1
|
Numata Y, Akutsu N, Idogawa M, Wagatsuma K, Numata Y, Ishigami K, Nakamura T, Hirano T, Kawakami Y, Masaki Y, Murota A, Sasaki S, Nakase H. Genomic analysis of an aggressive hepatic leiomyosarcoma case following treatment for hepatocellular carcinoma. Hepatol Res 2024. [PMID: 38459823 DOI: 10.1111/hepr.14034] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 08/08/2023] [Revised: 02/05/2024] [Accepted: 02/17/2024] [Indexed: 03/10/2024]
Abstract
A 70-year-old man undergoing treatment for immunoglobulin G4-related disease developed a liver mass on computed tomography during routine imaging examination. The tumor was located in the hepatic S1/4 region, was 38 mm in size, and showed arterial enhancement on dynamic contrast-enhanced computed tomography. We performed a liver biopsy and diagnosed moderately differentiated hepatocellular carcinoma. The patient underwent proton beam therapy. The tumor remained unchanged but enlarged after 4 years. The patient was diagnosed with hepatocellular carcinoma recurrence and received hepatic arterial chemoembolization. However, 1 year later, the patient developed jaundice, and the liver tumor grew in size. Unfortunately, the patient passed away. Autopsy revealed that the tumor consisted of spindle-shaped cells exhibiting nuclear atypia and a fission pattern and tested positive for α-smooth muscle actin and vimentin. No hepatocellular carcinoma components were observed, and the patient was pathologically diagnosed with hepatic leiomyosarcoma. Next-generation sequencing revealed somatic mutations in CACNA2D4, CTNNB1, DOCK5, IPO8, MTMR1, PABPC5, SEMA6D, and ZFP36L1. Based on the genetic mutation, sarcomatoid hepatocarcinoma was the most likely pathogenesis in this case. This mutation is indicative of the transition from sarcomatoid hepatocarcinoma to hepatic leiomyosarcoma.
Collapse
Affiliation(s)
- Yuto Numata
- Department of Gastroenterology and Hepatology, Sapporo Medical University School of Medicine, Sapporo, Japan
| | - Noriyuki Akutsu
- Department of Gastroenterology and Hepatology, Sapporo Medical University School of Medicine, Sapporo, Japan
| | - Masashi Idogawa
- Department of Gastroenterology and Hepatology, Sapporo Medical University School of Medicine, Sapporo, Japan
- Department of Medical Genome Sciences, Cancer Research Institute, Sapporo Medical University School of Medicine, Sapporo, Japan
| | - Kohei Wagatsuma
- Department of Gastroenterology and Hepatology, Sapporo Medical University School of Medicine, Sapporo, Japan
| | - Yasunao Numata
- Department of Gastroenterology and Hepatology, Sapporo Medical University School of Medicine, Sapporo, Japan
| | - Keisuike Ishigami
- Department of Gastroenterology and Hepatology, Sapporo Medical University School of Medicine, Sapporo, Japan
| | - Tomoya Nakamura
- Department of Gastroenterology and Hepatology, Sapporo Medical University School of Medicine, Sapporo, Japan
| | - Takehiro Hirano
- Department of Gastroenterology and Hepatology, Sapporo Medical University School of Medicine, Sapporo, Japan
| | - Yujiro Kawakami
- Department of Gastroenterology and Hepatology, Sapporo Medical University School of Medicine, Sapporo, Japan
| | - Yoshiharu Masaki
- Department of Gastroenterology and Hepatology, Sapporo Medical University School of Medicine, Sapporo, Japan
| | - Ayako Murota
- Department of Gastroenterology and Hepatology, Sapporo Medical University School of Medicine, Sapporo, Japan
| | - Shigeru Sasaki
- Department of Gastroenterology and Hepatology, Sapporo Medical University School of Medicine, Sapporo, Japan
| | - Hiroshi Nakase
- Department of Gastroenterology and Hepatology, Sapporo Medical University School of Medicine, Sapporo, Japan
| |
Collapse
|
2
|
Olatunji I, Cui F. Multimodal AI for prediction of distant metastasis in carcinoma patients. Front Bioinform 2023; 3:1131021. [PMID: 37228671 PMCID: PMC10203594 DOI: 10.3389/fbinf.2023.1131021] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/24/2022] [Accepted: 04/24/2023] [Indexed: 05/27/2023] Open
Abstract
Metastasis of cancer is directly related to death in almost all cases, however a lot is yet to be understood about this process. Despite advancements in the available radiological investigation techniques, not all cases of Distant Metastasis (DM) are diagnosed at initial clinical presentation. Also, there are currently no standard biomarkers of metastasis. Early, accurate diagnosis of DM is however crucial for clinical decision making, and planning of appropriate management strategies. Previous works have achieved little success in attempts to predict DM from either clinical, genomic, radiology, or histopathology data. In this work we attempt a multimodal approach to predict the presence of DM in cancer patients by combining gene expression data, clinical data and histopathology images. We tested a novel combination of Random Forest (RF) algorithm with an optimization technique for gene selection, and investigated if gene expression pattern in the primary tissues of three cancer types (Bladder Carcinoma, Pancreatic Adenocarcinoma, and Head and Neck Squamous Carcinoma) with DM are similar or different. Gene expression biomarkers of DM identified by our proposed method outperformed Differentially Expressed Genes (DEGs) identified by the DESeq2 software package in the task of predicting presence or absence of DM. Genes involved in DM tend to be more cancer type specific rather than general across all cancers. Our results also indicate that multimodal data is more predictive of metastasis than either of the three unimodal data tested, and genomic data provides the highest contribution by a wide margin. The results re-emphasize the importance for availability of sufficient image data when a weakly supervised training technique is used. Code is made available at: https://github.com/rit-cui-lab/Multimodal-AI-for-Prediction-of-Distant-Metastasis-in-Carcinoma-Patients.
Collapse
|
3
|
Zhang FL, Li DQ. Targeting Chromatin-Remodeling Factors in Cancer Cells: Promising Molecules in Cancer Therapy. Int J Mol Sci 2022; 23:12815. [PMID: 36361605 PMCID: PMC9655648 DOI: 10.3390/ijms232112815] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/30/2022] [Revised: 10/12/2022] [Accepted: 10/19/2022] [Indexed: 03/28/2024] Open
Abstract
ATP-dependent chromatin-remodeling complexes can reorganize and remodel chromatin and thereby act as important regulator in various cellular processes. Based on considerable studies over the past two decades, it has been confirmed that the abnormal function of chromatin remodeling plays a pivotal role in genome reprogramming for oncogenesis in cancer development and/or resistance to cancer therapy. Recently, exciting progress has been made in the identification of genetic alteration in the genes encoding the chromatin-remodeling complexes associated with tumorigenesis, as well as in our understanding of chromatin-remodeling mechanisms in cancer biology. Here, we present preclinical evidence explaining the signaling mechanisms involving the chromatin-remodeling misregulation-induced cancer cellular processes, including DNA damage signaling, metastasis, angiogenesis, immune signaling, etc. However, even though the cumulative evidence in this field provides promising emerging molecules for therapeutic explorations in cancer, more research is needed to assess the clinical roles of these genetic cancer targets.
Collapse
Affiliation(s)
- Fang-Lin Zhang
- Shanghai Cancer Center and Institutes of Biomedical Sciences, Shanghai Medical College, Fudan University, Shanghai 200032, China
- Cancer Institute, Shanghai Medical College, Fudan University, Shanghai 200032, China
- Department of Oncology, Shanghai Medical College, Fudan University, Shanghai 200032, China
| | - Da-Qiang Li
- Shanghai Cancer Center and Institutes of Biomedical Sciences, Shanghai Medical College, Fudan University, Shanghai 200032, China
- Cancer Institute, Shanghai Medical College, Fudan University, Shanghai 200032, China
- Department of Oncology, Shanghai Medical College, Fudan University, Shanghai 200032, China
- Department of Breast Surgery, Shanghai Medical College, Fudan University, Shanghai 200032, China
- Shanghai Key Laboratory of Breast Cancer, Shanghai Medical College, Fudan University, Shanghai 200032, China
- Shanghai Key Laboratory of Radiation Oncology, Shanghai Medical College, Fudan University, Shanghai 200032, China
| |
Collapse
|
4
|
Abstract
Phylloquinone (vitamin K1) and menaquinones (vitamin K2 family) are essential for post-translational γ-carboxylation of a small number of proteins, including clotting factors. These modified proteins have now been implicated in diverse physiological and pathological processes including cancer. Vitamin K intake has been inversely associated with cancer incidence and mortality in observational studies. Newly discovered functions of vitamin K in cancer cells include activation of the steroid and xenobiotic receptor (SXR) and regulation of oxidative stress, apoptosis, and autophagy. We provide an update of vitamin K biology, non-canonical mechanisms of vitamin K actions, the potential functions of vitamin K-dependent proteins in cancer, and observational trials on vitamin K intake and cancer.
Collapse
Affiliation(s)
- JoEllen Welsh
- Cancer Research Center and Department of Environmental Health Sciences, University at Albany, Rensselaer, NY 12144, USA.
| | - Min Ji Bak
- Cancer Research Center and Department of Environmental Health Sciences, University at Albany, Rensselaer, NY 12144, USA
| | - Carmen J Narvaez
- Cancer Research Center and Department of Environmental Health Sciences, University at Albany, Rensselaer, NY 12144, USA
| |
Collapse
|
5
|
Zhang Q, Liu X, Chen Z, Zhang S. Novel GIRlncRNA Signature for Predicting the Clinical Outcome and Therapeutic Response in NSCLC. Front Pharmacol 2022; 13:937531. [PMID: 35991889 PMCID: PMC9382191 DOI: 10.3389/fphar.2022.937531] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/06/2022] [Accepted: 06/23/2022] [Indexed: 11/18/2022] Open
Abstract
Background: Non–small cell lung cancer (NSCLC) is highly malignant with driver somatic mutations and genomic instability. Long non-coding RNAs (lncRNAs) play a vital role in regulating these two aspects. However, the identification of somatic mutation-derived, genomic instability-related lncRNAs (GIRlncRNAs) and their clinical significance in NSCLC remains largely unexplored. Methods: Clinical information, gene mutation, and lncRNA expression data were extracted from TCGA database. GIRlncRNAs were screened by a mutator hypothesis-derived computational frame. Co-expression, GO, and KEGG enrichment analyses were performed to investigate the biological functions. Cox and LASSO regression analyses were performed to create a prognostic risk model based on the GIRlncRNA signature (GIRlncSig). The prediction efficiency of the model was evaluated by using correlation analyses with mutation, driver gene, immune microenvironment contexture, and therapeutic response. The prognostic performance of the model was evaluated by external datasets. A nomogram was established and validated in the testing set and TCGA dataset. Results: A total of 1446 GIRlncRNAs were selected from the screen, and the established GIRlncSig was used to classify patients into high- and low-risk groups. Enrichment analyses showed that GIRlncRNAs were mainly associated with nucleic acid metabolism and DNA damage repair pathways. Cox analyses further identified 19 GIRlncRNAs to construct a GIRlncSig-based risk score model. According to Cox regression and stratification analyses, 14 risk lncRNAs (AC023824.3, AC013287.1, AP000829.1, LINC01611, AC097451.1, AC025419.1, AC079949.2, LINC01600, AC004862.1, AC021594.1, MYRF-AS1, LINC02434, LINC02412, and LINC00337) and five protective lncRNAs (LINC01067, AC012645.1, AL512604.3, AC008278.2, and AC089998.1) were considered powerful predictors. Analyses of the model showed that these GIRlncRNAs were correlated with somatic mutation pattern, immune microenvironment infiltration, immunotherapeutic response, drug sensitivity, and survival of NSCLC patients. The GIRlncSig risk score model demonstrated good predictive performance (AUCs of ROC for 10-year survival was 0.69) and prognostic value in different NSCLC datasets. The nomogram comprising GIRlncSig and tumor stage exhibited improved robustness and feasibility for predicting NSCLC prognosis. Conclusion: The newly identified GIRlncRNAs are powerful biomarkers for clinical outcome and prognosis of NSCLC. Our study highlights that the GIRlncSig-based score model may be a useful tool for risk stratification and management of NSCLC patients, which deserves further evaluation in future prospective studies.
Collapse
Affiliation(s)
- Qiangzhe Zhang
- State Key Laboratory of Medicinal Chemical Biology and College of Pharmacy, Tianjin Key Laboratory of Molecular Drug Research, Nankai University, Tianjin, China
| | - Xicheng Liu
- Department of Physiology and Pathophysiology, School of Basic Medical Sciences, Capital Medical University, Beijing, China
| | - Zhinan Chen
- National Translational Science Center for Molecular Medicine, Department of Cell Biology, State Key Laboratory of Cancer Biology, Fourth Military Medical University, Xi’an, China
| | - Sihe Zhang
- Department of Cell Biology, School of Medicine, Nankai University, Tianjin, China
- *Correspondence: Sihe Zhang, , https://orcid.org/0000-0002-8923-1993
| |
Collapse
|
6
|
MotieGhader H, Tabrizi-Nezhadi P, Deldar Abad Paskeh M, Baradaran B, Mokhtarzadeh A, Hashemi M, Lanjanian H, Jazayeri SM, Maleki M, Khodadadi E, Nematzadeh S, Kiani F, Maghsoudloo M, Masoudi-Nejad A. Drug repositioning in non-small cell lung cancer (NSCLC) using gene co-expression and drug–gene interaction networks analysis. Sci Rep 2022; 12:9417. [PMID: 35676421 PMCID: PMC9177601 DOI: 10.1038/s41598-022-13719-8] [Citation(s) in RCA: 7] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/22/2022] [Accepted: 05/16/2022] [Indexed: 12/14/2022] Open
Abstract
Lung cancer is the most common cancer in men and women. This cancer is divided into two main types, namely non-small cell lung cancer (NSCLC) and small cell lung cancer (SCLC). Around 85 to 90 percent of lung cancers are NSCLC. Repositioning potent candidate drugs in NSCLC treatment is one of the important topics in cancer studies. Drug repositioning (DR) or drug repurposing is a method for identifying new therapeutic uses of existing drugs. The current study applies a computational drug repositioning method to identify candidate drugs to treat NSCLC patients. To this end, at first, the transcriptomics profile of NSCLC and healthy (control) samples was obtained from the GEO database with the accession number GSE21933. Then, the gene co-expression network was reconstructed for NSCLC samples using the WGCNA, and two significant purple and magenta gene modules were extracted. Next, a list of transcription factor genes that regulate purple and magenta modules' genes was extracted from the TRRUST V2.0 online database, and the TF–TG (transcription factors–target genes) network was drawn. Afterward, a list of drugs targeting TF–TG genes was obtained from the DGIdb V4.0 database, and two drug–gene interaction networks, including drug-TG and drug-TF, were drawn. After analyzing gene co-expression TF–TG, and drug–gene interaction networks, 16 drugs were selected as potent candidates for NSCLC treatment. Out of 16 selected drugs, nine drugs, namely Methotrexate, Olanzapine, Haloperidol, Fluorouracil, Nifedipine, Paclitaxel, Verapamil, Dexamethasone, and Docetaxel, were chosen from the drug-TG sub-network. In addition, nine drugs, including Cisplatin, Daunorubicin, Dexamethasone, Methotrexate, Hydrocortisone, Doxorubicin, Azacitidine, Vorinostat, and Doxorubicin Hydrochloride, were selected from the drug-TF sub-network. Methotrexate and Dexamethasone are common in drug-TG and drug-TF sub-networks. In conclusion, this study proposed 16 drugs as potent candidates for NSCLC treatment through analyzing gene co-expression, TF–TG, and drug–gene interaction networks.
Collapse
|
7
|
Li L, Yu X, Ma G, Ji Z, Bao S, He X, Song L, Yu Y, Shi M, Liu X. Identification of an Innate Immune-Related Prognostic Signature in Early-Stage Lung Squamous Cell Carcinoma. Int J Gen Med 2021; 14:9007-9022. [PMID: 34876838 PMCID: PMC8643179 DOI: 10.2147/ijgm.s341175] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/04/2021] [Accepted: 11/15/2021] [Indexed: 12/26/2022] Open
Abstract
Background Early-stage lung squamous cell carcinoma (LUSC) progression is accompanied by changes in immune microenvironments and the expression of immune-related genes (IRGs). Identifying innate IRGs associated with prognosis may improve treatment and reveal new immunotherapeutic targets. Methods Gene expression profiles and clinical data of early-stage LUSC patients were obtained from the Gene Expression Omnibus and The Cancer Genome Atlas databases and IRGs from the InnateDB database. Univariate and multivariate Cox regression and LASSO regression analyses were performed to identify an innate IRG signature model prognostic in patients with early-stage LUSC. The predictive ability of this model was assessed by time-dependent receiver operator characteristic curve analysis, with the independence of the model-determined risk score assessed by univariate and multivariate Cox regression analyses. Overall survival (OS) in early-stage LUSC patients was assessed using a nomogram and decision curve analysis (DCA). Functional and biological pathways were determined by gene set enrichment analysis, and differences in biological functions and immune microenvironments between the high- and low-risk groups were assessed by ESTIMATE and the CIBERSORT algorithm. Results A signature involving six IRGs (SREBF2, GP2, BMX, NR1H4, DDX41, and GOPC) was prognostic of OS. Samples were divided into high- and low-risk groups based on median risk scores. OS was significantly shorter in the high-risk than in the low-risk group in the training (P < 0.001), GEO validation (P = 0.00021) and TCGA validation (P = 0.034) cohorts. Multivariate Cox regression analysis showed that risk score was an independent risk factor for OS, with the combination of risk score and T stage being optimally predictive of clinical benefit. GSEA, ESTIMATE, and the CIBERSORT algorithm showed that immune cell infiltration was higher and immune-related pathways were more strongly expressed in the low-risk group. Conclusion A signature that includes these six innate IRGs may predict prognosis in patients with early-stage LUSC.
Collapse
Affiliation(s)
- Liang Li
- Department of Thoracic Surgery, Shandong Provincial Hospital, Cheeloo College of Medicine, Shandong University, Jinan, Shandong, 250021, People's Republic of China
| | - Xue Yu
- Department of Pediatrics, Wuhan Children's Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, Hubei, 420100, People's Republic of China
| | - Guanqiang Ma
- Department of Thoracic Surgery, Shandong Provincial Hospital, Cheeloo College of Medicine, Shandong University, Jinan, Shandong, 250021, People's Republic of China
| | - Zhiqi Ji
- Department of Thoracic Surgery, Shandong Provincial Hospital, Cheeloo College of Medicine, Shandong University, Jinan, Shandong, 250021, People's Republic of China
| | - Shihao Bao
- Department of Thoracic Surgery, Shandong Provincial Hospital, Cheeloo College of Medicine, Shandong University, Jinan, Shandong, 250021, People's Republic of China
| | - Xiaopeng He
- Department of Thoracic Surgery, Shandong Provincial Hospital, Cheeloo College of Medicine, Shandong University, Jinan, Shandong, 250021, People's Republic of China.,Department of Thoracic Surgery, Shandong Provincial Hospital Affiliated to Shandong First Medical University, Jinan, Shandong, 250021, People's Republic of China
| | - Liang Song
- Department of Thoracic Surgery, Shandong Provincial Hospital, Cheeloo College of Medicine, Shandong University, Jinan, Shandong, 250021, People's Republic of China.,Department of Thoracic Surgery, Shandong Provincial Hospital Affiliated to Shandong First Medical University, Jinan, Shandong, 250021, People's Republic of China
| | - Yang Yu
- Department of Thoracic Surgery, Shandong Provincial Hospital, Cheeloo College of Medicine, Shandong University, Jinan, Shandong, 250021, People's Republic of China.,Department of Thoracic Surgery, Shandong Provincial Hospital Affiliated to Shandong First Medical University, Jinan, Shandong, 250021, People's Republic of China
| | - Mo Shi
- Department of Thoracic Surgery, Shandong Provincial Hospital, Cheeloo College of Medicine, Shandong University, Jinan, Shandong, 250021, People's Republic of China.,Department of Thoracic Surgery, Shandong Provincial Hospital Affiliated to Shandong First Medical University, Jinan, Shandong, 250021, People's Republic of China
| | - Xiangyan Liu
- Department of Thoracic Surgery, Shandong Provincial Hospital, Cheeloo College of Medicine, Shandong University, Jinan, Shandong, 250021, People's Republic of China.,Department of Thoracic Surgery, Shandong Provincial Hospital Affiliated to Shandong First Medical University, Jinan, Shandong, 250021, People's Republic of China
| |
Collapse
|