drum Tower Hospital, Affiliated Hospital of Medical School, Nanjing University, Nanjing, Jiangsu, China
| |
Collapse
|
23
|
Gu HX, Huang XS, Xu JX, Zhu P, Xu JF, Fan SF. Diagnostic Value of MRI Features in Dual-phenotype Hepatocellular Carcinoma: A Preliminary Study. J Digit Imaging 2023; 36:2554-2566. [PMID: 37578576 PMCID: PMC10584802 DOI: 10.1007/s10278-023-00888-9] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/21/2023] [Revised: 07/20/2023] [Accepted: 07/21/2023] [Indexed: 08/15/2023] Open
Abstract
This study aimed to explore the magnetic resonance imaging (MRI) features of dual-phenotype hepatocellular carcinoma (DPHCC) and their diagnostic value.The data of 208 patients with primary liver cancer were retrospectively analysed between January 2016 and June 2021. Based on the pathological diagnostic criteria, 27 patients were classified into the DPHCC group, 113 patients into the noncholangiocyte-phenotype hepatocellular carcinoma (NCPHCC) group, and 68 patients with intrahepatic cholangiocarcinoma (ICC) were classified into the ICC group. Two abdominal radiologists reviewed the preoperative MRI features by a double-blind method. The MRI features and key laboratory and clinical indicators were compared between the groups. The potentially valuable MRI features and key laboratory and clinical characteristics for predicting DPHCC were identified by univariate and multivariate analyses, and the odds ratios (ORs) were recorded. In multivariate analysis, tumour without capsule (P = 0.046, OR = 9.777), dynamic persistent enhancement (P = 0.006, OR = 46.941), and targetoid appearance on diffusion-weighted imaging (DWI) (P = 0.021, OR = 30.566) were independently significant factors in the detection of DPHCC compared to NCPHCC. Serum alpha-fetoprotein (AFP) > 20 µg/L (P = 0.036, OR = 67.097) and prevalence of hepatitis B virus (HBV) infection (P = 0.020, OR = 153.633) were independent significant factors in predicting DPHCC compared to ICC. The differences in other tumour marker levels and imaging features between the groups were not significant. In MR enhanced and diffusion imaging, tumour without capsule, persistent enhancement and DWI targetoid findings, combined with AFP > 20 µg/L and HBV infection-positive laboratory results, can help to diagnose DPHCC and differentiate it from NCPHCC and ICC. These results suggest that clinical, laboratory and MRI features should be integrated to construct an AI diagnostic model for DPHCC.
Collapse
Affiliation(s)
- Hong-Xian Gu
- Radiology Department, Second Affiliated Hospital of Zhejiang Chinese Medical University, Hangzhou, 310005, China
- Department of Radiology, the People's Hospital of Jianyang City, Chengdu, 641499, China
| | - Xiao-Shan Huang
- Radiology Department, Second Affiliated Hospital of Zhejiang Chinese Medical University, Hangzhou, 310005, China
| | - Jian-Xia Xu
- Radiology Department, Second Affiliated Hospital of Zhejiang Chinese Medical University, Hangzhou, 310005, China
| | - Ping Zhu
- Radiology Department, Second Affiliated Hospital of Zhejiang Chinese Medical University, Hangzhou, 310005, China
| | - Jian-Feng Xu
- Department of Radiology, Shulan (Hangzhou) Hospital, Hangzhou, 310000, China.
| | - Shu-Feng Fan
- Radiology Department, Second Affiliated Hospital of Zhejiang Chinese Medical University, Hangzhou, 310005, China.
| |
Collapse
|
24
|
Suo L, Gao M, Ma T, Gao Z. Effect of RPL27 knockdown on the proliferation and apoptosis of human liver cancer cells. Biochem Biophys Res Commun 2023; 682:156-162. [PMID: 37812860 DOI: 10.1016/j.bbrc.2023.10.012] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/07/2023] [Revised: 09/25/2023] [Accepted: 10/03/2023] [Indexed: 10/11/2023]
Abstract
RPL27 is linked to the development of various diseases including malignant tumors. RPL27 may play an oncogenic function in hepatocellular carcinoma (HCC), but this is unknown. So, the aim of this study was to investigate how the human liver cancer cell lines SNU449 and HepG2 responded to RPL27 knockdown in terms of proliferation and apoptosis. SNU449 and HepG2 were cultured and infected with shCon and shRPL27 lentiviral particles to induce RPL27 knockdown, and then RPL27 expression was detected using qPCR and Western blot. Cell proliferation was measured using CCK8, cell cloning, cell scraping, and transwell migration and invasion, while apoptosis was measured using flow cytometry (FCM). The qPCR revealed that mRNA expression of RPL27 decreased after knocking down RPL27 in cells. The CCK8 and cell cloning assay confirmed that knocking down RPL27 significantly reduced cell viability. The cell scratch assay and transwell assays showed that the proliferation rate decreased after knocking down RPL27. A substantial increase in apoptotic cells was discovered by FCM. According to WB, RPL27 knockdown increased the expression of Bax and Caspase-3 while decreasing the expression of bcl-2. The findings showed that RPL27 knockdown inhibited cell proliferation in SNU449 and HepG2 via inducing apoptosis, proving that RPL27 is a novel gene linked with HCC and is crucial for both proliferation and apoptosis. These outcomes imply that RPL27 may be a potential target for liver cancer diagnosis and therapy.
Collapse
Affiliation(s)
- Lida Suo
- Division of Hepatobiliary and Pancreatic Surgery, Department of General Surgery, The Second Hospital of Dalian Medical University, Dalian, China.
| | - Mingwei Gao
- Division of Hepatobiliary and Pancreatic Surgery, Department of General Surgery, The Second Hospital of Dalian Medical University, Dalian, China.
| | - Taiheng Ma
- Division of Hepatobiliary and Pancreatic Surgery, Department of General Surgery, The Second Hospital of Dalian Medical University, Dalian, China.
| | - Zhenming Gao
- Division of Hepatobiliary and Pancreatic Surgery, Department of General Surgery, The Second Hospital of Dalian Medical University, Dalian, China.
| |
Collapse
|
25
|
Ding, D, Wang, D, Qin Y. Development and validation of multi-omic prognostic signature of anoikis-related genes in liver hepatocellular carcinoma. Medicine (Baltimore) 2023; 102:e36190. [PMID: 37986299 PMCID: PMC10659623 DOI: 10.1097/md.0000000000036190] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 04/27/2023] [Revised: 10/24/2023] [Accepted: 10/27/2023] [Indexed: 11/22/2023] Open
Abstract
Liver hepatocellular carcinoma (LIHC) is characterized by high morbidity, rapid progression and early metastasis. Although many efforts have been made to improve the prognosis of LIHC, the situation is still dismal. Inability to initiate anoikis process is closely associated with cancer proliferation and metastasis, affecting patients' prognosis. In this study, a corresponding gene signature was constructed to comprehensively assess the prognostic value of anoikis-related genes (ARGs) in LIHC. Using TCGA-LIHC dataset, the mRNA levels of the differentially expressed ARGs in LIHC and normal tissues were compared by Student t test. And prognostic ARGs were identified through Cox regression analysis. Prognostic signature was established and then externally verified by ICGC-LIRI-JP dataset and GES14520 dataset via LASSO Cox regression model. Potential functions and mechanisms of ARGs in LIHC were evaluated by functional enrichment analyses. And the immune infiltration status in prognostic signature was analyzed by ESTIMATE algorithm and ssGSEA algorithm. Furthermore, ARGs expression in LIHC tissues was validated via qRT-PCR and IHC staining from the HPA website. A total of 97 differentially expressed ARGs were detected in LIHC tissues. Functional enrichment analysis revealed these genes were mainly involved in MAP kinase activity, apoptotic signaling pathway, anoikis and PI3K-Akt signaling pathway. Afterward, the prognostic signature consisting of BSG, ETV4, EZH2, NQO1, PLK1, PBK, and SPP1 had a moderate to high predictive accuracy and served as an independent prognostic indicator for LIHC. The prognostic signature was also applicable to patients with distinct clinical parameters in subgroup survival analysis. And it could reflect the specific immune microenvironment in LIHC, which indicated high-risk group tended to profit from ICI treatment. Moreover, qRT-PCR and IHC staining showed increasing expression of BSG, ETV4, EZH2, NQO1, PLK1, PBK and SPP1in LIHC tissues, which were consistent to the results from TCGA database. The current study developed a novel prognostic signature comprising of 7 ARGs, which could stratify the risk and effectively predict the prognosis of LIHC patients. Furthermore, it also offered a potential indicator for immunotherapy of LIHC.
Collapse
Affiliation(s)
- Dongxiao Ding,
- Department of Thoracic Surgery, The People’s Hospital of Beilun District, Ningbo, Zhejiang, China
| | - Dianqian Wang,
- Health Science Center, Ningbo University, Zhejiang, China
| | - Yunsheng Qin
- Department of Hepatological Surgery, First Affiliated Hospital of Medical School of Zhejiang University, Hangzhou, Zhejiang, China
| |
Collapse
|
26
|
Hsieh C, Laguna A, Ikeda I, Maxwell AWP, Chapiro J, Nadolski G, Jiao Z, Bai HX. Using Machine Learning to Predict Response to Image-guided Therapies for Hepatocellular Carcinoma. Radiology 2023; 309:e222891. [PMID: 37934098 DOI: 10.1148/radiol.222891] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/08/2023]
Abstract
Interventional oncology is a rapidly growing field with advances in minimally invasive image-guided local-regional treatments for hepatocellular carcinoma (HCC), including transarterial chemoembolization, transarterial radioembolization, and thermal ablation. However, current standardized clinical staging systems for HCC are limited in their ability to optimize patient selection for treatment as they rely primarily on serum markers and radiologist-defined imaging features. Given the variation in treatment responses, an updated scoring system that includes multidimensional aspects of the disease, including quantitative imaging features, serum markers, and functional biomarkers, is needed to optimally triage patients. With the vast amounts of numerical medical record data and imaging features, researchers have turned to image-based methods, such as radiomics and artificial intelligence (AI), to automatically extract and process multidimensional data from images. The synthesis of these data can provide clinically relevant results to guide personalized treatment plans and optimize resource utilization. Machine learning (ML) is a branch of AI in which a model learns from training data and makes effective predictions by teaching itself. This review article outlines the basics of ML and provides a comprehensive overview of its potential value in the prediction of treatment response in patients with HCC after minimally invasive image-guided therapy.
Collapse
Affiliation(s)
- Celina Hsieh
- From the Department of Diagnostic Imaging (C.H., A.W.P.M., Z.J.) and Warren Alpert Medical School (A.L.), Brown University, Providence, RI; Department of Radiology and Biomedical Imaging, Yale University School of Medicine, New Haven, Conn (I.I., J.C.); Department of Radiology, Hospital of the University of Pennsylvania, Philadelphia, Pa (G.N.); and Department of Radiology and Radiological Sciences, Johns Hopkins University School of Medicine, 601 N Caroline St, Baltimore, MD 21205 (H.X.B.)
| | - Amanda Laguna
- From the Department of Diagnostic Imaging (C.H., A.W.P.M., Z.J.) and Warren Alpert Medical School (A.L.), Brown University, Providence, RI; Department of Radiology and Biomedical Imaging, Yale University School of Medicine, New Haven, Conn (I.I., J.C.); Department of Radiology, Hospital of the University of Pennsylvania, Philadelphia, Pa (G.N.); and Department of Radiology and Radiological Sciences, Johns Hopkins University School of Medicine, 601 N Caroline St, Baltimore, MD 21205 (H.X.B.)
| | - Ian Ikeda
- From the Department of Diagnostic Imaging (C.H., A.W.P.M., Z.J.) and Warren Alpert Medical School (A.L.), Brown University, Providence, RI; Department of Radiology and Biomedical Imaging, Yale University School of Medicine, New Haven, Conn (I.I., J.C.); Department of Radiology, Hospital of the University of Pennsylvania, Philadelphia, Pa (G.N.); and Department of Radiology and Radiological Sciences, Johns Hopkins University School of Medicine, 601 N Caroline St, Baltimore, MD 21205 (H.X.B.)
| | - Aaron W P Maxwell
- From the Department of Diagnostic Imaging (C.H., A.W.P.M., Z.J.) and Warren Alpert Medical School (A.L.), Brown University, Providence, RI; Department of Radiology and Biomedical Imaging, Yale University School of Medicine, New Haven, Conn (I.I., J.C.); Department of Radiology, Hospital of the University of Pennsylvania, Philadelphia, Pa (G.N.); and Department of Radiology and Radiological Sciences, Johns Hopkins University School of Medicine, 601 N Caroline St, Baltimore, MD 21205 (H.X.B.)
| | - Julius Chapiro
- From the Department of Diagnostic Imaging (C.H., A.W.P.M., Z.J.) and Warren Alpert Medical School (A.L.), Brown University, Providence, RI; Department of Radiology and Biomedical Imaging, Yale University School of Medicine, New Haven, Conn (I.I., J.C.); Department of Radiology, Hospital of the University of Pennsylvania, Philadelphia, Pa (G.N.); and Department of Radiology and Radiological Sciences, Johns Hopkins University School of Medicine, 601 N Caroline St, Baltimore, MD 21205 (H.X.B.)
| | - Gregory Nadolski
- From the Department of Diagnostic Imaging (C.H., A.W.P.M., Z.J.) and Warren Alpert Medical School (A.L.), Brown University, Providence, RI; Department of Radiology and Biomedical Imaging, Yale University School of Medicine, New Haven, Conn (I.I., J.C.); Department of Radiology, Hospital of the University of Pennsylvania, Philadelphia, Pa (G.N.); and Department of Radiology and Radiological Sciences, Johns Hopkins University School of Medicine, 601 N Caroline St, Baltimore, MD 21205 (H.X.B.)
| | - Zhicheng Jiao
- From the Department of Diagnostic Imaging (C.H., A.W.P.M., Z.J.) and Warren Alpert Medical School (A.L.), Brown University, Providence, RI; Department of Radiology and Biomedical Imaging, Yale University School of Medicine, New Haven, Conn (I.I., J.C.); Department of Radiology, Hospital of the University of Pennsylvania, Philadelphia, Pa (G.N.); and Department of Radiology and Radiological Sciences, Johns Hopkins University School of Medicine, 601 N Caroline St, Baltimore, MD 21205 (H.X.B.)
| | - Harrison X Bai
- From the Department of Diagnostic Imaging (C.H., A.W.P.M., Z.J.) and Warren Alpert Medical School (A.L.), Brown University, Providence, RI; Department of Radiology and Biomedical Imaging, Yale University School of Medicine, New Haven, Conn (I.I., J.C.); Department of Radiology, Hospital of the University of Pennsylvania, Philadelphia, Pa (G.N.); and Department of Radiology and Radiological Sciences, Johns Hopkins University School of Medicine, 601 N Caroline St, Baltimore, MD 21205 (H.X.B.)
| |
Collapse
|
27
|
Jia W, Shi W, Yao Q, Mao Z, Chen C, Fan AQ, Wang Y, Zhao Z, Li J, Song W. Identifying immune infiltration by deep learning to assess the prognosis of patients with hepatocellular carcinoma. J Cancer Res Clin Oncol 2023; 149:12621-12635. [PMID: 37450030 DOI: 10.1007/s00432-023-05097-z] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/24/2023] [Accepted: 06/30/2023] [Indexed: 07/18/2023]
Abstract
BACKGROUND The treatment situation for hepatocellular carcinoma remains critical. The use of deep learning algorithms to assess immune infiltration is a promising new diagnostic tool. METHODS Patient data and whole slide images (WSIs) were obtained for the Xijing Hospital (XJH) cohort and TCGA cohort. We wrote programs using Visual studio 2022 with C# language to segment the WSI into tiles. Pathologists classified the tiles and later trained deep learning models using the ResNet 101V2 network via ML.NET with the TensorFlow framework. Model performance was evaluated using AccuracyMicro versus AccuracyMacro. Model performance was examined using ROC curves versus PR curves. The percentage of immune infiltration was calculated using the R package survminer to calculate the intergroup cutoff, and the Kaplan‒Meier method was used to plot the overall survival curve of patients. Cox regression was used to determine whether the percentage of immune infiltration was an independent risk factor for prognosis. A nomogram was constructed, and its accuracy was verified using time-dependent ROC curves with calibration curves. The CIBERSORT algorithm was used to assess immune infiltration between groups. Gene Ontology was used to explore the pathways of differentially expressed genes. RESULTS There were 100 WSIs and 165,293 tiles in the training set. The final deep learning models had an AccuracyMicro of 97.46% and an AccuracyMacro of 82.28%. The AUCs of the ROC curves on both the training and validation sets exceeded 0.95. The areas under the classification PR curves exceeded 0.85, except that of the TLS on the validation set, which might have had poor results (0.713) due to too few samples. There was a significant difference in OS between the TIL classification groups (p < 0.001), while there was no significant difference in OS between the TLS groups (p = 0.294). Cox regression showed that TIL percentage was an independent risk factor for prognosis in HCC patients (p = 0.015). The AUCs according to the nomogram were 0.714, 0.690, and 0.676 for the 1-year, 2-year, and 5-year AUCs in the TCGA cohort and 0.756, 0.797, and 0.883 in the XJH cohort, respectively. There were significant differences in the levels of infiltration of seven immune cell types between the two groups of samples, and gene ontology showed that the differentially expressed genes between the groups were immune related. Their expression levels of PD-1 and CTLA4 were also significantly different. CONCLUSION We constructed and tested a deep learning model that evaluates the immune infiltration of liver cancer tissue in HCC patients. Our findings demonstrate the value of the model in assessing patient prognosis, immune infiltration and immune checkpoint expression levels.
Collapse
Affiliation(s)
- Weili Jia
- Xi'an Medical University, Xi'an, China
- Department of Hepatobiliary Surgery, Xijing Hospital, Fourth Military Medical University, Xi'an, China
| | - Wen Shi
- Xi'an Medical University, Xi'an, China
- Department of Hepatobiliary Surgery, Xijing Hospital, Fourth Military Medical University, Xi'an, China
| | | | - Zhenzhen Mao
- Xi'an Medical University, Xi'an, China
- Department of Hepatobiliary Surgery, Xijing Hospital, Fourth Military Medical University, Xi'an, China
| | - Chao Chen
- Xi'an Medical University, Xi'an, China
- Department of Hepatobiliary Surgery, Xijing Hospital, Fourth Military Medical University, Xi'an, China
| | - AQiang Fan
- Xi'an Medical University, Xi'an, China
- Department of Gastrointestinal Surgery, Xijing Hospital, Fourth Military Medical University, Xi'an, China
| | - Yanfang Wang
- Xi'an Medical University, Xi'an, China
- Department of Hepatobiliary Surgery, Xijing Hospital, Fourth Military Medical University, Xi'an, China
| | - Zihao Zhao
- Department of Hepatobiliary Surgery, Xijing Hospital, Fourth Military Medical University, Xi'an, China
| | - Jipeng Li
- Department of Gastrointestinal Surgery, Xijing Hospital, Fourth Military Medical University, Xi'an, China.
| | - Wenjie Song
- Department of Hepatobiliary Surgery, Xijing Hospital, Fourth Military Medical University, Xi'an, China.
| |
Collapse
|
28
|
Hu M, Xia X, Chen L, Jin Y, Hu Z, Xia S, Yao X. Emerging biomolecules for practical theranostics of liver hepatocellular carcinoma. Ann Hepatol 2023; 28:101137. [PMID: 37451515 DOI: 10.1016/j.aohep.2023.101137] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 04/24/2023] [Revised: 06/17/2023] [Accepted: 06/28/2023] [Indexed: 07/18/2023]
Abstract
Most cases of hepatocellular carcinoma (HCC) are able to be diagnosed through regular surveillance in an identifiable patient population with chronic hepatitis B or cirrhosis. Nevertheless, 50% of global cases might present incidentally owing to symptomatic advanced-stage HCC after worsening of liver dysfunction. A systematic search based on PUBMED was performed to identify relevant outcomes, covering newer surveillance modalities including secretory proteins, DNA methylation, miRNAs, and genome sequencing analysis which proposed molecular expression signatures as ideal tools in the early-stage HCC detection. In the face of low accuracy without harmonization on the analytical approaches and data interpretation for liquid biopsy, a more accurate incidence of HCC will be unveiled by using deep machine learning system and multiplex immunohistochemistry analysis. A combination of molecular-secretory biomarkers, high-definition imaging and bedside clinical indexes in a surveillance setting offers a comprehensive range of HCC potential indicators. In addition, the sequential use of numerous lines of systemic anti-HCC therapies will simultaneously benefit more patients in survival. This review provides an overview on the most recent developments in HCC theranostic platform.
Collapse
Affiliation(s)
- Miner Hu
- International Institutes of Medicine, The Fourth Affiliated Hospital, Zhejiang University School of Medicine, Yiwu, Zhejiang, China
| | - Xiaojun Xia
- International Institutes of Medicine, The Fourth Affiliated Hospital, Zhejiang University School of Medicine, Yiwu, Zhejiang, China
| | - Lichao Chen
- International Institutes of Medicine, The Fourth Affiliated Hospital, Zhejiang University School of Medicine, Yiwu, Zhejiang, China
| | - Yunpeng Jin
- International Institutes of Medicine, The Fourth Affiliated Hospital, Zhejiang University School of Medicine, Yiwu, Zhejiang, China
| | - Zhenhua Hu
- International Institutes of Medicine, The Fourth Affiliated Hospital, Zhejiang University School of Medicine, Yiwu, Zhejiang, China; Division of Hepatobiliary and Pancreatic Surgery, Department of Surgery, Fourth Affiliated Hospital, School of Medicine, Zhejiang University, Yiwu, Zhejiang, China.
| | - Shudong Xia
- International Institutes of Medicine, The Fourth Affiliated Hospital, Zhejiang University School of Medicine, Yiwu, Zhejiang, China.
| | - Xudong Yao
- International Institutes of Medicine, The Fourth Affiliated Hospital, Zhejiang University School of Medicine, Yiwu, Zhejiang, China.
| |
Collapse
|
29
|
Guo Q, Huang Y, Zhan X. Hepatocellular Carcinoma Subtyping and Prognostic Model Construction Based on Chemokine-Related Genes. Med Princ Pract 2023; 32:332-342. [PMID: 37848003 PMCID: PMC10727522 DOI: 10.1159/000534537] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 06/06/2023] [Accepted: 10/09/2023] [Indexed: 10/19/2023] Open
Abstract
BACKGROUND Chemokines not only regulate immune cells but also play significant roles in development and treatment of tumors and patient prognoses. However, these effects have not been fully explained in hepatocellular carcinoma (HCC). MATERIALS AND METHODS We conducted a clustering analysis of chemokine-related genes. We then examined the differences in survival rates and analyzed immune levels using single-sample Gene Set Enrichment Analysis (ssGSEA) for each subtype. Based on chemokine-related genes of different subtypes, we built a prognostic model in The Cancer Genome Atlas (TCGA) dataset using the survival package and glmnet package and validated it in the Gene Expression Omnibus (GEO) dataset. We used univariate and multivariate regression analyses to select independent prognostic factors and used R package rms to draw a nomogram reflecting patient survival rates at 1, 3, and 5 years. RESULTS We identified two chemokine subtypes and, after screening, found that Cluster1 had higher survival rates than Cluster2. In addition, in terms of immune evaluation, stromal evaluation, ESTIMATE evaluation, immune abundance, immune function, and expressions of various immune checkpoints, immune levels of Cluster1 were significantly better than those of Cluster2. The immunophenoscore (IPS) of HCC patients in Cluster1 was significantly higher than that in Cluster2. Furthermore, we established a prognostic model consisting of 9 genes, which correlated with chemokines. Through testing, Riskscore was revealed as an independent prognostic factor, and the model could effectively predict HCC patients' prognoses in both TCGA and GEO datasets. CONCLUSION This study resulted in the development of a novel prognostic model related to chemokine genes, providing new targets and theoretical support for HCC patients.
Collapse
Affiliation(s)
- Qiusheng Guo
- Department of Medical Oncology, Affiliated Jinhua Hospital, Zhejiang University School of Medicine, Jinhua, China,
| | - Yangyang Huang
- Pharmacy Department, Zhejiang Jinhua Guangfu Tumor Hospital, Jinhua, China
| | - Xiaoan Zhan
- Department of Oncology Surgery, Zhejiang Jinhua Guangfu Tumor Hospital, Jinhua, China
| |
Collapse
|
30
|
Chen Z, Li X, Zhang Y, Yang Y, Zhang Y, Zhou D, Yang Y, Zhang S, Liu Y. MRI Features for Predicting Microvascular Invasion and Postoperative Recurrence in Hepatocellular Carcinoma Without Peritumoral Hypointensity. J Hepatocell Carcinoma 2023; 10:1595-1608. [PMID: 37786565 PMCID: PMC10541533 DOI: 10.2147/jhc.s422632] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/18/2023] [Accepted: 09/08/2023] [Indexed: 10/04/2023] Open
Abstract
Purpose To identify MRI features of hepatocellular carcinoma (HCC) that predict microvascular invasion (MVI) and postoperative intrahepatic recurrence in patients without peritumoral hepatobiliary phase (HBP) hypointensity. Patients and Methods One hundred and thirty patients with HCC who underwent preoperative gadoxetate-enhanced MRI and curative hepatic resection were retrospectively reviewed. Two radiologists reviewed all preoperative MR images and assessed the radiological features of HCCs. The ability of peritumoral HBP hypointensity to identify MVI and intrahepatic recurrence was analyzed. We then assessed the MRI features of HCC that predicted the MVI and intrahepatic recurrence-free survival (RFS) in the subgroup without peritumoral HBP hypointensity. Finally, a two-step flowchart was constructed to assist in clinical decision-making. Results Peritumoral HBP hypointensity (odds ratio, 3.019; 95% confidence interval: 1.071-8.512; P=0.037) was an independent predictor of MVI. The sensitivity, specificity, positive predictive value, negative predictive value, and AUROC of peritumoral HBP hypointensity in predicting MVI were 23.80%, 91.04%, 71.23%, 55.96%, and 0.574, respectively. Intrahepatic RFS was significantly shorter in patients with peritumoral HBP hypointensity (P<0.001). In patients without peritumoral HBP hypointensity, the only significant difference between MVI-positive and MVI-negative HCCs was the presence of a radiological capsule (P=0.038). Satellite nodule was an independent risk factor for intrahepatic RFS (hazard ratio,3.324; 95% CI: 1.733-6.378; P<0.001). The high-risk HCC detection rate was significantly higher when using the two-step flowchart that incorporated peritumoral HBP hypointensity and satellite nodule than when using peritumoral HBP hypointensity alone (P<0.001). Conclusion In patients without peritumoral HBP hypointensity, a radiological capsule is useful for identifying MVI and satellite nodule is an independent risk factor for intrahepatic RFS.
Collapse
Affiliation(s)
- Zhiyuan Chen
- Department of Radiology, The Second Affiliated Hospital of Guangzhou University of Chinese Medicine, Guangzhou, Guangdong, 510120, People’s Republic of China
| | - Xiaohuan Li
- The Second Affiliated Hospital of Guangzhou University of Chinese Medicine, Guangzhou, Guangdong, 510120, People’s Republic of China
| | - Yu Zhang
- The Second Affiliated Hospital of Guangzhou University of Chinese Medicine, Guangzhou, Guangdong, 510120, People’s Republic of China
| | - Yiming Yang
- Department of Radiology, The Second Affiliated Hospital of Guangzhou University of Chinese Medicine, Guangzhou, Guangdong, 510120, People’s Republic of China
| | - Yan Zhang
- Integrated Department, the Second Affiliated Hospital of Guangzhou University of Chinese Medicine, Guangzhou, Guangdong, 510120, People’s Republic of China
| | - Dongjing Zhou
- Department of Radiology, The Second Affiliated Hospital of Guangzhou University of Chinese Medicine, Guangzhou, Guangdong, 510120, People’s Republic of China
| | - Yu Yang
- Department of Pathology, the Second Affiliated Hospital of Guangzhou University of Chinese Medicine, Guangzhou, Guangdong, 510120, People’s Republic of China
| | - Shuping Zhang
- Department of Radiology, The Second Affiliated Hospital of Guangzhou University of Chinese Medicine, Guangzhou, Guangdong, 510120, People’s Republic of China
| | - Yupin Liu
- Department of Radiology, The Second Affiliated Hospital of Guangzhou University of Chinese Medicine, Guangzhou, Guangdong, 510120, People’s Republic of China
| |
Collapse
|
31
|
Chen Y, Yang C, Sheng L, Jiang H, Song B. The Era of Immunotherapy in Hepatocellular Carcinoma: The New Mission and Challenges of Magnetic Resonance Imaging. Cancers (Basel) 2023; 15:4677. [PMID: 37835371 PMCID: PMC10572030 DOI: 10.3390/cancers15194677] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/05/2023] [Revised: 09/11/2023] [Accepted: 09/13/2023] [Indexed: 10/15/2023] Open
Abstract
In recent years, significant advancements in immunotherapy for hepatocellular carcinoma (HCC) have shown the potential to further improve the prognosis of patients with advanced HCC. However, in clinical practice, there is still a lack of effective biomarkers for identifying the patient who would benefit from immunotherapy and predicting the tumor response to immunotherapy. The immune microenvironment of HCC plays a crucial role in tumor development and drug responses. However, due to the complexity of immune microenvironment, currently, no single pathological or molecular biomarker can effectively predict tumor responses to immunotherapy. Magnetic resonance imaging (MRI) images provide rich biological information; existing studies suggest the feasibility of using MRI to assess the immune microenvironment of HCC and predict tumor responses to immunotherapy. Nevertheless, there are limitations, such as the suboptimal performance of conventional MRI sequences, incomplete feature extraction in previous deep learning methods, and limited interpretability. Further study needs to combine qualitative features, quantitative parameters, multi-omics characteristics related to the HCC immune microenvironment, and various deep learning techniques in multi-center research cohorts. Subsequently, efforts should also be undertaken to construct and validate a visual predictive tool of tumor response, and assess its predictive value for patient survival benefits. Additionally, future research endeavors must aim to provide an accurate, efficient, non-invasive, and highly interpretable method for predicting the effectiveness of immune therapy.
Collapse
Affiliation(s)
- Yidi Chen
- Department of Radiology, West China Hospital, Sichuan University, Chengdu 610064, China; (Y.C.); (C.Y.); (L.S.)
| | - Chongtu Yang
- Department of Radiology, West China Hospital, Sichuan University, Chengdu 610064, China; (Y.C.); (C.Y.); (L.S.)
| | - Liuji Sheng
- Department of Radiology, West China Hospital, Sichuan University, Chengdu 610064, China; (Y.C.); (C.Y.); (L.S.)
| | - Hanyu Jiang
- Department of Radiology, West China Hospital, Sichuan University, Chengdu 610064, China; (Y.C.); (C.Y.); (L.S.)
| | - Bin Song
- Department of Radiology, West China Hospital, Sichuan University, Chengdu 610064, China; (Y.C.); (C.Y.); (L.S.)
- Department of Radiology, Sanya People’s Hospital, Sanya 572000, China
| |
Collapse
|
32
|
Choi JH, Thung SN. Advances in Histological and Molecular Classification of Hepatocellular Carcinoma. Biomedicines 2023; 11:2582. [PMID: 37761023 PMCID: PMC10526317 DOI: 10.3390/biomedicines11092582] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/13/2023] [Revised: 09/06/2023] [Accepted: 09/08/2023] [Indexed: 09/29/2023] Open
Abstract
Hepatocellular carcinoma (HCC) is a primary liver cancer characterized by hepatocellular differentiation. HCC is molecularly heterogeneous with a wide spectrum of histopathology. The prognosis of patients with HCC is generally poor, especially in those with advanced stages. HCC remains a diagnostic challenge for pathologists because of its morphological and phenotypic diversity. However, recent advances have enhanced our understanding of the molecular genetics and histological subtypes of HCC. Accurate diagnosis of HCC is important for patient management and prognosis. This review provides an update on HCC pathology, focusing on molecular genetics, histological subtypes, and diagnostic approaches.
Collapse
Affiliation(s)
- Joon Hyuk Choi
- Department of Pathology, Yeungnam University College of Medicine, Daegu 42415, Republic of Korea
| | - Swan N. Thung
- Department of Pathology, Molecular and Cell-Based Medicine, Icahn School of Medicine at Mount Sinai, 1468 Madison Avenue, New York, NY 10029, USA;
| |
Collapse
|
33
|
He Z, Shen X, Wang B, Xu L, Ling Q. CT radiomics for noninvasively predicting NQO1 expression levels in hepatocellular carcinoma. PLoS One 2023; 18:e0290900. [PMID: 37695786 PMCID: PMC10495018 DOI: 10.1371/journal.pone.0290900] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/18/2023] [Accepted: 08/18/2023] [Indexed: 09/13/2023] Open
Abstract
Using noninvasive radiomics to predict pathological biomarkers is an innovative work worthy of exploration. This retrospective cohort study aimed to analyze the correlation between NAD(P)H quinone oxidoreductase 1 (NQO1) expression levels and the prognosis of patients with hepatocellular carcinoma (HCC) and to construct radiomic models to predict the expression levels of NQO1 prior to surgery. Data of patients with HCC from The Cancer Genome Atlas (TCGA) and the corresponding arterial phase-enhanced CT images from The Cancer Imaging Archive were obtained for prognosis analysis, radiomic feature extraction, and model development. In total, 286 patients with HCC from TCGA were included. According to the cut-off value calculated using R, patients were divided into high-expression (n = 143) and low-expression groups (n = 143). Kaplan-Meier survival analysis showed that higher NQO1 expression levels were significantly associated with worse prognosis in patients with HCC (p = 0.017). Further multivariate analysis confirmed that high NQO1 expression was an independent risk factor for poor prognosis (HR = 1.761, 95% CI: 1.136-2.73, p = 0.011). Based on the arterial phase-enhanced CT images, six radiomic features were extracted, and a new bi-regional radiomics model was established, which could noninvasively predict higher NQO1 expression with good performance. The area under the curve (AUC) was 0.9079 (95% CI 0.8127-1.0000). The accuracy, sensitivity, and specificity were 0.86, 0.88, and 0.84, respectively, with a threshold value of 0.404. The data verification of our center showed that this model has good predictive efficiency, with an AUC of 0.8791 (95% CI 0.6979-1.0000). In conclusion, there existed a significant correlation between the CT image features and the expression level of NQO1, which could indirectly reflect the prognosis of patients with HCC. The predictive model based on arterial phase CT imaging features has good stability and diagnostic efficiency and is a potential means of identifying the expression level of NQO1 in HCC tissues before surgery.
Collapse
Affiliation(s)
- Zenglei He
- Department of Hepatobiliary and Pancreatic Surgery, The First Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, Zhejiang, PR China
| | - Xiaoyong Shen
- Department of Radiology, The First Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, Zhejiang, PR China
| | - Bin Wang
- Department of Hepatobiliary and Pancreatic Surgery, The First Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, Zhejiang, PR China
| | - Li Xu
- Department of Hepatobiliary and Pancreatic Surgery, The First Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, Zhejiang, PR China
| | - Qi Ling
- Department of Hepatobiliary and Pancreatic Surgery, The First Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, Zhejiang, PR China
| |
Collapse
|
34
|
Kiran N, Sapna F, Kiran F, Kumar D, Raja F, Shiwlani S, Paladini A, Sonam F, Bendari A, Perkash RS, Anjali F, Varrassi G. Digital Pathology: Transforming Diagnosis in the Digital Age. Cureus 2023; 15:e44620. [PMID: 37799211 PMCID: PMC10547926 DOI: 10.7759/cureus.44620] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/18/2023] [Accepted: 09/03/2023] [Indexed: 10/07/2023] Open
Abstract
In the context of rapid technological advancements, the narrative review titled "Digital Pathology: Transforming Diagnosis in the Digital Age" explores the significant impact of digital pathology in reshaping diagnostic approaches. This review delves into the various effects of the field, including remote consultations and artificial intelligence (AI)-assisted analysis, revealing the ongoing transformation taking place. The investigation explores the process of digitizing traditional glass slides, which aims to improve accessibility and facilitate sharing. Additionally, it addresses the complexities associated with data security and standardization challenges. Incorporating AI enhances pathologists' diagnostic capabilities and accelerates analytical procedures. Furthermore, the review highlights the growing importance of collaborative networks facilitating global knowledge sharing. It also emphasizes the significant impact of this technology on medical education and patient care. This narrative review aims to provide an overview of digital pathology's transformative and innovative potential, highlighting its disruptive nature in reshaping diagnostic practices.
Collapse
Affiliation(s)
- Nfn Kiran
- Pathology and Laboratory Medicine, Staten Island University Hospital, New York, USA
| | - Fnu Sapna
- Pathology and Laboratory Medicine, Albert Einstein College of Medicine, New York, USA
| | - Fnu Kiran
- Pathology and Laboratory Medicine, University of Missouri School of Medicine, Columbia, USA
| | - Deepak Kumar
- Pathology and Laboratory Medicine, University of Missouri, Columbia, USA
| | - Fnu Raja
- Pathology and Laboratory Medicine, MetroHealth Medical Center, Cleveland, USA
| | - Sheena Shiwlani
- Pathology and Laboratory Medicine, Isra University, Karachi, PAK
- Pathology, Mount Sinai Hospital, New York, USA
| | - Antonella Paladini
- Clinical Medicine, Public Health and Life Science (MESVA), University of L'Aquila, L'Aquila, ITA
| | - Fnu Sonam
- Pathology and Laboratory Medicine, Liaquat University of Medical and Health Sciences, Sukkur, PAK
- Medicine, Mustafai Trust Central Hospital, Sukkur, PAK
| | - Ahmed Bendari
- Pathology and Laboratory Medicine, Lenox Hill Hospital, New York, USA
| | | | - Fnu Anjali
- Internal Medicine, Sakhi Baba General Hospital, Sukkur, PAK
| | | |
Collapse
|
35
|
Zhang Z, Mao M, Wang F, Zhang Y, Shi J, Chang L, Wu X, Zhang Z, Xu P, Lu S. Comprehensive analysis and immune landscape of chemokines- and chemokine receptors-based signature in hepatocellular carcinoma. Front Immunol 2023; 14:1164669. [PMID: 37545521 PMCID: PMC10399597 DOI: 10.3389/fimmu.2023.1164669] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/13/2023] [Accepted: 07/04/2023] [Indexed: 08/08/2023] Open
Abstract
Background Despite encouraging results from immunotherapy combined with targeted therapy for hepatocellular carcinoma (HCC), the prognosis remains poor. Chemokines and their receptors are an essential component in the development of HCC, but their significance in HCC have not yet been fully elucidated. We aimed to establish chemokine-related prognostic signature and investigate the association between the genes and tumor immune microenvironment (TIME). Methods 342 HCC patients have screened from the TCGA cohort. A prognostic signature was developed using least absolute shrinkage and selection operator regression and Cox proportional risk regression analysis. External validation was performed using the LIHC-JP cohort deployed from the ICGC database. Single-cell RNA sequencing (scRNA-seq) data from the GEO database. Two nomograms were developed to estimate the outcome of HCC patients. RT-qPCR was used to validate the differences in the expression of genes contained in the signature. Results The prognostic signature containing two chemokines-(CCL14, CCL20) and one chemokine receptor-(CCR3) was successfully established. The HCC patients were stratified into high- and low-risk groups according to their median risk scores. We found that patients in the low-risk group had better outcomes than those in the high-risk group. The results of univariate and multivariate Cox regression analyses suggested that this prognostic signature could be considered an independent risk factor for the outcome of HCC patients. We discovered significant differences in the infiltration of various immune cell subtypes, tumor mutation burden, biological pathways, the expression of immune activation or suppression genes, and the sensitivity of different groups to chemotherapy agents and small molecule-targeted drugs in the high- and low-risk groups. Subsequently, single-cell analysis results showed that the higher expression of CCL20 was associated with HCC metastasis. The RT-qPCR results demonstrated remarkable discrepancies in the expression of CCL14, CCL20, and CCR3 between HCC and its paired adjacent non-tumor tissues. Conclusion In this study, a novel prognostic biomarker explored in depth the association between the prognostic model and TIME was developed and verified. These results may be applied in the future to improve the efficacy of immunotherapy or targeted therapy for HCC.
Collapse
Affiliation(s)
- Ze Zhang
- Medical School of Chinese People’s Liberation Army (PLA), Beijing, China
- Faculty of Hepato-Pancreato-Biliary Surgery, Chinese PLA General Hospital, Beijing, China
- Institute of Hepatobiliary Surgery of Chinese PLA, Beijing, China
- Key Laboratory of Digital Hepatobiliary Surgery, PLA, Beijing, China
| | - Mingsong Mao
- School of Basic Medical Sciences, Anhui Medical University, Hefei, China
| | - Fangzhou Wang
- Medical School of Chinese People’s Liberation Army (PLA), Beijing, China
- Faculty of Hepato-Pancreato-Biliary Surgery, Chinese PLA General Hospital, Beijing, China
- Institute of Hepatobiliary Surgery of Chinese PLA, Beijing, China
- Key Laboratory of Digital Hepatobiliary Surgery, PLA, Beijing, China
| | - Yao Zhang
- State Key Laboratory of Proteomics, National Center for Protein Sciences (Beijing), Research Unit of Proteomics and Research and Development of New Drug of Chinese Academy of Medical Sciences, Beijing Proteome Research Center, Institute of Lifeomics, Beijing, China
| | - Jihang Shi
- Medical School of Chinese People’s Liberation Army (PLA), Beijing, China
- Faculty of Hepato-Pancreato-Biliary Surgery, Chinese PLA General Hospital, Beijing, China
- Institute of Hepatobiliary Surgery of Chinese PLA, Beijing, China
- Key Laboratory of Digital Hepatobiliary Surgery, PLA, Beijing, China
| | - Lei Chang
- State Key Laboratory of Proteomics, National Center for Protein Sciences (Beijing), Research Unit of Proteomics and Research and Development of New Drug of Chinese Academy of Medical Sciences, Beijing Proteome Research Center, Institute of Lifeomics, Beijing, China
| | - Xiaolin Wu
- School of Medicine, Guizhou University, Guiyang, Guizhou, China
| | - Zhenpeng Zhang
- State Key Laboratory of Proteomics, National Center for Protein Sciences (Beijing), Research Unit of Proteomics and Research and Development of New Drug of Chinese Academy of Medical Sciences, Beijing Proteome Research Center, Institute of Lifeomics, Beijing, China
| | - Ping Xu
- School of Basic Medical Sciences, Anhui Medical University, Hefei, China
- State Key Laboratory of Proteomics, National Center for Protein Sciences (Beijing), Research Unit of Proteomics and Research and Development of New Drug of Chinese Academy of Medical Sciences, Beijing Proteome Research Center, Institute of Lifeomics, Beijing, China
- School of Medicine, Guizhou University, Guiyang, Guizhou, China
| | - Shichun Lu
- Faculty of Hepato-Pancreato-Biliary Surgery, Chinese PLA General Hospital, Beijing, China
- Institute of Hepatobiliary Surgery of Chinese PLA, Beijing, China
- Key Laboratory of Digital Hepatobiliary Surgery, PLA, Beijing, China
| |
Collapse
|
36
|
Schattenberg JM, Chalasani N, Alkhouri N. Artificial Intelligence Applications in Hepatology. Clin Gastroenterol Hepatol 2023; 21:2015-2025. [PMID: 37088460 DOI: 10.1016/j.cgh.2023.04.007] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 10/09/2022] [Revised: 03/16/2023] [Accepted: 04/14/2023] [Indexed: 04/25/2023]
Abstract
Over the past 2 decades, the field of hepatology has witnessed major developments in diagnostic tools, prognostic models, and treatment options making it one of the most complex medical subspecialties. Through artificial intelligence (AI) and machine learning, computers are now able to learn from complex and diverse clinical datasets to solve real-world medical problems with performance that surpasses that of physicians in certain areas. AI algorithms are currently being implemented in liver imaging, interpretation of liver histopathology, noninvasive tests, prediction models, and more. In this review, we provide a summary of the state of AI in hepatology and discuss current challenges for large-scale implementation including some ethical aspects. We emphasize to the readers that most AI-based algorithms that are discussed in this review are still considered in early development and their utility and impact on patient outcomes still need to be assessed in future large-scale and inclusive studies. Our vision is that the use of AI in hepatology will enhance physician performance, decrease the burden and time spent on documentation, and reestablish the personalized patient-physician relationship that is of utmost importance for obtaining good outcomes.
Collapse
Affiliation(s)
- Jörn M Schattenberg
- Metabolic Liver Research Program, I. Department of Medicine, University Medical Center Mainz, Mainz, Germany
| | - Naga Chalasani
- Indiana University School of Medicine and Indiana University Health, Indianapolis, Indiana
| | - Naim Alkhouri
- Arizona Liver Health and University of Arizona, Tucson, Arizona.
| |
Collapse
|
37
|
Liang Y, Wang Z, Peng Y, Dai Z, Lai C, Qiu Y, Yao Y, Shi Y, Shang J, Huang X. Development of ensemble learning models for prognosis of hepatocellular carcinoma patients underwent postoperative adjuvant transarterial chemoembolization. Front Oncol 2023; 13:1169102. [PMID: 37305570 PMCID: PMC10254793 DOI: 10.3389/fonc.2023.1169102] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/18/2023] [Accepted: 05/09/2023] [Indexed: 06/13/2023] Open
Abstract
Background Postoperative adjuvant transarterial chemoembolization (PA-TACE) has been increasing widely used to improve the prognosis of hepatocellular carcinoma (HCC) patients. However, clinical outcomes vary from patient to patient, which calls for individualized prognostic prediction and early management. Methods A total of 274 HCC patients who underwent PA-TACE were enrolled in this study. The prediction performance of five machine learning models was compared and the prognostic variables of postoperative outcomes were identified. Results Compared with other machine learning models, the risk prediction model based on ensemble learning strategies, including Boosting, Bagging, and Stacking algorithms, presented better prediction performance for overall mortality and HCC recurrence. Moreover, the results showed that the Stacking algorithm had relatively low time consumption, good discriminative ability, and the best prediction performance. In addition, according to time-dependent ROC analysis, the ensemble learning strategies were found to perform well in predicting both OS and RFS for the patients. Our study also found that BCLC Stage, hsCRP/ALB and frequency of PA-TACE were relatively important variables in both overall mortality and recurrence, while MVI contributed more to the recurrence of the patients. Conclusion Among the five machine learning models, the ensemble learning strategies, especially the Stacking algorithm, could better predict the prognosis of HCC patients following PA-TACE. Machine learning models could also help clinicians identify the important prognostic factors that are clinically useful in individualized patient monitoring and management.
Collapse
Affiliation(s)
- Yuxin Liang
- Liver Transplantation Center and Hepatobiliary and Pancreatic Surgery, Sichuan Cancer Hospital and Institute, Sichuan Cancer Center, School of Medicine, University of Electronic Science and Technology of China, Chengdu, China
- Department of Hepatobiliary-Pancreatic Surgery, Cell Transplantation Center, Sichuan Provincial People’s Hospital, University of Electronic Science and Technology of China, Chengdu, China
| | - Zirui Wang
- School of Computer Science and Engineering, University of Electronic Science and Technology of China, Chengdu, China
| | - Yujiao Peng
- Liver Transplantation Center and Hepatobiliary and Pancreatic Surgery, Sichuan Cancer Hospital and Institute, Sichuan Cancer Center, School of Medicine, University of Electronic Science and Technology of China, Chengdu, China
| | - Zonglin Dai
- Liver Transplantation Center and Hepatobiliary and Pancreatic Surgery, Sichuan Cancer Hospital and Institute, Sichuan Cancer Center, School of Medicine, University of Electronic Science and Technology of China, Chengdu, China
- Department of Hepatobiliary-Pancreatic Surgery, Cell Transplantation Center, Sichuan Provincial People’s Hospital, University of Electronic Science and Technology of China, Chengdu, China
| | - Chunyou Lai
- Liver Transplantation Center and Hepatobiliary and Pancreatic Surgery, Sichuan Cancer Hospital and Institute, Sichuan Cancer Center, School of Medicine, University of Electronic Science and Technology of China, Chengdu, China
- Department of Hepatobiliary-Pancreatic Surgery, Cell Transplantation Center, Sichuan Provincial People’s Hospital, University of Electronic Science and Technology of China, Chengdu, China
| | - Yuqin Qiu
- Liver Transplantation Center and Hepatobiliary and Pancreatic Surgery, Sichuan Cancer Hospital and Institute, Sichuan Cancer Center, School of Medicine, University of Electronic Science and Technology of China, Chengdu, China
| | - Yutong Yao
- Liver Transplantation Center and Hepatobiliary and Pancreatic Surgery, Sichuan Cancer Hospital and Institute, Sichuan Cancer Center, School of Medicine, University of Electronic Science and Technology of China, Chengdu, China
- Department of Hepatobiliary-Pancreatic Surgery, Cell Transplantation Center, Sichuan Provincial People’s Hospital, University of Electronic Science and Technology of China, Chengdu, China
| | - Ying Shi
- Liver Transplantation Center and Hepatobiliary and Pancreatic Surgery, Sichuan Cancer Hospital and Institute, Sichuan Cancer Center, School of Medicine, University of Electronic Science and Technology of China, Chengdu, China
- Department of Hepatobiliary-Pancreatic Surgery, Cell Transplantation Center, Sichuan Provincial People’s Hospital, University of Electronic Science and Technology of China, Chengdu, China
| | - Jin Shang
- Liver Transplantation Center and Hepatobiliary and Pancreatic Surgery, Sichuan Cancer Hospital and Institute, Sichuan Cancer Center, School of Medicine, University of Electronic Science and Technology of China, Chengdu, China
- Department of Hepatobiliary-Pancreatic Surgery, Cell Transplantation Center, Sichuan Provincial People’s Hospital, University of Electronic Science and Technology of China, Chengdu, China
| | - Xiaolun Huang
- Liver Transplantation Center and Hepatobiliary and Pancreatic Surgery, Sichuan Cancer Hospital and Institute, Sichuan Cancer Center, School of Medicine, University of Electronic Science and Technology of China, Chengdu, China
- Department of Hepatobiliary-Pancreatic Surgery, Cell Transplantation Center, Sichuan Provincial People’s Hospital, University of Electronic Science and Technology of China, Chengdu, China
| |
Collapse
|
38
|
Mansur A, Vrionis A, Charles JP, Hancel K, Panagides JC, Moloudi F, Iqbal S, Daye D. The Role of Artificial Intelligence in the Detection and Implementation of Biomarkers for Hepatocellular Carcinoma: Outlook and Opportunities. Cancers (Basel) 2023; 15:cancers15112928. [PMID: 37296890 DOI: 10.3390/cancers15112928] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/15/2023] [Revised: 05/23/2023] [Accepted: 05/24/2023] [Indexed: 06/12/2023] Open
Abstract
Liver cancer is a leading cause of cancer-related death worldwide, and its early detection and treatment are crucial for improving morbidity and mortality. Biomarkers have the potential to facilitate the early diagnosis and management of liver cancer, but identifying and implementing effective biomarkers remains a major challenge. In recent years, artificial intelligence has emerged as a promising tool in the cancer sphere, and recent literature suggests that it is very promising in facilitating biomarker use in liver cancer. This review provides an overview of the status of AI-based biomarker research in liver cancer, with a focus on the detection and implementation of biomarkers for risk prediction, diagnosis, staging, prognostication, prediction of treatment response, and recurrence of liver cancers.
Collapse
Affiliation(s)
| | - Andrea Vrionis
- Morsani College of Medicine, University of South Florida Health, Tampa, FL 33602, USA
| | - Jonathan P Charles
- Morsani College of Medicine, University of South Florida Health, Tampa, FL 33602, USA
| | - Kayesha Hancel
- Department of Radiology, Massachusetts General Hospital, Boston, MA 02114, USA
| | | | - Farzad Moloudi
- Department of Radiology, Massachusetts General Hospital, Boston, MA 02114, USA
| | - Shams Iqbal
- Department of Radiology, Massachusetts General Hospital, Boston, MA 02114, USA
| | - Dania Daye
- Department of Radiology, Massachusetts General Hospital, Boston, MA 02114, USA
| |
Collapse
|
39
|
Li J, Li Y, Li F, Xu L. NK cell marker gene-based model shows good predictive ability in prognosis and response to immunotherapies in hepatocellular carcinoma. Sci Rep 2023; 13:7294. [PMID: 37147523 PMCID: PMC10163253 DOI: 10.1038/s41598-023-34602-0] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/04/2022] [Accepted: 05/04/2023] [Indexed: 05/07/2023] Open
Abstract
Hepatocellular carcinoma (HCC) is the fourth leading cause of malignancy worldwide, and its progression is influenced by the immune microenvironment. Natural killer (NK) cells are essential in the anti-tumor response and have been linked to immunotherapies for cancers. Therefore, it is important to unify and validate the role of NK cell-related gene signatures in HCC. In this study, we used RNA-seq analysis on HCC samples from public databases. We applied the ConsensusClusterPlus tool to construct the consensus matrix and cluster the samples based on their NK cell-related expression profile data. We employed the least absolute shrinkage and selection operator regression analysis to identify the hub genes. Additionally, we utilized the CIBERSORT and ESTIMATE web-based methods to perform immune-related evaluations. Our results showed that the NK cell-related gene-based classification divided HCC patients into three clusters. The C3 cluster was activated in immune activation signaling pathways and showed better prognosis and good clinical features. In contrast, the C1 cluster was remarkably enriched in cell cycle pathways. The stromal score, immune score, and ESTIMATE score in C3 were much higher than those in C2 and C1. Furthermore, we identified six hub genes: CDC20, HMOX1, S100A9, CFHR3, PCN1, and GZMA. The NK cell-related genes-based risk score subgroups demonstrated that a higher risk score subgroup showed poorer prognosis. In summary, our findings suggest that NK cell-related genes play an essential role in HCC prognosis prediction and have therapeutic potential in promoting NK cell antitumor immunity. The six identified hub genes may serve as useful biomarkers for novel therapeutic targets.
Collapse
Affiliation(s)
- Juan Li
- Department of Infectious Diseases, The First Affiliated Hospital of Zhengzhou University, No. 1 Jianshedong Road, Erqi District, Zhengzhou, 450052, China.
| | - Yi Li
- Department of Infectious Diseases, The First Affiliated Hospital of Zhengzhou University, No. 1 Jianshedong Road, Erqi District, Zhengzhou, 450052, China
| | - Fulei Li
- Department of Infectious Diseases, The First Affiliated Hospital of Zhengzhou University, No. 1 Jianshedong Road, Erqi District, Zhengzhou, 450052, China
| | - Lixia Xu
- Department of Infectious Diseases, The First Affiliated Hospital of Zhengzhou University, No. 1 Jianshedong Road, Erqi District, Zhengzhou, 450052, China
| |
Collapse
|
40
|
Ma S, Liu J, Li W, Liu Y, Hui X, Qu P, Jiang Z, Li J, Wang J. Machine learning in TCM with natural products and molecules: current status and future perspectives. Chin Med 2023; 18:43. [PMID: 37076902 PMCID: PMC10116715 DOI: 10.1186/s13020-023-00741-9] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/30/2023] [Accepted: 03/28/2023] [Indexed: 04/21/2023] Open
Abstract
Traditional Chinese medicine (TCM) has been practiced for thousands of years with clinical efficacy. Natural products and their effective agents such as artemisinin and paclitaxel have saved millions of lives worldwide. Artificial intelligence is being increasingly deployed in TCM. By summarizing the principles and processes of deep learning and traditional machine learning algorithms, analyzing the application of machine learning in TCM, reviewing the results of previous studies, this study proposed a promising future perspective based on the combination of machine learning, TCM theory, chemical compositions of natural products, and computational simulations based on molecules and chemical compositions. In the first place, machine learning will be utilized in the effective chemical components of natural products to target the pathological molecules of the disease which could achieve the purpose of screening the natural products on the basis of the pathological mechanisms they target. In this approach, computational simulations will be used for processing the data for effective chemical components, generating datasets for analyzing features. In the next step, machine learning will be used to analyze the datasets on the basis of TCM theories such as the superposition of syndrome elements. Finally, interdisciplinary natural product-syndrome research will be established by unifying the results of the two steps outlined above, potentially realizing an intelligent artificial intelligence diagnosis and treatment model based on the effective chemical components of natural products under the guidance of TCM theory. This perspective outlines an innovative application of machine learning in the clinical practice of TCM based on the investigation of chemical molecules under the guidance of TCM theory.
Collapse
Affiliation(s)
- Suya Ma
- Guang'anmen Hospital, China Academy of Chinese Medicine Sciences, Beijing, 100053, China
| | - Jinlei Liu
- Guang'anmen Hospital, China Academy of Chinese Medicine Sciences, Beijing, 100053, China
| | - Wenhua Li
- Tianjin University of Traditional Chinese Medicine, Tianjin, 301617, China
| | - Yongmei Liu
- Guang'anmen Hospital, China Academy of Chinese Medicine Sciences, Beijing, 100053, China
| | - Xiaoshan Hui
- Guang'anmen Hospital, China Academy of Chinese Medicine Sciences, Beijing, 100053, China
| | - Peirong Qu
- Guang'anmen Hospital, China Academy of Chinese Medicine Sciences, Beijing, 100053, China
| | - Zhilin Jiang
- Guang'anmen Hospital, China Academy of Chinese Medicine Sciences, Beijing, 100053, China
| | - Jun Li
- Guang'anmen Hospital, China Academy of Chinese Medicine Sciences, Beijing, 100053, China.
| | - Jie Wang
- Guang'anmen Hospital, China Academy of Chinese Medicine Sciences, Beijing, 100053, China.
| |
Collapse
|
41
|
Sun J, Fu L, Zhang W, Li D, Zhang M, Xu Z, Bai H, Ding P. Convolutional neural network models for automatic diagnosis and graduation in skin frostbite. Int Wound J 2023; 20:910-916. [PMID: 36054618 PMCID: PMC10031220 DOI: 10.1111/iwj.13937] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/18/2022] [Accepted: 08/09/2022] [Indexed: 10/14/2022] Open
Abstract
The study aimed to develop and validate a convolutional neural network (CNN)-based deep learning method for automatic diagnosis and graduation of skin frostbite. A dataset of 71 annotated images was used for the training, the validation, and the testing based on ResNet-50 model. The performances were evaluated with the test set. The diagnosis and graduation performance of our approach was compared with two residents from burns department. The approach correctly identified all the frostbite of IV (18/18, 100%), but with respectively 1 mistake in the diagnosis of degree I (29/30, 96.67%), II (28/29, 96.55%) and III (37/38, 97.37%). The accuracy of the approach on the whole test set was 97.39% (112/115). The accuracy of the two residents were respectively 77.39% and 73.04%. Weighted Kappa of 0.583 indicates good reliability between the two residents (P = .445). Kendall's coefficient of concordance is 0.326 (P = .548), indicating differences in accuracy between the approach and the two residents. Our approach based on CNNs demonstrated an encouraging performance for the automatic diagnosis and graduation of skin frostbite, with higher accuracy and efficiency.
Collapse
Affiliation(s)
- Jiachen Sun
- Department of Burns and Plastic SurgeryThe Fourth Medical Center of Chinese PLA General HospitalBeijingChina
| | - Lin Fu
- Plastic Surgery Hospital of Chinese Academy of Medical SciencesPeking Union Medical CollegeBeijingChina
| | - Wen Zhang
- Department of Burns and Plastic SurgeryThe Fourth Medical Center of Chinese PLA General HospitalBeijingChina
| | - Dongjie Li
- Department of Burns and Plastic SurgeryThe Fourth Medical Center of Chinese PLA General HospitalBeijingChina
| | - Ming Zhang
- Department of Burns and Plastic SurgeryThe Fourth Medical Center of Chinese PLA General HospitalBeijingChina
| | - Zineng Xu
- R&D DepartmentDeepcare Inc.BeijingChina
| | | | - Peng Ding
- R&D DepartmentDeepcare Inc.BeijingChina
| |
Collapse
|
42
|
Zhang Y, Zhang Y, He P, Ge F, Huo Z, Qiao G. The genetic liability to rheumatoid arthritis may decrease hepatocellular carcinoma risk in East Asian population: a Mendelian randomization study. Arthritis Res Ther 2023; 25:49. [PMID: 36973792 PMCID: PMC10041783 DOI: 10.1186/s13075-023-03029-3] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/16/2023] [Accepted: 03/10/2023] [Indexed: 03/29/2023] Open
Abstract
BACKGROUND Patients with rheumatoid arthritis (RA) have a rising possibility of acquiring certain kinds of cancers than the general public. The causal risk association between RA and hepatocellular carcinoma (HCC) remains unknown. METHODS Genetic summary data from genome-wide association study (GWAS), including RA (n = 19,190) and HCC (n = 197,611), was analyzed. The inverse-variance weighted (IVW) approach was used as the principal analysis, complemented with weighted median, weighted mode, simple median method, and MR-Egger analyses. The genetic data of RA (n = 212,453) was used to verify the results in eastern Asia populations. RESULTS The results from the IVW methods indicated that genetically predicted RA was significantly linked with a declined possibility of HCC for East Asians (OR = 0.86; 95% CI: 0.78, 0.95; p = 0.003). The weighted median and the weighted mode also supported similar results (all p < 0.05). Additionally, neither the funnel plots nor the MR-Egger intercepts revealed any directional pleiotropic effects between RA and HCC. Moreover, the other set of RA data validated the results. CONCLUSION The RA may decrease the risk of being susceptible to the HCC in eastern Asia populations, which was beyond expectation. In the future, additional investigations should be made into potential biomedical mechanisms.
Collapse
Affiliation(s)
- Yuzhuo Zhang
- Guangzhou Medical University, Guangzhou, 511436, Guangdong, China
| | - Yudong Zhang
- Department of Thoracic Surgery & Department of Cardiothoracic Surgery of East Division, The First Affiliated Hospital, Sun Yat-Sen University, Guangzhou, 510062, Guangdong, China
| | - Peng He
- Guangzhou Medical University, Guangzhou, 511436, Guangdong, China
| | - Fan Ge
- Guangzhou Medical University, Guangzhou, 511436, Guangdong, China.
- National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, 100021, China.
| | - Zhenyu Huo
- Guangzhou Medical University, Guangzhou, 511436, Guangdong, China.
- National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, 100021, China.
| | - Guibin Qiao
- Department of Thoracic Surgery, Guangdong Provincial People's Hospital, Guangdong Academy of Medical Sciences, Guangzhou, 510080, Guangdong, China.
- Shantou University Medical College, Shantou, China.
- The Second School of Clinical Medicine, Southern Medical University, Guangzhou, 510515, China.
| |
Collapse
|
43
|
Bakrania A, Joshi N, Zhao X, Zheng G, Bhat M. Artificial intelligence in liver cancers: Decoding the impact of machine learning models in clinical diagnosis of primary liver cancers and liver cancer metastases. Pharmacol Res 2023; 189:106706. [PMID: 36813095 DOI: 10.1016/j.phrs.2023.106706] [Citation(s) in RCA: 9] [Impact Index Per Article: 9.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 01/14/2023] [Revised: 02/17/2023] [Accepted: 02/19/2023] [Indexed: 02/22/2023]
Abstract
Liver cancers are the fourth leading cause of cancer-related mortality worldwide. In the past decade, breakthroughs in the field of artificial intelligence (AI) have inspired development of algorithms in the cancer setting. A growing body of recent studies have evaluated machine learning (ML) and deep learning (DL) algorithms for pre-screening, diagnosis and management of liver cancer patients through diagnostic image analysis, biomarker discovery and predicting personalized clinical outcomes. Despite the promise of these early AI tools, there is a significant need to explain the 'black box' of AI and work towards deployment to enable ultimate clinical translatability. Certain emerging fields such as RNA nanomedicine for targeted liver cancer therapy may also benefit from application of AI, specifically in nano-formulation research and development given that they are still largely reliant on lengthy trial-and-error experiments. In this paper, we put forward the current landscape of AI in liver cancers along with the challenges of AI in liver cancer diagnosis and management. Finally, we have discussed the future perspectives of AI application in liver cancer and how a multidisciplinary approach using AI in nanomedicine could accelerate the transition of personalized liver cancer medicine from bench side to the clinic.
Collapse
Affiliation(s)
- Anita Bakrania
- Toronto General Hospital Research Institute, Toronto, ON, Canada; Ajmera Transplant Program, University Health Network, Toronto, ON, Canada; Princess Margaret Cancer Centre, University Health Network, Toronto, ON, Canada.
| | | | - Xun Zhao
- Toronto General Hospital Research Institute, Toronto, ON, Canada; Ajmera Transplant Program, University Health Network, Toronto, ON, Canada
| | - Gang Zheng
- Princess Margaret Cancer Centre, University Health Network, Toronto, ON, Canada; Institute of Biomedical Engineering, University of Toronto, Toronto, ON, Canada; Department of Medical Biophysics, University of Toronto, Toronto, ON, Canada
| | - Mamatha Bhat
- Toronto General Hospital Research Institute, Toronto, ON, Canada; Ajmera Transplant Program, University Health Network, Toronto, ON, Canada; Division of Gastroenterology, Department of Medicine, University Health Network and University of Toronto, Toronto, ON, Canada; Department of Medical Sciences, Toronto, ON, Canada.
| |
Collapse
|
44
|
Xiao C, Liu S, Ge G, Jiang H, Wang L, Chen Q, Jin C, Mo J, Li J, Wang K, Zhang Q, Zhou J. Roles of hypoxia-inducible factor in hepatocellular carcinoma under local ablation therapies. Front Pharmacol 2023; 14:1086813. [PMID: 36814489 PMCID: PMC9939531 DOI: 10.3389/fphar.2023.1086813] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/01/2022] [Accepted: 01/18/2023] [Indexed: 02/09/2023] Open
Abstract
Hepatocellular carcinoma (HCC) is one of the most common digestive malignancies. HCC It ranges as the fifth most common cause of cancer mortality worldwide. While The prognosis of metastatic or advanced HCC is still quite poor. Recently, locoregional treatment, especially local ablation therapies, plays an important role in the treatment of HCC. Radiofrequency ablation (RFA) and high-intensity focused ultrasound (HIFU) ablation are the most common-used methods effective and feasible for treating HCC. However, the molecular mechanisms underlying the actions of ablation in the treatments for HCC and the HCC recurrence after ablation still are poorly understood. Hypoxia-inducible factor (HIF), the key gene switch for adaptive responses to hypoxia, has been found to play an essential role in the rapid aggressive recurrence of HCC after ablation treatment. In this review, we summarized the current evidence of the roles of HIF in the treatment of HCC with ablation. Fifteen relevant studies were included and further analyzed. Among them, three clinical studies suggested that HIF-1α might serve as a crucial role in the RAF treatment of HCC or the local recurrence of HCC after RFA. The remainder included experimental studies demonstrated that HIF-1, 2α might target the different molecules (e.g., BNIP3, CA-IX, and arginase-1) and signaling cascades (e.g., VEGFA/EphA2 pathway), constituting a complex network that promoted HCC invasion and metastasis after ablation. Currently, the inhibitors of HIF have been developed, providing important proof of targeting HIF for the prevention of HCC recurrence after IRFA and HIFU ablation. Further confirmation by prospective clinical and in-depth experimental studies is still warranted to illustrate the effects of HIF in HCC recurrence followed ablation treatment in the future.
Collapse
Affiliation(s)
- Chunying Xiao
- Department of Ultrasound, Taizhou Central Hospital (Taizhou University, Hospital), Taizhou, Zhejiang, China
| | - Sheng Liu
- Department of Hepatobiliary Surgery, Hubei Cancer Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
| | - Ge Ge
- Department of Ultrasound, Taizhou Central Hospital (Taizhou University, Hospital), Taizhou, Zhejiang, China
| | - Hao Jiang
- Department of General Surgery, Taizhou Central Hospital (Taizhou University, Hospital), Taizhou, Zhejiang, China
| | - Liezhi Wang
- Department of General Surgery, Taizhou Central Hospital (Taizhou University, Hospital), Taizhou, Zhejiang, China
| | - Qi Chen
- Precision Medicine Center, Taizhou Central Hospital (Taizhou University Hospital), Taizhou, Zhejiang, China
| | - Chong Jin
- Department of General Surgery, Taizhou Central Hospital (Taizhou University, Hospital), Taizhou, Zhejiang, China
| | - Jinggang Mo
- Department of General Surgery, Taizhou Central Hospital (Taizhou University, Hospital), Taizhou, Zhejiang, China
| | - Jin Li
- Department of Ultrasound, Taizhou Central Hospital (Taizhou University, Hospital), Taizhou, Zhejiang, China
| | - Kunpeng Wang
- Department of General Surgery, Taizhou Central Hospital (Taizhou University, Hospital), Taizhou, Zhejiang, China
| | - Qianqian Zhang
- Liyuan Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China,*Correspondence: Qianqian Zhang, ; Jianyu Zhou,
| | - Jianyu Zhou
- Department of Ultrasound, Taizhou Central Hospital (Taizhou University, Hospital), Taizhou, Zhejiang, China,*Correspondence: Qianqian Zhang, ; Jianyu Zhou,
| |
Collapse
|
45
|
Li J, Chen J, Tao Q, Zheng J, Zhou Z. Machine learning integrations for development of a T-cell-tolerance derived signature to improve the clinical outcomes and precision treatment of hepatocellular carcinoma. Am J Cancer Res 2023; 13:66-85. [PMID: 36777501 PMCID: PMC9906086] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/22/2022] [Accepted: 01/07/2023] [Indexed: 02/14/2023] Open
Abstract
Hepatocellular carcinoma (HCC) is characterized by high rates of recurrence and metastasis and poor prognosis. A recently discovered concept of T cell tolerance (TCT) has become an entirely new target of cancer immunotherapy. Unfortunately, the effect of TCT on the outcomes of HCC has not been explored. In this study, 7 public datasets and one external clinical cohort, including 1716 HCC patients were explored. Through WGCNA analysis and differential analysis, we explored the key TCT-related modulates. A total of 95 machine learning integrations across all validation cohorts were compared and the optimal method with the highest average C-index value was selected to construct the TCT derived signature (TCTS). In all independent clinical cohorts, TCTS showed accurate prediction of the prognosis, and was significantly correlated with clinical indicators and molecular features. Compared with 77 published gene signatures, the TCTS exhibited superior predictive performance. In the external clinical cohort, a novel nomogram (comprising TNM stage, Hepatitis B, Vascular invasion, Perineural invasion, AFP and TCTS) was constructed to test the clinical performance of TCTS. The results showed that the high TCTS scoring group showed dismal prognosis, improved sensitivity to oxaliplatin and good response to anti-PD-1/PD-L1 immunotherapy. Moreover, the low TCTS score group had few genomic alterations, low immune activation and low PD-1/PD-L1 expression levels. In conclusion, TCTS is an ideal biomarker for predicting the clinical outcomes and improving precision treatment of HCC.
Collapse
Affiliation(s)
- Junjian Li
- Department of Hepatobiliary Surgery, The First Affiliated Hospital of Wenzhou Medical UniversityWenzhou 325000, Zhejiang, P. R. China
| | - Ji Chen
- Key Laboratory of Diagnosis and Treatment of Severe Hepato-Pancreatic Diseases of Zhejiang Province, The First Affiliated Hospital of Wenzhou Medical UniversityWenzhou 325000, Zhejiang, P. R. China
| | - Qiqi Tao
- Key Laboratory of Diagnosis and Treatment of Severe Hepato-Pancreatic Diseases of Zhejiang Province, The First Affiliated Hospital of Wenzhou Medical UniversityWenzhou 325000, Zhejiang, P. R. China
| | - Jianjian Zheng
- Key Laboratory of Clinical Laboratory Diagnosis and Translational Research of Zhejiang Province, The First Affiliated Hospital of Wenzhou Medical UniversityWenzhou 325000, Zhejiang, P. R. China
| | - Zhenxu Zhou
- Department of Hernia and Abdominal Wall Surgery, The First Affiliated Hospital of Wenzhou Medical UniversityWenzhou 325000, Zhejiang, P. R. China
| |
Collapse
|
46
|
Tian Y, Xiao H, Yang Y, Zhang P, Yuan J, Zhang W, Chen L, Fan Y, Zhang J, Cheng H, Deng T, Yang L, Wang W, Chen G, Wang P, Gong P, Niu X, Zhang X. Crosstalk between 5-methylcytosine and N 6-methyladenosine machinery defines disease progression, therapeutic response and pharmacogenomic landscape in hepatocellular carcinoma. Mol Cancer 2023; 22:5. [PMID: 36627693 PMCID: PMC9830866 DOI: 10.1186/s12943-022-01706-6] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/24/2022] [Accepted: 12/22/2022] [Indexed: 01/12/2023] Open
Abstract
BACKGROUND Accumulated evidence highlights the significance of the crosstalk between epigenetic and epitranscriptomic mechanisms, notably 5-methylcytosine (5mC) and N6-methyladenosine (m6A). Herein, we conducted a widespread analysis regarding the crosstalk between 5mC and m6A regulators in hepatocellular carcinoma (HCC). METHODS Pan-cancer genomic analysis of the crosstalk between 5mC and m6A regulators was presented at transcriptomic, genomic, epigenetic, and other multi-omics levels. Hub 5mC and m6A regulators were summarized to define an epigenetic and epitranscriptomic module eigengene (EME), which reflected both the pre- and post-transcriptional modifications. RESULTS 5mC and m6A regulators interacted with one another at the multi-omic levels across pan-cancer, including HCC. The EME scoring system enabled to greatly optimize risk stratification and accurately predict HCC patients' clinical outcomes and progression. Additionally, the EME accurately predicted the responses to mainstream therapies (TACE and sorafenib) and immunotherapy as well as hyper-progression. In vitro, 5mC and m6A regulators cooperatively weakened apoptosis and facilitated proliferation, DNA damage repair, G2/M arrest, migration, invasion and epithelial-to-mesenchymal transition (EMT) in HCC cells. The EME scoring system was remarkably linked to potential extrinsic and intrinsic immune escape mechanisms, and the high EME might contribute to a reduced copy number gain/loss frequency. Finally, we determined potential therapeutic compounds and druggable targets (TUBB1 and P2RY4) for HCC patients with high EME. CONCLUSIONS Our findings suggest that HCC may result from a unique synergistic combination of 5mC-epigenetic mechanism mixed with m6A-epitranscriptomic mechanism, and their crosstalk defines therapeutic response and pharmacogenomic landscape.
Collapse
Affiliation(s)
- Yu Tian
- grid.263488.30000 0001 0472 9649Department of General Surgery and Integrated Chinese and Western Medicine, Institute of Precision Diagnosis and Treatment of Digestive System Tumors, Carson International Cancer Center, Shenzhen University General Hospital, Shenzhen University, Shenzhen, Guangdong 518055 China
| | - Haijuan Xiao
- grid.508012.eDepartment of Oncology, Shaanxi Province, Affiliated Hospital of the Shaanxi University of Traditional Chinese Medicine, Xianyang, Shaanxi 712046 China
| | - Yanhui Yang
- grid.263488.30000 0001 0472 9649Department of General Surgery and Integrated Chinese and Western Medicine, Institute of Precision Diagnosis and Treatment of Digestive System Tumors, Carson International Cancer Center, Shenzhen University General Hospital, Shenzhen University, Shenzhen, Guangdong 518055 China ,grid.453074.10000 0000 9797 0900Department of Trauma Surgery, The First Affiliated Hospital and College of Clinical Medicine, Henan University of Science and Technology, Luoyang, 471003 Henan China
| | - Pingping Zhang
- grid.411525.60000 0004 0369 1599Department of Gastroenterology, Changhai Hospital, Naval Medical University, Shanghai, 200433 China
| | - Jiahui Yuan
- grid.263488.30000 0001 0472 9649Department of General Surgery and Integrated Chinese and Western Medicine, Institute of Precision Diagnosis and Treatment of Digestive System Tumors, Carson International Cancer Center, Shenzhen University General Hospital, Shenzhen University, Shenzhen, Guangdong 518055 China
| | - Wei Zhang
- grid.263488.30000 0001 0472 9649Department of General Surgery and Integrated Chinese and Western Medicine, Institute of Precision Diagnosis and Treatment of Digestive System Tumors, Carson International Cancer Center, Shenzhen University General Hospital, Shenzhen University, Shenzhen, Guangdong 518055 China
| | - Lijie Chen
- grid.412449.e0000 0000 9678 1884China Medical University, Shenyang, 110122 Liaoning China
| | - Yibao Fan
- grid.263488.30000 0001 0472 9649Department of General Surgery and Integrated Chinese and Western Medicine, Institute of Precision Diagnosis and Treatment of Digestive System Tumors, Carson International Cancer Center, Shenzhen University General Hospital, Shenzhen University, Shenzhen, Guangdong 518055 China
| | - Jinze Zhang
- grid.263488.30000 0001 0472 9649Department of General Surgery and Integrated Chinese and Western Medicine, Institute of Precision Diagnosis and Treatment of Digestive System Tumors, Carson International Cancer Center, Shenzhen University General Hospital, Shenzhen University, Shenzhen, Guangdong 518055 China
| | - Huan Cheng
- grid.263488.30000 0001 0472 9649Department of General Surgery and Integrated Chinese and Western Medicine, Institute of Precision Diagnosis and Treatment of Digestive System Tumors, Carson International Cancer Center, Shenzhen University General Hospital, Shenzhen University, Shenzhen, Guangdong 518055 China
| | - Tingwei Deng
- grid.263488.30000 0001 0472 9649Department of General Surgery and Integrated Chinese and Western Medicine, Institute of Precision Diagnosis and Treatment of Digestive System Tumors, Carson International Cancer Center, Shenzhen University General Hospital, Shenzhen University, Shenzhen, Guangdong 518055 China
| | - Lin Yang
- grid.440299.2Department of Hepatobiliary Surgery, Shaanxi Province, Xianyang Central Hospital, Xianyang, 712099 Shaanxi China
| | - Weiwei Wang
- grid.263488.30000 0001 0472 9649Department of General Surgery and Integrated Chinese and Western Medicine, Institute of Precision Diagnosis and Treatment of Digestive System Tumors, Carson International Cancer Center, Shenzhen University General Hospital, Shenzhen University, Shenzhen, Guangdong 518055 China ,grid.414011.10000 0004 1808 090XHepatobiliary Surgery, People’s Hospital of Zhengzhou University and Henan Provincial People’s Hospital, Zhengzhou, 450001 Henan China
| | - Guoyong Chen
- grid.263488.30000 0001 0472 9649Department of General Surgery and Integrated Chinese and Western Medicine, Institute of Precision Diagnosis and Treatment of Digestive System Tumors, Carson International Cancer Center, Shenzhen University General Hospital, Shenzhen University, Shenzhen, Guangdong 518055 China ,grid.414011.10000 0004 1808 090XHepatobiliary Surgery, People’s Hospital of Zhengzhou University and Henan Provincial People’s Hospital, Zhengzhou, 450001 Henan China
| | - Peiqin Wang
- Department of Gastroenterology, Changzheng Hospital, Naval Medical University, Shanghai, 200003 China
| | - Peng Gong
- grid.263488.30000 0001 0472 9649Department of General Surgery and Integrated Chinese and Western Medicine, Institute of Precision Diagnosis and Treatment of Digestive System Tumors, Carson International Cancer Center, Shenzhen University General Hospital, Shenzhen University, Shenzhen, Guangdong 518055 China
| | - Xing Niu
- grid.263488.30000 0001 0472 9649Department of General Surgery and Integrated Chinese and Western Medicine, Institute of Precision Diagnosis and Treatment of Digestive System Tumors, Carson International Cancer Center, Shenzhen University General Hospital, Shenzhen University, Shenzhen, Guangdong 518055 China ,grid.412449.e0000 0000 9678 1884China Medical University, Shenyang, 110122 Liaoning China
| | - Xianbin Zhang
- grid.263488.30000 0001 0472 9649Department of General Surgery and Integrated Chinese and Western Medicine, Institute of Precision Diagnosis and Treatment of Digestive System Tumors, Carson International Cancer Center, Shenzhen University General Hospital, Shenzhen University, Shenzhen, Guangdong 518055 China
| |
Collapse
|
47
|
Mou L, Pu Z, Luo Y, Quan R, So Y, Jiang H. Construction of a lipid metabolism-related risk model for hepatocellular carcinoma by single cell and machine learning analysis. Front Immunol 2023; 14:1036562. [PMID: 36936948 PMCID: PMC10014552 DOI: 10.3389/fimmu.2023.1036562] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/04/2022] [Accepted: 02/15/2023] [Indexed: 03/05/2023] Open
Abstract
One of the most common cancers is hepatocellular carcinoma (HCC). Numerous studies have shown the relationship between abnormal lipid metabolism-related genes (LMRGs) and malignancies. In most studies, the single LMRG was studied and has limited clinical application value. This study aims to develop a novel LMRG prognostic model for HCC patients and to study its utility for predictive, preventive, and personalized medicine. We used the single-cell RNA sequencing (scRNA-seq) dataset and TCGA dataset of HCC samples and discovered differentially expressed LMRGs between primary and metastatic HCC patients. By using the least absolute selection and shrinkage operator (LASSO) regression machine learning algorithm, we constructed a risk prognosis model with six LMRGs (AKR1C1, CYP27A1, CYP2C9, GLB1, HMGCS2, and PLPP1). The risk prognosis model was further validated in an external cohort of ICGC. We also constructed a nomogram that could accurately predict overall survival in HCC patients based on cancer status and LMRGs. Further investigation of the association between the LMRG model and somatic tumor mutational burden (TMB), tumor immune infiltration, and biological function was performed. We found that the most frequent somatic mutations in the LMRG high-risk group were CTNNB1, TTN, TP53, ALB, MUC16, and PCLO. Moreover, naïve CD8+ T cells, common myeloid progenitors, endothelial cells, granulocyte-monocyte progenitors, hematopoietic stem cells, M2 macrophages, and plasmacytoid dendritic cells were significantly correlated with the LMRG high-risk group. Finally, gene set enrichment analysis showed that RNA degradation, spliceosome, and lysosome pathways were associated with the LMRG high-risk group. For the first time, we used scRNA-seq and bulk RNA-seq to construct an LMRG-related risk score model, which may provide insights into more effective treatment strategies for predictive, preventive, and personalized medicine of HCC patients.
Collapse
Affiliation(s)
- Lisha Mou
- Imaging Department, Shenzhen Institute of Translational Medicine, The First Affiliated Hospital of Shenzhen University, Shenzhen Second People's Hospital, Shenzhen, China
- MetaLife Center, Shenzhen Institute of Translational Medicine, The First Affiliated Hospital of Shenzhen University, Shenzhen Second People's Hospital, Shenzhen, China
| | - Zuhui Pu
- Imaging Department, Shenzhen Institute of Translational Medicine, The First Affiliated Hospital of Shenzhen University, Shenzhen Second People's Hospital, Shenzhen, China
| | - Yongxiang Luo
- Department of General Surgery, The First People's Hospital of Qinzhou/The Tenth Affiliated Hospital of Guangxi Medical University, Qinzhou, Guangxi, China
| | - Ryan Quan
- MetaLife Center, Shenzhen Institute of Translational Medicine, The First Affiliated Hospital of Shenzhen University, Shenzhen Second People's Hospital, Shenzhen, China
| | - Yunhu So
- MetaLife Center, Shenzhen Institute of Translational Medicine, The First Affiliated Hospital of Shenzhen University, Shenzhen Second People's Hospital, Shenzhen, China
| | - Hui Jiang
- Department of General Surgery, The First People's Hospital of Qinzhou/The Tenth Affiliated Hospital of Guangxi Medical University, Qinzhou, Guangxi, China
| |
Collapse
|
48
|
Zhuang X, Deng G, Wu X, Xie J, Li D, Peng S, Tang D, Zhou G. Recent advances of three-dimensional bioprinting technology in hepato-pancreato-biliary cancer models. Front Oncol 2023; 13:1143600. [PMID: 37188191 PMCID: PMC10175665 DOI: 10.3389/fonc.2023.1143600] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/13/2023] [Accepted: 04/03/2023] [Indexed: 05/17/2023] Open
Abstract
Hepato-pancreato-biliary (HPB) cancer is a serious category of cancer including tumors originating in the liver, pancreas, gallbladder and biliary ducts. It is limited by two-dimensional (2D) cell culture models for studying its complicated tumor microenvironment including diverse contents and dynamic nature. Recently developed three-dimensional (3D) bioprinting is a state-of-the-art technology for fabrication of biological constructs through layer-by-layer deposition of bioinks in a spatially defined manner, which is computer-aided and designed to generate viable 3D constructs. 3D bioprinting has the potential to more closely recapitulate the tumor microenvironment, dynamic and complex cell-cell and cell-matrix interactions compared to the current methods, which benefits from its precise definition of positioning of various cell types and perfusing network in a high-throughput manner. In this review, we introduce and compare multiple types of 3D bioprinting methodologies for HPB cancer and other digestive tumors. We discuss the progress and application of 3D bioprinting in HPB and gastrointestinal cancers, focusing on tumor model manufacturing. We also highlight the current challenges regarding clinical translation of 3D bioprinting and bioinks in the field of digestive tumor research. Finally, we suggest valuable perspectives for this advanced technology, including combination of 3D bioprinting with microfluidics and application of 3D bioprinting in the field of tumor immunology.
Collapse
Affiliation(s)
- Xiaomei Zhuang
- Scientific Research Center, The Seventh Affiliated Hospital, Sun Yat-sen University, Shenzhen, China
| | - Gang Deng
- Department of General Surgery, The Seventh Affiliated Hospital, Sun Yat-sen University, Shenzhen, China
| | - Xiaoying Wu
- Department of General Surgery, The Seventh Affiliated Hospital, Sun Yat-sen University, Shenzhen, China
| | - Juping Xie
- Department of General Surgery, The Seventh Affiliated Hospital, Sun Yat-sen University, Shenzhen, China
| | - Dong Li
- Department of General Surgery, The Seventh Affiliated Hospital, Sun Yat-sen University, Shenzhen, China
| | - Songlin Peng
- Department of General Surgery, The Seventh Affiliated Hospital, Sun Yat-sen University, Shenzhen, China
| | - Di Tang
- Department of General Surgery, The Seventh Affiliated Hospital, Sun Yat-sen University, Shenzhen, China
| | - Guoying Zhou
- Scientific Research Center, The Seventh Affiliated Hospital, Sun Yat-sen University, Shenzhen, China
- *Correspondence: Guoying Zhou, ;
| |
Collapse
|
49
|
Eschrich J, Kobus Z, Geisel D, Halskov S, Roßner F, Roderburg C, Mohr R, Tacke F. The Diagnostic Approach towards Combined Hepatocellular-Cholangiocarcinoma-State of the Art and Future Perspectives. Cancers (Basel) 2023; 15:cancers15010301. [PMID: 36612297 PMCID: PMC9818385 DOI: 10.3390/cancers15010301] [Citation(s) in RCA: 6] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/14/2022] [Revised: 12/17/2022] [Accepted: 12/27/2022] [Indexed: 01/03/2023] Open
Abstract
Combined hepatocellular-cholangiocarcinoma (cHCC-CCA) is a rare primary liver cancer which displays clinicopathologic features of both hepatocellular (HCC) and cholangiocellular carcinoma (CCA). The similarity to HCC and CCA makes the diagnostic workup particularly challenging. Alpha-fetoprotein (AFP) and carbohydrate antigen 19-9 (CA 19-9) are blood tumour markers related with HCC and CCA, respectively. They can be used as diagnostic markers in cHCC-CCA as well, albeit with low sensitivity. The imaging features of cHCC-CCA overlap with those of HCC and CCA, dependent on the predominant histopathological component. Using the Liver Imaging and Reporting Data System (LI-RADS), as many as half of cHCC-CCAs may be falsely categorised as HCC. This is especially relevant since the diagnosis of HCC may be made without histopathological confirmation in certain cases. Thus, in instances of diagnostic uncertainty (e.g., simultaneous radiological HCC and CCA features, elevation of CA 19-9 and AFP, HCC imaging features and elevated CA 19-9, and vice versa) multiple image-guided core needle biopsies should be performed and analysed by an experienced pathologist. Recent advances in the molecular characterisation of cHCC-CCA, innovative diagnostic approaches (e.g., liquid biopsies) and methods to analyse multiple data points (e.g., clinical, radiological, laboratory, molecular, histopathological features) in an all-encompassing way (e.g., by using artificial intelligence) might help to address some of the existing diagnostic challenges.
Collapse
Affiliation(s)
- Johannes Eschrich
- Department of Hepatology and Gastroenterology, Campus Virchow Klinikum and Campus Charité Mitte, Charité Universitätsmedizin Berlin, Augustenburger Platz 1, 13353 Berlin, Germany
| | - Zuzanna Kobus
- Department of Hepatology and Gastroenterology, Campus Virchow Klinikum and Campus Charité Mitte, Charité Universitätsmedizin Berlin, Augustenburger Platz 1, 13353 Berlin, Germany
| | - Dominik Geisel
- Department for Radiology, Campus Virchow Klinikum, Charité Universitätsmedizin Berlin, Augustenburger Platz 1, 13353 Berlin, Germany
| | - Sebastian Halskov
- Department for Radiology, Campus Virchow Klinikum, Charité Universitätsmedizin Berlin, Augustenburger Platz 1, 13353 Berlin, Germany
| | - Florian Roßner
- Department of Pathology, Charité Universitätsmedizin Berlin, Charitéplatz 1, 10117 Berlin, Germany
| | - Christoph Roderburg
- Clinic for Gastroenterology, Hepatology and Infectious Diseases, Medical Faculty of Heinrich Heine University Düsseldorf, University Hospital Düsseldorf, Moorenstraße 5, 40225 Düsseldorf, Germany
| | - Raphael Mohr
- Department of Hepatology and Gastroenterology, Campus Virchow Klinikum and Campus Charité Mitte, Charité Universitätsmedizin Berlin, Augustenburger Platz 1, 13353 Berlin, Germany
| | - Frank Tacke
- Department of Hepatology and Gastroenterology, Campus Virchow Klinikum and Campus Charité Mitte, Charité Universitätsmedizin Berlin, Augustenburger Platz 1, 13353 Berlin, Germany
| |
Collapse
|
50
|
Wang J, Wu R, Sun JY, Lei F, Tan H, Lu X. An overview: Management of patients with advanced hepatocellular carcinoma. Biosci Trends 2022; 16:405-425. [PMID: 36476621 DOI: 10.5582/bst.2022.01109] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/12/2022]
Abstract
Hepatocellular carcinoma (HCC) has constituted a significant health burden worldwide, and patients with advanced HCC, which is stage C as defined by the Barcelona Clinic Liver Cancer staging system, have a poor overall survival of 6-8 months. Studies have indicated the significant survival benefit of treatment based on sorafenib, lenvatinib, or atezolizumab-bevacizumab with reliable safety. In addition, the combination of two or more molecularly targeted therapies (first- plus second-line) has become a hot topic recently and is now being extensively investigated in patients with advanced HCC. In addition, a few biomarkers have been investigated and found to predict drug susceptibility and prognosis, which provides an opportunity to evaluate the clinical benefits of current therapies. In addition, many therapies other than tyrosine kinase inhibitors that might have additional survival benefits when combined with other therapeutic modalities, including immunotherapy, transarterial chemoembolization, radiofrequency ablation, hepatectomy, and chemotherapy, have also been examined. This review provides an overview on the current understanding of disease management and summarizes current challenges with and future perspectives on advanced HCC.
Collapse
Affiliation(s)
- Jincheng Wang
- The First Affiliated Hospital of Nanjing Medical University, Nanjing Medical University, Nanjing, Jiangsu, China.,Graduate School of Biomedical Science and Engineering, Hokkaido University, Sapporo, Japan
| | - Rui Wu
- The First Affiliated Hospital of Nanjing Medical University, Nanjing Medical University, Nanjing, Jiangsu, China
| | - Jin-Yu Sun
- The First Affiliated Hospital of Nanjing Medical University, Nanjing Medical University, Nanjing, Jiangsu, China
| | - Feifei Lei
- Department of Infectious Diseases, Liver Disease Laboratory, Renmin Hospital, Hubei University of Medicine, Shiyan, Hubei, China
| | - Huabing Tan
- Department of Infectious Diseases, Liver Disease Laboratory, Renmin Hospital, Hubei University of Medicine, Shiyan, Hubei, China
| | - Xiaojie Lu
- Department of General Surgery, Liver Transplantation Center, The First Affiliated Hospital of Nanjing Medical University, Nanjing, Jiangsu, China
| |
Collapse
|