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Galli E, Patelli G, Villa F, Gri N, Mazzarelli C, Mangoni I, Sgrazzutti C, Ghezzi S, Sartore-Bianchi A, Belli LS, De Carlis L, Vanzulli A, Siena S, Bencardino K. Circulating blood biomarkers for minimal residual disease in hepatocellular carcinoma: A systematic review. Cancer Treat Rev 2025; 135:102908. [PMID: 40058162 DOI: 10.1016/j.ctrv.2025.102908] [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: 11/09/2024] [Revised: 02/24/2025] [Accepted: 02/25/2025] [Indexed: 04/08/2025]
Abstract
BACKGROUND Relapse after radical treatment remains a major concern in hepatocellular carcinoma (HCC), affecting 50-75 % of early-stage cases within 5 years. Early recurrence prediction is a clinical unmet need. Circulating blood biomarkers could provide a minimally invasive approach to detect minimal residual disease (MRD) post-intervention. Although alpha-fetoprotein has been the primary biomarker in this setting, its MRD sensitivity is limited to 50-70 %. This systematic review aims to summarize available evidence regarding the clinical validity and potential utility of emerging circulating blood biomarkers for MRD detection in HCC patients. METHODS We searched PubMed and Embase for peer-reviewed articles and abstracts published up to 2025, and ClinicalTrials.gov for ongoing trials on circulating blood biomarkers for MRD in HCC. RESULTS A total of 91 studies (74 with results and 17 ongoing, out of 2,386) were retrieved. We evaluated various blood biomarkers, including circulating DNA (cDNA, N = 24), circulating tumor cells (CTCs, N = 20), circulating RNA (cRNA, N = 8), and other miscellaneous (N = 22) for MRD detection in HCC. These biomarkers demonstrated encouraging results, albeit with notable heterogeneity. In particular, circulating tumor DNA (ctDNA) and CTCs stand as the most robust novel approaches, with 50-80 % sensitivity and specificity up to 94 %. Nonetheless, none of the 17 ongoing studies involve biomarker-driven intervention to prove clinical utility. CONCLUSIONS Novel circulating blood biomarkers are mature for MRD detection in HCC. However, variability in methodologies and results highlights the need for further validation. We encourage the investigation of CTCs and/or ctDNA in interventional trials to assess clinical utility. This biomarker-driven approach may enhance adjuvant treatment effectiveness in MRD-positive cases while minimizing toxicity in MRD-negative patients.
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Affiliation(s)
- Edoardogregorio Galli
- Department of Oncology and Hemato-Oncology, Università degli Studi di Milano (La Statale), Milan, Italy; Niguarda Cancer Center, Department of Hematology, Oncology and Molecular Medicine, Grande Ospedale Metropolitano Niguarda, Milan, Italy
| | - Giorgio Patelli
- Department of Oncology and Hemato-Oncology, Università degli Studi di Milano (La Statale), Milan, Italy; Niguarda Cancer Center, Department of Hematology, Oncology and Molecular Medicine, Grande Ospedale Metropolitano Niguarda, Milan, Italy; IFOM ETS - The AIRC Institute of Molecular Oncology, Milan, Italy.
| | - Federica Villa
- Niguarda Cancer Center, Department of Hematology, Oncology and Molecular Medicine, Grande Ospedale Metropolitano Niguarda, Milan, Italy
| | - Nicole Gri
- Department of Oncology and Hemato-Oncology, Università degli Studi di Milano (La Statale), Milan, Italy; Niguarda Cancer Center, Department of Hematology, Oncology and Molecular Medicine, Grande Ospedale Metropolitano Niguarda, Milan, Italy
| | - Chiara Mazzarelli
- Hepatology and Gastroenterology Unit, Grande Ospedale Metropolitano Niguarda, Milan, Italy
| | - Iacopo Mangoni
- Department of General Surgery and Transplantation, Grande Ospedale Metropolitano Niguarda, Milan, Italy
| | | | - Silvia Ghezzi
- Niguarda Cancer Center, Department of Hematology, Oncology and Molecular Medicine, Grande Ospedale Metropolitano Niguarda, Milan, Italy
| | - Andrea Sartore-Bianchi
- Department of Oncology and Hemato-Oncology, Università degli Studi di Milano (La Statale), Milan, Italy; Niguarda Cancer Center, Department of Hematology, Oncology and Molecular Medicine, Grande Ospedale Metropolitano Niguarda, Milan, Italy; Division of Clinical Research and Innovation, Grande Ospedale Metropolitano Niguarda, Milan, Italy
| | - Luca Saverio Belli
- Hepatology and Gastroenterology Unit, Grande Ospedale Metropolitano Niguarda, Milan, Italy
| | - Luciano De Carlis
- Department of General Surgery and Transplantation, Grande Ospedale Metropolitano Niguarda, Milan, Italy
| | - Angelo Vanzulli
- Department of Oncology and Hemato-Oncology, Università degli Studi di Milano (La Statale), Milan, Italy; Department of Radiology, Grande Ospedale Metropolitano Niguarda, Milan, Italy
| | - Salvatore Siena
- Department of Oncology and Hemato-Oncology, Università degli Studi di Milano (La Statale), Milan, Italy; Niguarda Cancer Center, Department of Hematology, Oncology and Molecular Medicine, Grande Ospedale Metropolitano Niguarda, Milan, Italy
| | - Katia Bencardino
- Niguarda Cancer Center, Department of Hematology, Oncology and Molecular Medicine, Grande Ospedale Metropolitano Niguarda, Milan, Italy
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Liu L, Yu P, Zhao Z, Yang H, Yu R. Pharmacological mechanisms of carvacrol against hepatocellular carcinoma by network pharmacology and molecular docking. Technol Health Care 2025:9287329241306192. [PMID: 39973856 DOI: 10.1177/09287329241306192] [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: 02/21/2025]
Abstract
BACKGROUND Preclinical studies have demonstrated that carvacrol possesses various biological and pharmacological properties, including anti-hepatocellular carcinoma (HCC) effects. However, the molecular basis of its therapeutic action on HCC remains unclear. OBJECTIVE The aim of this study was to investigate and further validate the multi-target therapeutic mechanism of carvacrol against HCC. METHODS The chemical structure of carvacrol was obtained from the PubChem database, and its potential targets were identified using SwissTargetPrediction, HERB, and BATMAN-TCM. HCC-specific genes were screened from the TCGA-LIHC cohort. The therapeutic targets of carvacrol against HCC were determined through the intersection of these datasets. Subsequently, a multivariate Cox regression prognostic model was established. Molecular docking was performed to analyze the interactions between carvacrol and its therapeutic targets. Additionally, molecular dynamics simulations were conducted to validate the molecular docking results using Discovery Studio 2019 software. RESULTS A total of 223 carvacrol targets and 882 HCC-specific genes were identified. Fifteen therapeutic targets of carvacrol against HCC were obtained, including CA2, AR, ALB, AURKA, ALPL, EPHX2, BCHE, IL1RN, AGRN, CRP, DMGDH, APOA1, SOX9, HPX, and CHKA. The prognostic model accurately and independently predicted survival outcomes. AGRN and AURKA were significantly associated with HCC overall survival. Molecular docking and molecular dynamics simulations demonstrated that carvacrol exhibited strong potential for stable binding to the therapeutic targets AGRN and AURKA. CONCLUSION Our findings elucidate the multi-target mechanism of action of carvacrol against HCC, providing a foundation for future research on its application in HCC management.
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Affiliation(s)
- Lu Liu
- Cancer Center, Zhejiang University, Lishui Hospital, Lishui City, Zhejiang Province, China
- Cancer Center, The Fifth Affiliated Hospital of Wenzhou Medical College, Lishui City, Zhejiang Province, China
- Cancer Center, Lishui Central Hospital, Lishui City, Zhejiang Province, China
| | - Ping Yu
- Department of Pharmacy, Shaoxing People's Hospital, Shaoxing City, Zhejiang Province, China
- Department of Pharmacy, Shaoxing Hospital Affiliated Zhejiang University School of Medicine, Shaoxing City, Zhejiang Province, China
| | - Zhongwei Zhao
- Cancer Center, Zhejiang University, Lishui Hospital, Lishui City, Zhejiang Province, China
- Cancer Center, The Fifth Affiliated Hospital of Wenzhou Medical College, Lishui City, Zhejiang Province, China
- Cancer Center, Lishui Central Hospital, Lishui City, Zhejiang Province, China
| | - Hongyuan Yang
- Cancer Center, Zhejiang University, Lishui Hospital, Lishui City, Zhejiang Province, China
- Cancer Center, The Fifth Affiliated Hospital of Wenzhou Medical College, Lishui City, Zhejiang Province, China
- Cancer Center, Lishui Central Hospital, Lishui City, Zhejiang Province, China
| | - Risheng Yu
- Department of Radiology, Second Affiliated Hospital, School of Medicine, Zhejiang University, Hangzhou City, Zhejiang, China
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Lanza C, Ascenti V, Amato GV, Pellegrino G, Triggiani S, Tintori J, Intrieri C, Angileri SA, Biondetti P, Carriero S, Torcia P, Ierardi AM, Carrafiello G. All You Need to Know About TACE: A Comprehensive Review of Indications, Techniques, Efficacy, Limits, and Technical Advancement. J Clin Med 2025; 14:314. [PMID: 39860320 PMCID: PMC11766109 DOI: 10.3390/jcm14020314] [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: 11/10/2024] [Revised: 12/17/2024] [Accepted: 12/28/2024] [Indexed: 01/27/2025] Open
Abstract
Transcatheter arterial chemoembolization (TACE) is a proven and widely accepted treatment option for hepatocellular carcinoma and it is recommended as first-line non-curative therapy for BCLC B/intermediate HCC (preserved liver function, multifocal, no cancer-related symptoms) in patients without vascular involvement. Different types of TACE are available nowadays, including TAE, c-TACE, DEB-TACE, and DSM-TACE, but at present there is insufficient evidence to recommend one TACE technique over another and the choice is left to the operator. This review then aims to provide a comprehensive overview of the current literature on indications, types of procedures, safety, and efficacy of different TACE treatments.
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Affiliation(s)
- Carolina Lanza
- Department of Diagnostic and Interventional Radiology, Foundation IRCCS Cà Granda—Ospedale Maggiore Policlinico, Via Francesco Sforza 35, 20122 Milan, Italy; (C.L.); (P.B.); (S.C.); (P.T.); (A.M.I.); (G.C.)
| | - Velio Ascenti
- Postgraduate School in Radiodiagnostics, Università degli Studi di Milano, 20122 Milan, Italy; (V.A.); (G.V.A.); (G.P.); (S.T.); (J.T.)
| | - Gaetano Valerio Amato
- Postgraduate School in Radiodiagnostics, Università degli Studi di Milano, 20122 Milan, Italy; (V.A.); (G.V.A.); (G.P.); (S.T.); (J.T.)
| | - Giuseppe Pellegrino
- Postgraduate School in Radiodiagnostics, Università degli Studi di Milano, 20122 Milan, Italy; (V.A.); (G.V.A.); (G.P.); (S.T.); (J.T.)
| | - Sonia Triggiani
- Postgraduate School in Radiodiagnostics, Università degli Studi di Milano, 20122 Milan, Italy; (V.A.); (G.V.A.); (G.P.); (S.T.); (J.T.)
| | - Jacopo Tintori
- Postgraduate School in Radiodiagnostics, Università degli Studi di Milano, 20122 Milan, Italy; (V.A.); (G.V.A.); (G.P.); (S.T.); (J.T.)
| | - Cristina Intrieri
- Postgraduate School in Diangostic Imaging, Università degli Studi di Siena, 20122 Milan, Italy;
| | - Salvatore Alessio Angileri
- Department of Diagnostic and Interventional Radiology, Foundation IRCCS Cà Granda—Ospedale Maggiore Policlinico, Via Francesco Sforza 35, 20122 Milan, Italy; (C.L.); (P.B.); (S.C.); (P.T.); (A.M.I.); (G.C.)
| | - Pierpaolo Biondetti
- Department of Diagnostic and Interventional Radiology, Foundation IRCCS Cà Granda—Ospedale Maggiore Policlinico, Via Francesco Sforza 35, 20122 Milan, Italy; (C.L.); (P.B.); (S.C.); (P.T.); (A.M.I.); (G.C.)
| | - Serena Carriero
- Department of Diagnostic and Interventional Radiology, Foundation IRCCS Cà Granda—Ospedale Maggiore Policlinico, Via Francesco Sforza 35, 20122 Milan, Italy; (C.L.); (P.B.); (S.C.); (P.T.); (A.M.I.); (G.C.)
| | - Pierluca Torcia
- Department of Diagnostic and Interventional Radiology, Foundation IRCCS Cà Granda—Ospedale Maggiore Policlinico, Via Francesco Sforza 35, 20122 Milan, Italy; (C.L.); (P.B.); (S.C.); (P.T.); (A.M.I.); (G.C.)
| | - Anna Maria Ierardi
- Department of Diagnostic and Interventional Radiology, Foundation IRCCS Cà Granda—Ospedale Maggiore Policlinico, Via Francesco Sforza 35, 20122 Milan, Italy; (C.L.); (P.B.); (S.C.); (P.T.); (A.M.I.); (G.C.)
| | - Gianpaolo Carrafiello
- Department of Diagnostic and Interventional Radiology, Foundation IRCCS Cà Granda—Ospedale Maggiore Policlinico, Via Francesco Sforza 35, 20122 Milan, Italy; (C.L.); (P.B.); (S.C.); (P.T.); (A.M.I.); (G.C.)
- Faculty of Health Science, Università degli Studi di Milano, Via Festa del Perdono 7, 20122 Milan, Italy
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Zheng G, Wu L, Bouamar H, Cserhati M, Chiu YC, Hinck CS, Wieteska Ł, Zeballos Torrez CR, Hu R, Easley A, Chen Y, Hinck AP, Cigarroa FG, Sun LZ. Ficolin-3 induces apoptosis and suppresses malignant property of hepatocellular carcinoma cells via the complement pathway. Life Sci 2024; 357:123103. [PMID: 39357793 DOI: 10.1016/j.lfs.2024.123103] [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: 06/07/2024] [Revised: 09/18/2024] [Accepted: 09/28/2024] [Indexed: 10/04/2024]
Abstract
AIMS Ficolin 3 (FCN3) has the highest complement-activating capacity through the lectin pathway and is synthesized mainly in the liver and lung. Yet, its potential molecular mechanism in hepatocarcinogenesis is not fully understood. MATERIALS AND METHODS The expression of FCN3 in hepatocellular carcinoma (HCC) tumor and non-tumor tissues was analyzed by RT-qPCR, Western blotting and immunofluorescence staining assays. Lentivector-mediated ectopic overexpression was performed to explore the role of FCN3 in vitro and in vivo. Whether FCN3 inhibited HCC cell growth and survival via complement pathway was determined with immunocytochemical staining for C3b, membrane attack complex (MAC) formation and complement killing assay using recombinant FCN3 (rFCN3) in combination with human serum with or without heat inactivation, and with C6 blocking antibody. KEY FINDINGS The transcript and protein of FCN3 were found to be remarkably down-regulated in HCC tumor tissues. FCN3 expression was found to be associated with better survival of HCC patients. Restoration of FCN3 expression significantly inhibited proliferation, migration and anchorage independent growth of HCC cell lines, and xenograft tumor growth. FCN3 expression induced apoptosis of HCC cells. C3 and MAC formation was stimulated by FCN3 overexpression or rFCN3 treatment. rFCN3 enhanced human serum-induced complement activation and cell death. C6 blocking antibody significantly attenuated complement-mediated cell death and restored the growth of FCN3-overexpressing HCC cells. SIGNIFICANCE FCN3 has a malignant suppressor role in HCC cells. Our study provides new insights into the molecular mechanisms that drive HCC progression and potential therapeutic targets for treating HCC.
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Affiliation(s)
- Guixi Zheng
- Department of Cell Systems & Anatomy, University of Texas Health Science Center at San Antonio, TX, United States of America; Department of Clinical Laboratory, Qilu Hospital of Shandong University, China
| | - Lianqiu Wu
- Department of Cell Systems & Anatomy, University of Texas Health Science Center at San Antonio, TX, United States of America
| | - Hakim Bouamar
- Department of Cell Systems & Anatomy, University of Texas Health Science Center at San Antonio, TX, United States of America
| | - Matyas Cserhati
- Department of Cell Systems & Anatomy, University of Texas Health Science Center at San Antonio, TX, United States of America
| | - Yu-Chiao Chiu
- Greehey Children's Cancer Research Institute, University of Texas Health Science Center at San Antonio, TX, United States of America
| | - Cinthia S Hinck
- Department of Structural Biology, University of Pittsburgh School of Medicine, PA, United States of America
| | - Łukasz Wieteska
- Department of Structural Biology, University of Pittsburgh School of Medicine, PA, United States of America
| | - Carla R Zeballos Torrez
- Department of Cell Systems & Anatomy, University of Texas Health Science Center at San Antonio, TX, United States of America
| | - Ruolei Hu
- Department of Cell Systems & Anatomy, University of Texas Health Science Center at San Antonio, TX, United States of America
| | - Acarizia Easley
- Department of Cell Systems & Anatomy, University of Texas Health Science Center at San Antonio, TX, United States of America
| | - Yidong Chen
- Department of Structural Biology, University of Pittsburgh School of Medicine, PA, United States of America; Department of Population Health Sciences, University of Texas Health Science Center at San Antonio, TX, United States of America
| | - Andrew P Hinck
- Department of Structural Biology, University of Pittsburgh School of Medicine, PA, United States of America
| | - Francisco G Cigarroa
- Transplant Center, University of Texas Health Science Center at San Antonio, TX, United States of America.
| | - Lu-Zhe Sun
- Department of Cell Systems & Anatomy, University of Texas Health Science Center at San Antonio, TX, United States of America.
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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.
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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.
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Son A, Kim W, Lee W, Park J, Kim H. Applicability of selected reaction monitoring for precise screening tests. Expert Rev Proteomics 2024; 21:237-246. [PMID: 38697802 DOI: 10.1080/14789450.2024.2350975] [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: 10/27/2023] [Accepted: 03/27/2024] [Indexed: 05/05/2024]
Abstract
INTRODUCTION The proactive identification of diseases through screening tests has long been endorsed as a means to preempt symptomatic onset. However, such screening endeavors are fraught with complications, such as diagnostic inaccuracies, procedural risks, and patient unease during examinations. These challenges are amplified when screenings for multiple diseases are administered concurrently. Selected Reaction Monitoring (SRM) offers a unique advantage, allowing for the high-throughput quantification of hundreds of analytes with minimal interferences. AREAS COVERED Our research posits that SRM-based assays, traditionally tailored for single-disease biomarker profiling, can be repurposed for multi-disease screening. This innovative approach has the potential to substantially alleviate time, labor, and cost demands on healthcare systems and patients alike. Nonetheless, there are formidable methodological hurdles to overcome. These include difficulties in detecting low-abundance proteins and the risk of model overfitting due to the multiple functionalities of single proteins across different disease spectrums - issues especially pertinent in blood-based assays where detection sensitivity is constrained. As we move forward, technological strides in sample preparation, online extraction, throughput, and automation are expected to ameliorate these limitations. EXPERT OPINION The maturation of mass spectrometry's integration into clinical laboratories appears imminent, positioning it as an invaluable asset for delivering highly sensitive, reproducible, and precise diagnostic results.
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Affiliation(s)
- Ahrum Son
- Department of Molecular Medicine, The Scripps Research Institute, La Jolla, CA, USA
| | - Woojin Kim
- Department of Bio-AI convergence Chungnam National University,Daejeon, South Korea
| | - Wonseok Lee
- Department of Bio-AI convergence Chungnam National University,Daejeon, South Korea
| | - Jongham Park
- Department of Bio-AI convergence Chungnam National University,Daejeon, South Korea
| | - Hyunsoo Kim
- Department of Bio-AI convergence Chungnam National University,Daejeon, South Korea
- Department of Convergent Bioscience and Informatics, Chungnam National University, Daejeon, Republic of Korea
- SCICS, Daejeon, Republic of Korea
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Moawad AW, Morshid A, Khalaf AM, Elmohr MM, Hazle JD, Fuentes D, Badawy M, Kaseb AO, Hassan M, Mahvash A, Szklaruk J, Qayyum A, Abusaif A, Bennett WC, Nolan TS, Camp B, Elsayes KM. Multimodality annotated hepatocellular carcinoma data set including pre- and post-TACE with imaging segmentation. Sci Data 2023; 10:33. [PMID: 36653372 PMCID: PMC9849450 DOI: 10.1038/s41597-023-01928-3] [Citation(s) in RCA: 16] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/06/2021] [Accepted: 01/03/2023] [Indexed: 01/19/2023] Open
Abstract
Hepatocellular carcinoma (HCC) is the most common primary liver neoplasm, and its incidence has doubled over the past two decades owing to increasing risk factors. Despite surveillance, most HCC cases are diagnosed at advanced stages and can only be treated using transarterial chemo-embolization (TACE) or systemic therapy. TACE failure may occur with incidence reaching up to 60% of cases, leaving patients with a financial and emotional burden. Radiomics has emerged as a new tool capable of predicting tumor response to TACE from pre-procedural computed tomography (CT) studies. This data report defines the HCC-TACE data collection of confirmed HCC patients who underwent TACE and have pre- and post-procedure CT imaging studies and available treatment outcomes (time-to-progression and overall survival). Clinically curated segmentation of pre-procedural CT studies was done for the purpose of algorithm training for prediction and automatic liver tumor segmentation.
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Affiliation(s)
- Ahmed W Moawad
- Departments of Imaging Physics, The University of Texas MD Anderson Cancer Center, Houston, TX, 77030, USA.
- Department of radiology, Mercy catholic medical center, Darby, PA, 19023, USA.
| | - Ali Morshid
- Departments of Imaging Physics, The University of Texas MD Anderson Cancer Center, Houston, TX, 77030, USA.
| | - Ahmed M Khalaf
- Departments of Imaging Physics, The University of Texas MD Anderson Cancer Center, Houston, TX, 77030, USA
| | - Mohab M Elmohr
- Departments of Imaging Physics, The University of Texas MD Anderson Cancer Center, Houston, TX, 77030, USA.
- Department of radiology, Baylor college of medicine, TX, 77030, Houston, USA.
| | - John D Hazle
- Departments of Imaging Physics, The University of Texas MD Anderson Cancer Center, Houston, TX, 77030, USA.
| | - David Fuentes
- Departments of Imaging Physics, The University of Texas MD Anderson Cancer Center, Houston, TX, 77030, USA.
| | - Mohamed Badawy
- Departments of Imaging Physics, The University of Texas MD Anderson Cancer Center, Houston, TX, 77030, USA
| | - Ahmed O Kaseb
- Departments of Gastrointestinal Oncology, The University of Texas MD Anderson Cancer Center, Houston, TX, 77030, USA.
| | - Manal Hassan
- Departments of Gastrointestinal Oncology, The University of Texas MD Anderson Cancer Center, Houston, TX, 77030, USA.
| | - Armeen Mahvash
- Departments of Interventional Radiology, The University of Texas MD Anderson Cancer Center, Houston, TX, 77030, USA
| | - Janio Szklaruk
- Departments of Body Imaging, The University of Texas MD Anderson Cancer Center, Houston, TX, 77030, USA.
| | - Aliyya Qayyum
- Departments of Body Imaging, The University of Texas MD Anderson Cancer Center, Houston, TX, 77030, USA
| | - Abdelrahman Abusaif
- Departments of Body Imaging, The University of Texas MD Anderson Cancer Center, Houston, TX, 77030, USA
| | - William C Bennett
- Department of Biomedical Informatics, University of Arkansas for Medical Sciences, Little Rock, AR, 72205, USA.
| | - Tracy S Nolan
- Department of Biomedical Informatics, University of Arkansas for Medical Sciences, Little Rock, AR, 72205, USA.
| | - Brittney Camp
- Department of Biomedical Informatics, University of Arkansas for Medical Sciences, Little Rock, AR, 72205, USA.
| | - Khaled M Elsayes
- Departments of Body Imaging, The University of Texas MD Anderson Cancer Center, Houston, TX, 77030, USA.
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An advanced network pharmacology study to explore the novel molecular mechanism of Compound Kushen Injection for treating hepatocellular carcinoma by bioinformatics and experimental verification. BMC Complement Med Ther 2022; 22:54. [PMID: 35236335 PMCID: PMC8892752 DOI: 10.1186/s12906-022-03530-3] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/19/2021] [Accepted: 02/07/2022] [Indexed: 12/13/2022] Open
Abstract
Background Compound Kushen Injection (CKI) is a Chinese patent drug that exerts curative effects in the clinical treatment of hepatocellular carcinoma (HCC). This study aimed to explore the targets and potential pharmacological mechanisms of CKI in the treatment of HCC. Methods In this study, network pharmacology was used in combination with molecular biology experiments to predict and verify the molecular mechanism of CKI in the treatment of HCC. The constituents of CKI were identified by UHPLC-MS/MS and literature search. The targets corresponding to these compounds and the targets related to HCC were collected based on public databases. To screen out the potential hub targets of CKI in the treatment of HCC, a compound-HCC target network was constructed. The underlying pharmacological mechanism was explored through the subsequent enrichment analysis. Interactive Gene Expression Profiling Analysis and Kaplan-Meier plotter were used to examine the expression and prognostic value of hub genes. Furthermore, the effects of CKI on HCC were verified through molecular docking simulations and cell experiments in vitro. Results Network analysis revealed that BCHE, SRD5A2, EPHX2, ADH1C, ADH1A and CDK1 were the key targets of CKI in the treatment of HCC. Among them, only CDK1 was highly expressed in HCC tissues, while the other 5 targets were lowly expressed. Furthermore, the six hub genes were all closely related to the prognosis of HCC patients in survival analysis. Molecular docking revealed that there was an efficient binding potential between the constituents of CKI and BCHE. Experiments in vitro proved that CKI inhibited the proliferation of HepG2 cells and up-regulated SRD5A2 and ADH1A, while down-regulated CDK1 and EPHX2. Conclusions This study revealed and verified the targets of CKI on HCC based on network pharmacology and experiments and provided a scientific reference for further mechanism research. Supplementary Information The online version contains supplementary material available at 10.1186/s12906-022-03530-3.
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Balsano C, Alisi A, Brunetto MR, Invernizzi P, Burra P, Piscaglia F. The application of artificial intelligence in hepatology: A systematic review. Dig Liver Dis 2022; 54:299-308. [PMID: 34266794 DOI: 10.1016/j.dld.2021.06.011] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 03/02/2021] [Revised: 06/04/2021] [Accepted: 06/07/2021] [Indexed: 02/06/2023]
Abstract
The integration of human and artificial intelligence (AI) in medicine has only recently begun but it has already become obvious that intelligent systems can dramatically improve the management of liver diseases. Big data made it possible to envisage transformative developments of the use of AI for diagnosing, predicting prognosis and treating liver diseases, but there is still a lot of work to do. If we want to achieve the 21st century digital revolution, there is an urgent need for specific national and international rules, and to adhere to bioethical parameters when collecting data. Avoiding misleading results is essential for the effective use of AI. A crucial question is whether it is possible to sustain, technically and morally, the process of integration between man and machine. We present a systematic review on the applications of AI to hepatology, highlighting the current challenges and crucial issues related to the use of such technologies.
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Affiliation(s)
- Clara Balsano
- Dept. of Life, Health and Environmental Sciences MESVA, University of L'Aquila, Piazza S. Salvatore Tommasi 1, 67100, Coppito, L'Aquila. Italy; Francesco Balsano Foundation, Via Giovanni Battista Martini 6, 00198, Rome, Italy.
| | - Anna Alisi
- Research Unit of Molecular Genetics of Complex Phenotypes, Bambino Gesù Children's Hospital, IRCCS, Rome, Italy
| | - Maurizia R Brunetto
- Hepatology Unit and Laboratory of Molecular Genetics and Pathology of Hepatitis Viruses, University Hospital of Pisa, Pisa, Italy
| | - Pietro Invernizzi
- Division of Gastroenterology and Center of Autoimmune Liver Diseases, Department of Medicine and Surgery, San Gerardo Hospital, University of Milano, Bicocca, Italy
| | - Patrizia Burra
- Multivisceral Transplant Unit, Department of Surgery, Oncology, Gastroenterology, Padua University Hospital, Padua, Italy
| | - Fabio Piscaglia
- Division of Internal Medicine, IRCCS Azienda Ospedaliero Universitaria di Bologna, Bologna, Italy
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10
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Zou Y, Xu Y, Chen X, Wu Y, Fu L, Lv Y. Research Progress on Leucine-Rich Alpha-2 Glycoprotein 1: A Review. Front Pharmacol 2022; 12:809225. [PMID: 35095520 PMCID: PMC8797156 DOI: 10.3389/fphar.2021.809225] [Citation(s) in RCA: 19] [Impact Index Per Article: 6.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/04/2021] [Accepted: 12/13/2021] [Indexed: 12/18/2022] Open
Abstract
Leucine-rich alpha⁃2 glycoprotein 1 (LRG1) is an important member of the leucine-rich repetitive sequence protein family. LRG1 was mainly involved in normal physiological activities of the nervous system, such as synapse formation, synapse growth, the development of nerve processes, neurotransmitter transfer and release, and cell adhesion molecules or ligand-binding proteins. Also, LRG1 affected the development of respiratory diseases, hematological diseases, endocrine diseases, tumor diseases, eye diseases, cardiovascular diseases, rheumatic immune diseases, infectious diseases, etc. LRG1 was a newly discovered important upstream signaling molecule of transforming growth factor⁃β (TGF⁃β) that affected various pathological processes through the TGF⁃β signaling pathway. However, research on LRG1 and its involvement in the occurrence and development of diseases was still in its infancy and the current studies were mainly focused on proteomic detection and basic animal experimental reports. We could reasonably predict that LRG1 might act as a new direction and strategy for the treatment of many diseases.
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Affiliation(s)
- Yonghui Zou
- Department of Pharmacy, The First Affiliated Hospital of Nanchang University, Nanchang, China.,School of Clinical Medicine, Nanchang University, Nanchang, China
| | - Yi Xu
- Department of Pharmacy, The First Affiliated Hospital of Nanchang University, Nanchang, China.,School of Clinical Medicine, Nanchang University, Nanchang, China
| | - Xiaofeng Chen
- Department of Pharmacy, The First Affiliated Hospital of Nanchang University, Nanchang, China.,School of Clinical Medicine, Nanchang University, Nanchang, China
| | - Yaoqi Wu
- Department of Pharmacy, The First Affiliated Hospital of Nanchang University, Nanchang, China.,College of Pharmacy, Nanchang University, Nanchang, China
| | - Longsheng Fu
- Department of Pharmacy, The First Affiliated Hospital of Nanchang University, Nanchang, China
| | - Yanni Lv
- Department of Pharmacy, The First Affiliated Hospital of Nanchang University, Nanchang, China
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11
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Camilli C, Hoeh AE, De Rossi G, Moss SE, Greenwood J. LRG1: an emerging player in disease pathogenesis. J Biomed Sci 2022; 29:6. [PMID: 35062948 PMCID: PMC8781713 DOI: 10.1186/s12929-022-00790-6] [Citation(s) in RCA: 62] [Impact Index Per Article: 20.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/16/2021] [Accepted: 01/11/2022] [Indexed: 12/15/2022] Open
Abstract
The secreted glycoprotein leucine-rich α-2 glycoprotein 1 (LRG1) was first described as a key player in pathogenic ocular neovascularization almost a decade ago. Since then, an increasing number of publications have reported the involvement of LRG1 in multiple human conditions including cancer, diabetes, cardiovascular disease, neurological disease, and inflammatory disorders. The purpose of this review is to provide, for the first time, a comprehensive overview of the LRG1 literature considering its role in health and disease. Although LRG1 is constitutively expressed by hepatocytes and neutrophils, Lrg1-/- mice show no overt phenotypic abnormality suggesting that LRG1 is essentially redundant in development and homeostasis. However, emerging data are challenging this view by suggesting a novel role for LRG1 in innate immunity and preservation of tissue integrity. While our understanding of beneficial LRG1 functions in physiology remains limited, a consistent body of evidence shows that, in response to various inflammatory stimuli, LRG1 expression is induced and directly contributes to disease pathogenesis. Its potential role as a biomarker for the diagnosis, prognosis and monitoring of multiple conditions is widely discussed while dissecting the mechanisms underlying LRG1 pathogenic functions. Emphasis is given to the role that LRG1 plays as a vasculopathic factor where it disrupts the cellular interactions normally required for the formation and maintenance of mature vessels, thereby indirectly contributing to the establishment of a highly hypoxic and immunosuppressive microenvironment. In addition, LRG1 has also been reported to affect other cell types (including epithelial, immune, mesenchymal and cancer cells) mostly by modulating the TGFβ signalling pathway in a context-dependent manner. Crucially, animal studies have shown that LRG1 inhibition, through gene deletion or a function-blocking antibody, is sufficient to attenuate disease progression. In view of this, and taking into consideration its role as an upstream modifier of TGFβ signalling, LRG1 is suggested as a potentially important therapeutic target. While further investigations are needed to fill gaps in our current understanding of LRG1 function, the studies reviewed here confirm LRG1 as a pleiotropic and pathogenic signalling molecule providing a strong rationale for its use in the clinic as a biomarker and therapeutic target.
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Affiliation(s)
- Carlotta Camilli
- Institute of Ophthalmology, University College London, London, UK.
| | - Alexandra E Hoeh
- Institute of Ophthalmology, University College London, London, UK
| | - Giulia De Rossi
- Institute of Ophthalmology, University College London, London, UK
| | - Stephen E Moss
- Institute of Ophthalmology, University College London, London, UK
| | - John Greenwood
- Institute of Ophthalmology, University College London, London, UK
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12
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Spieler B, Sabottke C, Moawad AW, Gabr AM, Bashir MR, Do RKG, Yaghmai V, Rozenberg R, Gerena M, Yacoub J, Elsayes KM. Artificial intelligence in assessment of hepatocellular carcinoma treatment response. Abdom Radiol (NY) 2021; 46:3660-3671. [PMID: 33786653 DOI: 10.1007/s00261-021-03056-1] [Citation(s) in RCA: 13] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/29/2020] [Revised: 03/03/2021] [Accepted: 03/09/2021] [Indexed: 02/08/2023]
Abstract
Artificial Intelligence (AI) continues to shape the practice of radiology, with imaging of hepatocellular carcinoma (HCC) being of no exception. This article prepared by members of the LI-RADS Treatment Response (TR LI-RADS) work group and associates, presents recent trends in the utility of AI applications for the volumetric evaluation and assessment of HCC treatment response. Various topics including radiomics, prognostic imaging findings, and locoregional therapy (LRT) specific issues will be discussed in the framework of HCC treatment response classification systems with focus on the Liver Reporting and Data System treatment response algorithm (LI-RADS TRA).
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13
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Shin D, Rhee SJ, Lee J, Yeo I, Do M, Joo EJ, Jung HY, Roh S, Lee SH, Kim H, Bang M, Lee KY, Kwon JS, Ha K, Ahn YM, Kim Y. Quantitative Proteomic Approach for Discriminating Major Depressive Disorder and Bipolar Disorder by Multiple Reaction Monitoring-Mass Spectrometry. J Proteome Res 2021; 20:3188-3203. [PMID: 33960196 DOI: 10.1021/acs.jproteome.1c00058] [Citation(s) in RCA: 12] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/14/2022]
Abstract
Because major depressive disorder (MDD) and bipolar disorder (BD) manifest with similar symptoms, misdiagnosis is a persistent issue, necessitating their differentiation through objective methods. This study was aimed to differentiate between these disorders using a targeted proteomic approach. Multiple reaction monitoring-mass spectrometry (MRM-MS) analysis was performed to quantify protein targets regarding the two disorders in plasma samples of 270 individuals (90 MDD, 90 BD, and 90 healthy controls (HCs)). In the training set (72 MDD and 72 BD), a generalizable model comprising nine proteins was developed. The model was evaluated in the test set (18 MDD and 18 BD). The model demonstrated a good performance (area under the curve (AUC) >0.8) in discriminating MDD from BD in the training (AUC = 0.84) and test sets (AUC = 0.81) and in distinguishing MDD from BD without current hypomanic/manic/mixed symptoms (90 MDD and 75 BD) (AUC = 0.83). Subsequently, the model demonstrated excellent performance for drug-free MDD versus BD (11 MDD and 10 BD) (AUC = 0.96) and good performance for MDD versus HC (AUC = 0.87) and BD versus HC (AUC = 0.86). Furthermore, the nine proteins were associated with neuro, oxidative/nitrosative stress, and immunity/inflammation-related biological functions. This proof-of-concept study introduces a potential model for distinguishing between the two disorders.
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Affiliation(s)
| | - Sang Jin Rhee
- Department of Psychiatry, Seoul National University College of Medicine, Seoul 03080, Republic of Korea.,Department of Neuropsychiatry, Seoul National University Hospital, Seoul 03080, Republic of Korea
| | | | | | | | - Eun-Jeong Joo
- Department of Neuropsychiatry, School of Medicine, Eulji University, Daejeon 34824, Republic of Korea.,Department of Psychiatry, Nowon Eulji Medical Center, Eulji University, Seoul 01830, Republic of Korea
| | - Hee Yeon Jung
- Department of Psychiatry, Seoul National University College of Medicine, Seoul 03080, Republic of Korea.,Department of Psychiatry, SMG-SNU Boramae Medical Center, Seoul 07061, Republic of Korea.,Institute of Human Behavioral Medicine, Seoul National University Medical Research Center, 101 Daehakro, Seoul 30380, Republic of Korea
| | - Sungwon Roh
- Department of Psychiatry, Hanyang University Hospital, Seoul 04763, Republic of Korea.,Department of Psychiatry, Hanyang University College of Medicine, Seoul 04763, Republic of Korea
| | - Sang-Hyuk Lee
- Department of Psychiatry, CHA Bundang Medical Center, CHA University School of Medicine, Seongnam 13496, Republic of Korea
| | - Hyeyoung Kim
- Department of Psychiatry, Inha University Hospital, Incheon 22332, Republic of Korea
| | - Minji Bang
- Department of Psychiatry, CHA Bundang Medical Center, CHA University School of Medicine, Seongnam 13496, Republic of Korea
| | - Kyu Young Lee
- Department of Neuropsychiatry, School of Medicine, Eulji University, Daejeon 34824, Republic of Korea.,Department of Psychiatry, Nowon Eulji Medical Center, Eulji University, Seoul 01830, Republic of Korea
| | - Jun Soo Kwon
- Department of Psychiatry, Seoul National University College of Medicine, Seoul 03080, Republic of Korea.,Department of Neuropsychiatry, Seoul National University Hospital, Seoul 03080, Republic of Korea.,Institute of Human Behavioral Medicine, Seoul National University Medical Research Center, 101 Daehakro, Seoul 30380, Republic of Korea
| | - Kyooseob Ha
- Department of Psychiatry, Seoul National University College of Medicine, Seoul 03080, Republic of Korea.,Department of Neuropsychiatry, Seoul National University Hospital, Seoul 03080, Republic of Korea.,Institute of Human Behavioral Medicine, Seoul National University Medical Research Center, 101 Daehakro, Seoul 30380, Republic of Korea
| | - Yong Min Ahn
- Department of Psychiatry, Seoul National University College of Medicine, Seoul 03080, Republic of Korea.,Department of Neuropsychiatry, Seoul National University Hospital, Seoul 03080, Republic of Korea.,Institute of Human Behavioral Medicine, Seoul National University Medical Research Center, 101 Daehakro, Seoul 30380, Republic of Korea
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14
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Jiawei Z, Min M, Yingru X, Xin Z, Danting L, Yafeng L, Jun X, Wangfa H, Lijun Z, Jing W, Dong H. Identification of Key Genes in Lung Adenocarcinoma and Establishment of Prognostic Mode. Front Mol Biosci 2020; 7:561456. [PMID: 33195408 PMCID: PMC7653064 DOI: 10.3389/fmolb.2020.561456] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/26/2020] [Accepted: 09/07/2020] [Indexed: 12/23/2022] Open
Abstract
BACKGROUND The development of human tumors is associated with the abnormal expression of various functional genes, and a massive tumor-based database needs to be deeply mined. Based on a multigene prediction model, access to urgent prognosis of patients has become possible. MATERIALS AND METHODS We selected three RNA expression profiles (GSE32863, GSE10072, and GSE43458) from the lung adenocarcinoma (LUAD) database of the Gene Expression Omnibus (GEO) and analyzed the differentially expressed genes (DEGs) between tumor and normal tissue using GEO2R program. After that, we analyzed the transcriptome data of 479 LUAD samples (54 normal tissue samples and 425 cancer tissue samples) and their clinical follow-up data from the (TCGA) database. Kaplan-Meier (KM) curve and receiver operating characteristic (ROC) were used to assess the prediction model. Multivariate Cox analysis was used to identify independent predictors. TCGA pancreatic adenocarcinoma datasets were used to establish a nomogram model. RESULTS We found 98 significantly prognosis-related genes using KM and COX analysis, among which six genes were found to be the DEGs in GEO. Using multivariate analysis, it was found that a single gene could not be used as an independent predictor of prognosis. However, the risk score calculated by weighting these six genes could serve as an independent prognosis predictor. COX analysis performed with multiple covariates such as age, gender, tumor stage, and TNM typing showed that risk score could still be utilized as an independent risk factor for patient survival rate (p = 0.013) and had an applicable reliability (area under the curve, AUC = 0.665). By combining risk score and various clinical features, the nomogram model was constructed, which had been proven to have high consistency for the prediction of 3- and 5-year survival rate (concordance = 0.751) and high accuracy as tested by ROC (AUC = 0.71;AUC = 0.708). CONCLUSION We proposed a method to predict the prognosis of LUAD by weighting multiple genes and constructed a nomogram model suitable for the prognostic evaluation of LUAD, which could provide a new tool for the identification of therapeutic targets and the efficacy evaluation of LUAD.
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Affiliation(s)
- Zhou Jiawei
- School of Medicine, Anhui University of Science and Technology, Huainan, China
| | - Mu Min
- Key Laboratory of Industrial Dust Prevention and Control and Occupational Safety and Health, Ministry of Education, Anhui University of Science and Technology, Huainan, China
| | - Xing Yingru
- Affiliated Cancer Hospital, Anhui University of Science and Technology, Huainan, China
| | - Zhang Xin
- School of Medicine, Anhui University of Science and Technology, Huainan, China
| | - Li Danting
- School of Medicine, Anhui University of Science and Technology, Huainan, China
| | - Liu Yafeng
- School of Medicine, Anhui University of Science and Technology, Huainan, China
| | - Xie Jun
- Affiliated Cancer Hospital, Anhui University of Science and Technology, Huainan, China
| | - Hu Wangfa
- Affiliated Cancer Hospital, Anhui University of Science and Technology, Huainan, China
| | - Zhang Lijun
- School of Medicine, Anhui University of Science and Technology, Huainan, China
| | - Wu Jing
- School of Medicine, Anhui University of Science and Technology, Huainan, China
- Key Laboratory of Industrial Dust Prevention and Control and Occupational Safety and Health, Ministry of Education, Anhui University of Science and Technology, Huainan, China
| | - Hu Dong
- School of Medicine, Anhui University of Science and Technology, Huainan, China
- Key Laboratory of Industrial Dust Prevention and Control and Occupational Safety and Health, Ministry of Education, Anhui University of Science and Technology, Huainan, China
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15
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Trevisan França de Lima L, Broszczak D, Zhang X, Bridle K, Crawford D, Punyadeera C. The use of minimally invasive biomarkers for the diagnosis and prognosis of hepatocellular carcinoma. Biochim Biophys Acta Rev Cancer 2020; 1874:188451. [PMID: 33065194 DOI: 10.1016/j.bbcan.2020.188451] [Citation(s) in RCA: 31] [Impact Index Per Article: 6.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/01/2020] [Revised: 10/08/2020] [Accepted: 10/08/2020] [Indexed: 02/07/2023]
Abstract
Hepatocellular carcinoma (HCC) is a common cause of cancer-related deaths worldwide. Despite advances in systemic therapies, patient survival remains low due to late diagnosis and frequent underlying liver diseases. HCC diagnosis generally relies on imaging and liver tissue biopsy. Liver biopsy presents limitations because it is invasive, potentially risky for patients and it frequently misrepresents tumour heterogeneity. Recently, liquid biopsy has emerged as a way to monitor cancer progression in a non-invasive manner. Tumours shed content into the bloodstream, such as circulating tumour cells (CTCs), circulating nucleic acids, extracellular vesicles and proteins, that can be isolated from biological fluids of patients with HCC. These biomarkers provide knowledge regarding the genetic landscape of tumours and might be used for diagnostic or prognostic purposes. In this review, we summarize recent literature on circulating biomarkers for HCC, namely CTCs, circulating tumour DNA (ctDNA), RNA, extracellular vesicles and proteins, and their clinical relevance in HCC.
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Affiliation(s)
- Lucas Trevisan França de Lima
- Institute of Health & Biomedical Innovation, School of Biomedical Sciences, Queensland University of Technology, Kelvin Grove Campus, QLD, Australia; Gallipoli Medical Research Foundation, Greenslopes Private Hospital, Greenslopes, QLD, Australia
| | - Daniel Broszczak
- Institute of Health & Biomedical Innovation, School of Biomedical Sciences, Queensland University of Technology, Kelvin Grove Campus, QLD, Australia
| | - Xi Zhang
- Institute of Health & Biomedical Innovation, School of Biomedical Sciences, Queensland University of Technology, Kelvin Grove Campus, QLD, Australia
| | - Kim Bridle
- The University of Queensland, Faculty of Medicine, Herston, QLD, Australia; Gallipoli Medical Research Foundation, Greenslopes Private Hospital, Greenslopes, QLD, Australia
| | - Darrell Crawford
- The University of Queensland, Faculty of Medicine, Herston, QLD, Australia; Gallipoli Medical Research Foundation, Greenslopes Private Hospital, Greenslopes, QLD, Australia
| | - Chamindie Punyadeera
- Institute of Health & Biomedical Innovation, School of Biomedical Sciences, Queensland University of Technology, Kelvin Grove Campus, QLD, Australia.
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16
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Zhao Y, Li Y, Liu W, Xing S, Wang D, Chen J, Sun L, Mu J, Liu W, Xing B, Sun W, He F. Identification of noninvasive diagnostic biomarkers for hepatocellular carcinoma by urinary proteomics. J Proteomics 2020; 225:103780. [PMID: 32298775 DOI: 10.1016/j.jprot.2020.103780] [Citation(s) in RCA: 19] [Impact Index Per Article: 3.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/23/2020] [Revised: 04/02/2020] [Accepted: 04/11/2020] [Indexed: 02/07/2023]
Abstract
Hepatocellular carcinoma (HCC) ranks fourth in cancer mortality worldwide, and third in China. Hepatitis B virus (HBV) infection is a main risk factor for HCC in China, and the early diagnosis of HCC in high-risk population is very important. However, the commonly used diagnostic biomarker alpha-fetoprotein has limitations in clinical practice. In order to identify reliable and noninvasive HCC urinary biomarkers, a high-throughput proteomics streamline was applied in the analysis of urine samples from 74 HCC and 82 high-risk patients with chronic HBV infected liver diseases. Candidate diagnostic markers were screened by feature selection algorithm, and were combined with random forest or simple voting algorithms in the training dataset. Then the multiple feature models were validated in an independent test dataset. The selected features were further verified by Multiple Reaction Monitoring (MRM) in another independent dataset. By integrating 7 features screened in the discovery phase, random forest model achieved AUC of 0.92 and 0.87 in training and test datasets, respectively, while voting model performed better with AUC of 0.94 and 0.90, respectively. In the MRM dataset, the 7 features were targeted quantified, and voting model integrating the 7 features achieved AUC of 0.95. Our work highlights the potential of noninvasive urinary protein biomarkers in HCC diagnosis with high-risk population, which will be beneficial to HCC auxiliary diagnosis and HCC surveillance. SIGNIFICANCE: A high throughput urinary proteome analysis platform was committed into the discovery of noninvasive HCC biomarkers in high-risk patients with chronic HBV infected liver diseases. The combination of 7 urinary features achieved good performance in distinguishing HCC from high-risk population. The expression of the 7 features was validated by targeted MRM, and the integration of the features also worked well in the MRM dataset. This is the first time that urinary proteomic strategy was applied in discovering HCC biomarkers from high-risk population. This result will be helpful for HCC auxiliary diagnosis and surveillance in a noninvasive way.
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Affiliation(s)
- Yinghua Zhao
- School of Life Sciences, Peking University, Beijing, China; State Key Laboratory of Proteomics, Beijing Proteome Research Center, National Center for Protein Sciences (Beijing), Beijing Institute of Lifeomics, Beijing, China
| | - Yang Li
- State Key Laboratory of Proteomics, Beijing Proteome Research Center, National Center for Protein Sciences (Beijing), Beijing Institute of Lifeomics, Beijing, China
| | - Wei Liu
- Key Laboratory of Carcinogenesis and Translational Research (Ministry of Education/Beijing), Department of Hepato-Pancreato-Billiary Surgery I, Peking University Cancer Hospital and Institute, Beijing, China
| | - Shan Xing
- State Key Laboratory of Oncology in South China, Collaborative Innovation Center for Cancer Medicine, Sun Yat-sen University Cancer Center, Guangzhou, China
| | - Dan Wang
- The Center for Critical Care Medicine, The Fifth Medical Center, Chinese PLA General Hospital, Beijing, China
| | - Jing Chen
- Liver Failure Treatment and Research Center, The Fifth Medical Center, Chinese PLA General Hospital, Beijing, China
| | - Longqin Sun
- State Key Laboratory of Proteomics, Beijing Proteome Research Center, National Center for Protein Sciences (Beijing), Beijing Institute of Lifeomics, Beijing, China
| | - Jinsong Mu
- The Center for Critical Care Medicine, The Fifth Medical Center, Chinese PLA General Hospital, Beijing, China.
| | - Wanli Liu
- State Key Laboratory of Oncology in South China, Collaborative Innovation Center for Cancer Medicine, Sun Yat-sen University Cancer Center, Guangzhou, China.
| | - Baocai Xing
- Key Laboratory of Carcinogenesis and Translational Research (Ministry of Education/Beijing), Department of Hepato-Pancreato-Billiary Surgery I, Peking University Cancer Hospital and Institute, Beijing, China.
| | - Wei Sun
- State Key Laboratory of Proteomics, Beijing Proteome Research Center, National Center for Protein Sciences (Beijing), Beijing Institute of Lifeomics, Beijing, China.
| | - Fuchu He
- State Key Laboratory of Proteomics, Beijing Proteome Research Center, National Center for Protein Sciences (Beijing), Beijing Institute of Lifeomics, Beijing, China.
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17
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Qu S, Shi Q, Xu J, Yi W, Fan H. Weighted Gene Coexpression Network Analysis Reveals the Dynamic Transcriptome Regulation and Prognostic Biomarkers of Hepatocellular Carcinoma. Evol Bioinform Online 2020; 16:1176934320920562. [PMID: 32523331 PMCID: PMC7235675 DOI: 10.1177/1176934320920562] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/06/2020] [Accepted: 03/30/2020] [Indexed: 12/14/2022] Open
Abstract
This study was aimed at revealing the dynamic regulation of mRNAs, long noncoding RNAs (lncRNAs), and microRNAs (miRNAs) in hepatocellular carcinoma (HCC) and to identify HCC biomarkers capable of predicting prognosis. Differentially expressed mRNAs (DEmRNAs), lncRNAs, and miRNAs were acquired by comparing expression profiles of HCC with normal samples, using an expression data set from The Cancer Genome Atlas. Altered biological functions and pathways in HCC were analyzed by subjecting DEmRNAs to Gene Ontology and Kyoto Encyclopedia of Genes and Genomes analysis. Gene modules significantly associated with disease status were identified by weighted gene coexpression network analysis. An lncRNA-mRNA and an miRNA-mRNA coexpression network were constructed for genes in disease-related modules, followed by the identification of prognostic biomarkers using Kaplan-Meier survival analysis. Differential expression and association with the prognosis of 4 miRNAs were verified in independent data sets. A total of 1220 differentially expressed genes were identified between HCC and normal samples. Differentially expressed mRNAs were significantly enriched in functions and pathways related to “plasma membrane structure,” “sensory perception,” “metabolism,” and “cell proliferation.” Two disease-associated gene modules were identified. Among genes in lncRNA-mRNA and miRNA-mRNA coexpression networks, 9 DEmRNAs and 7 DEmiRNAs were identified to be potential prognostic biomarkers. MIMAT0000102, MIMAT0003882, and MIMAT0004677 were successfully validated in independent data sets. Our results may advance our understanding of molecular mechanisms underlying HCC. The biomarkers may contribute to diagnosis in future clinical practice.
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Affiliation(s)
- Shuping Qu
- Department of Hepatic Surgery, The Eastern Hepatobiliary Surgery Hospital, Second Military Medical University, Shanghai, China
| | - Qiuyuan Shi
- Department of Nuclear Medicine, Shanghai Tenth People's Hospital, School of Medicine, Tongji University, Shanghai, China
| | - Jing Xu
- Department of Interventional Oncology, Shanghai Seventh People's Hospital, Shanghai University of Traditional Chinese Medicine, Shanghai, China
| | - Wanwan Yi
- Department of Nuclear Medicine, Shanghai Tenth People's Hospital, School of Medicine, Tongji University, Shanghai, China
| | - Hengwei Fan
- Department of Hepatic Surgery, The Eastern Hepatobiliary Surgery Hospital, Second Military Medical University, Shanghai, China
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18
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Kowalczyk T, Ciborowski M, Kisluk J, Kretowski A, Barbas C. Mass spectrometry based proteomics and metabolomics in personalized oncology. Biochim Biophys Acta Mol Basis Dis 2020; 1866:165690. [PMID: 31962175 DOI: 10.1016/j.bbadis.2020.165690] [Citation(s) in RCA: 46] [Impact Index Per Article: 9.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/03/2019] [Revised: 12/18/2019] [Accepted: 01/15/2020] [Indexed: 02/06/2023]
Abstract
Precision medicine (PM) means the customization of healthcare with decisions and practices adjusted to the individual patient. It includes personalized diagnostics, patients' sub-classification, individual treatment selection and the monitoring of its effectiveness. Currently, in oncology, PM is based on the molecular and cellular features of a tumor, its microenvironment and the patient's genetics and lifestyle. Surprisingly, the available targeted therapies were found effective only in a subset of patients. An in-depth understanding of tumor biology is crucial to improve their effectiveness and develop new therapeutic targets. Completion of genetic information with proteomics and metabolomics can give broader knowledge about tumor biology which consequently provides novel biomarkers and indicates new therapeutic targets. Recently, metabolomics and proteomics have extensively been applied in the field of oncology. In the context of PM, human studies, with the use of mass spectrometry (MS) which allows the detection of thousands of molecules in a large number of samples, are the most valuable. Such studies, focused on cancer biomarkers discovery or patients' stratification, are presented in this review. Moreover, the technical aspects of MS-based clinical proteomics and metabolomics are described.
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Affiliation(s)
- Tomasz Kowalczyk
- Metabolomics Laboratory, Clinical Research Centre, Medical University of Bialystok, Bialystok, Poland
| | - Michal Ciborowski
- Metabolomics Laboratory, Clinical Research Centre, Medical University of Bialystok, Bialystok, Poland
| | - Joanna Kisluk
- Department of Clinical Molecular Biology, Medical University of Bialystok, Bialystok, Poland
| | - Adam Kretowski
- Metabolomics Laboratory, Clinical Research Centre, Medical University of Bialystok, Bialystok, Poland; Department of Endocrinology, Diabetology and Internal Medicine, Medical University of Bialystok, Bialystok, Poland
| | - Coral Barbas
- Centre for Metabolomics and Bioanalysis (CEMBIO), Facultad de Farmacia, Universidad CEU San Pablo, Madrid, Spain.
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Park J, Oh HJ, Han D, Wang JI, Park IA, Ryu HS, Kim Y. Parallel Reaction Monitoring-Mass Spectrometry (PRM-MS)-Based Targeted Proteomic Surrogates for Intrinsic Subtypes in Breast Cancer: Comparative Analysis with Immunohistochemical Phenotypes. J Proteome Res 2019; 19:2643-2653. [DOI: 10.1021/acs.jproteome.9b00490] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/06/2023]
Affiliation(s)
- Joonho Park
- Department of Biomedical Engineering, Seoul National University College of Medicine, 103 Daehak-ro, Seoul 03080, Korea
| | - Hyeon Jeong Oh
- Department of Pathology, Seoul National University Hospital, 101 Daehak-ro, Seoul 03080, Korea
| | - Dohyun Han
- Biomedical Research Institute, Seoul National University Hospital, 101 Daehak-ro, Seoul 03080, Korea
| | - Joseph I. Wang
- Biomedical Research Institute, Seoul National University Hospital, 101 Daehak-ro, Seoul 03080, Korea
| | - In Ae Park
- Department of Pathology, Seoul National University Hospital, 101 Daehak-ro, Seoul 03080, Korea
| | - Han Suk Ryu
- Department of Pathology, Seoul National University Hospital, 101 Daehak-ro, Seoul 03080, Korea
| | - Youngsoo Kim
- Department of Biomedical Engineering, Seoul National University College of Medicine, 103 Daehak-ro, Seoul 03080, Korea
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20
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Khalaf AM, Fuentes D, Morshid A, Kaseb AO, Hassan M, Hazle JD, Elsayes KM. Hepatocellular carcinoma response to transcatheter arterial chemoembolisation using automatically generated pre-therapeutic tumour volumes by a random forest-based segmentation protocol. Clin Radiol 2019; 74:974.e13-974.e20. [PMID: 31521326 DOI: 10.1016/j.crad.2019.07.023] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/14/2019] [Accepted: 07/31/2019] [Indexed: 01/03/2023]
Abstract
AIM To demonstrate the feasibility of correlating pre-therapeutic volumes and residual liver volume (RLV) with clinical outcomes: time to progression (TTP) and overall survival (OS) in hepatocellular carcinoma (HCC) treated with transcatheter arterial chemoembolisation (TACE). MATERIALS AND METHODS TTP was calculated from a database of 105 patients, receiving first-line treatment with TACE. TTP cut-off for stratifying patients into responders and non-responders was 28 weeks. Pre-treatment tumour and liver volumes were correlated with the TTP and OS following treatment. Univariate cox-regression model was used to assess whether these volumes could predict TTP and/or OS. Kaplan-Meier analysis with log-rank test was used to compare the TTP between high and low volume groups for viable, necrotic, and total tumour. Kaplan-Meier analysis was performed comparing the OS of 10 patients with the longest TTP (mean=122 weeks) in the responder group and 10 patients with the shortest TTP (mean=7 weeks) in the non-responder group. RESULTS HCC in high tumour volume groups had a shorter TTP than lesions in low tumour volume groups (p=0.05, p=0.04, p=0.02, for enhancing, non-enhancing, total tumour groups, respectively). A negative (correlation coefficient [CC] 0.3) linear correlation between TTP and tumour volumes, and a positive linear correlation between TTP and residual liver volumes were also demonstrated (CC 0.3). Patients with the longest TTP had a higher OS than with the shortest TTP (p=0.03). CONCLUSION This demonstrates the feasibility of predicting treatment response of HCC to TACE using volumetric measurements of pre-treatment lesion and the feasibility of correlating RLV with TACE outcome data in HCC patients.
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Affiliation(s)
- A M Khalaf
- Department of Imaging Physics, The University of Texas Anderson Cancer Center, Houston, TX 77030, USA
| | - D Fuentes
- Department of Imaging Physics, The University of Texas Anderson Cancer Center, Houston, TX 77030, USA
| | - A Morshid
- Department of Imaging Physics, The University of Texas Anderson Cancer Center, Houston, TX 77030, USA
| | - A O Kaseb
- Department of Gastrointestinal Oncology, The University of Texas Anderson Cancer Center, Houston, TX 77030, USA
| | - M Hassan
- Department of Gastrointestinal Oncology, The University of Texas Anderson Cancer Center, Houston, TX 77030, USA
| | - J D Hazle
- Department of Imaging Physics, The University of Texas Anderson Cancer Center, Houston, TX 77030, USA
| | - K M Elsayes
- Department of Diagnostic Radiology, The University of Texas Anderson Cancer Center, Houston, TX 77030, USA.
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Morshid A, Elsayes KM, Khalaf AM, Elmohr MM, Yu J, Kaseb AO, Hassan M, Mahvash A, Wang Z, Hazle JD, Fuentes D. A machine learning model to predict hepatocellular carcinoma response to transcatheter arterial chemoembolization. Radiol Artif Intell 2019; 1:e180021. [PMID: 31858078 PMCID: PMC6920060 DOI: 10.1148/ryai.2019180021] [Citation(s) in RCA: 76] [Impact Index Per Article: 12.7] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/10/2018] [Revised: 05/25/2019] [Accepted: 08/05/2019] [Indexed: 01/27/2023]
Abstract
PURPOSE Some patients with hepatocellular carcinoma (HCC) are more likely to experience disease progression despite transcatheter arterial chemoembolization (TACE) treatment, and thus would benefit from early switching to other therapeutic regimens. We sought to evaluate a fully automated machine learning algorithm that uses pre-therapeutic quantitative computed tomography (CT) image features and clinical factors to predict HCC response to TACE. MATERIALS AND METHODS Outcome information from 105 patients receiving first-line treatment with TACE was evaluated retrospectively. The primary clinical endpoint was time to progression (TTP) based on follow-up CT radiological criteria (mRECIST). A 14-week cutoff was used to classify patients as TACE-susceptible (TTP ≥14 weeks) or TACE-refractory (TTP <14 weeks). Response to TACE was predicted using a random forest classifier with the Barcelona Clinic Liver Cancer (BCLC) stage and quantitative image features as input as well as the BCLC stage alone as a control. RESULTS The model's response prediction accuracy rate was 74.2% (95% CI=64%-82%) using a combination of the BCLC stage plus quantitative image features versus 62.9% (95% CI= 52%-72%) using the BCLC stage alone. Shape image features of the tumor and background liver were the dominant features correlated to the TTP as selected by the Boruta method and were used to predict the outcome. CONCLUSION This preliminary study demonstrates that quantitative image features obtained prior to therapy can improve the accuracy of predicting response of HCC to TACE. This approach is likely to provide useful information for aiding HCC patient selection for TACE.
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Affiliation(s)
- Ali Morshid
- From the Departments of Imaging Physics (A. Morshid, A.M.K., M.M.E., J.Y., J.D.H., D.F.), Diagnostic Radiology (K.M.E.), Gastrointestinal Oncology (A.O.K., M.H.), and Interventional Radiology (A. Mahvash), The University of Texas MD Anderson Cancer Center, 1515 Holcombe Blvd, Houston, TX 77030; and Brown Foundation Institute of Molecular Medicine, McGovern Medical School, The University of Texas Health Science Center at Houston, Houston, Tex (Z.W.)
| | - Khaled M. Elsayes
- From the Departments of Imaging Physics (A. Morshid, A.M.K., M.M.E., J.Y., J.D.H., D.F.), Diagnostic Radiology (K.M.E.), Gastrointestinal Oncology (A.O.K., M.H.), and Interventional Radiology (A. Mahvash), The University of Texas MD Anderson Cancer Center, 1515 Holcombe Blvd, Houston, TX 77030; and Brown Foundation Institute of Molecular Medicine, McGovern Medical School, The University of Texas Health Science Center at Houston, Houston, Tex (Z.W.)
| | - Ahmed M. Khalaf
- From the Departments of Imaging Physics (A. Morshid, A.M.K., M.M.E., J.Y., J.D.H., D.F.), Diagnostic Radiology (K.M.E.), Gastrointestinal Oncology (A.O.K., M.H.), and Interventional Radiology (A. Mahvash), The University of Texas MD Anderson Cancer Center, 1515 Holcombe Blvd, Houston, TX 77030; and Brown Foundation Institute of Molecular Medicine, McGovern Medical School, The University of Texas Health Science Center at Houston, Houston, Tex (Z.W.)
| | - Mohab M. Elmohr
- From the Departments of Imaging Physics (A. Morshid, A.M.K., M.M.E., J.Y., J.D.H., D.F.), Diagnostic Radiology (K.M.E.), Gastrointestinal Oncology (A.O.K., M.H.), and Interventional Radiology (A. Mahvash), The University of Texas MD Anderson Cancer Center, 1515 Holcombe Blvd, Houston, TX 77030; and Brown Foundation Institute of Molecular Medicine, McGovern Medical School, The University of Texas Health Science Center at Houston, Houston, Tex (Z.W.)
| | - Justin Yu
- From the Departments of Imaging Physics (A. Morshid, A.M.K., M.M.E., J.Y., J.D.H., D.F.), Diagnostic Radiology (K.M.E.), Gastrointestinal Oncology (A.O.K., M.H.), and Interventional Radiology (A. Mahvash), The University of Texas MD Anderson Cancer Center, 1515 Holcombe Blvd, Houston, TX 77030; and Brown Foundation Institute of Molecular Medicine, McGovern Medical School, The University of Texas Health Science Center at Houston, Houston, Tex (Z.W.)
| | - Ahmed O. Kaseb
- From the Departments of Imaging Physics (A. Morshid, A.M.K., M.M.E., J.Y., J.D.H., D.F.), Diagnostic Radiology (K.M.E.), Gastrointestinal Oncology (A.O.K., M.H.), and Interventional Radiology (A. Mahvash), The University of Texas MD Anderson Cancer Center, 1515 Holcombe Blvd, Houston, TX 77030; and Brown Foundation Institute of Molecular Medicine, McGovern Medical School, The University of Texas Health Science Center at Houston, Houston, Tex (Z.W.)
| | - Manal Hassan
- From the Departments of Imaging Physics (A. Morshid, A.M.K., M.M.E., J.Y., J.D.H., D.F.), Diagnostic Radiology (K.M.E.), Gastrointestinal Oncology (A.O.K., M.H.), and Interventional Radiology (A. Mahvash), The University of Texas MD Anderson Cancer Center, 1515 Holcombe Blvd, Houston, TX 77030; and Brown Foundation Institute of Molecular Medicine, McGovern Medical School, The University of Texas Health Science Center at Houston, Houston, Tex (Z.W.)
| | - Armeen Mahvash
- From the Departments of Imaging Physics (A. Morshid, A.M.K., M.M.E., J.Y., J.D.H., D.F.), Diagnostic Radiology (K.M.E.), Gastrointestinal Oncology (A.O.K., M.H.), and Interventional Radiology (A. Mahvash), The University of Texas MD Anderson Cancer Center, 1515 Holcombe Blvd, Houston, TX 77030; and Brown Foundation Institute of Molecular Medicine, McGovern Medical School, The University of Texas Health Science Center at Houston, Houston, Tex (Z.W.)
| | - Zhihui Wang
- From the Departments of Imaging Physics (A. Morshid, A.M.K., M.M.E., J.Y., J.D.H., D.F.), Diagnostic Radiology (K.M.E.), Gastrointestinal Oncology (A.O.K., M.H.), and Interventional Radiology (A. Mahvash), The University of Texas MD Anderson Cancer Center, 1515 Holcombe Blvd, Houston, TX 77030; and Brown Foundation Institute of Molecular Medicine, McGovern Medical School, The University of Texas Health Science Center at Houston, Houston, Tex (Z.W.)
| | - John D. Hazle
- From the Departments of Imaging Physics (A. Morshid, A.M.K., M.M.E., J.Y., J.D.H., D.F.), Diagnostic Radiology (K.M.E.), Gastrointestinal Oncology (A.O.K., M.H.), and Interventional Radiology (A. Mahvash), The University of Texas MD Anderson Cancer Center, 1515 Holcombe Blvd, Houston, TX 77030; and Brown Foundation Institute of Molecular Medicine, McGovern Medical School, The University of Texas Health Science Center at Houston, Houston, Tex (Z.W.)
| | - David Fuentes
- From the Departments of Imaging Physics (A. Morshid, A.M.K., M.M.E., J.Y., J.D.H., D.F.), Diagnostic Radiology (K.M.E.), Gastrointestinal Oncology (A.O.K., M.H.), and Interventional Radiology (A. Mahvash), The University of Texas MD Anderson Cancer Center, 1515 Holcombe Blvd, Houston, TX 77030; and Brown Foundation Institute of Molecular Medicine, McGovern Medical School, The University of Texas Health Science Center at Houston, Houston, Tex (Z.W.)
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Kim H, Kim Y, Han B, Jang JY, Kim Y. Clinically Applicable Deep Learning Algorithm Using Quantitative Proteomic Data. J Proteome Res 2019; 18:3195-3202. [DOI: 10.1021/acs.jproteome.9b00268] [Citation(s) in RCA: 10] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/21/2022]
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Method Validation by CPTAC Guidelines for Multi-protein Marker Assays Using Multiple Reaction Monitoring-mass Spectrometry. BIOTECHNOL BIOPROC E 2019. [DOI: 10.1007/s12257-018-0454-7] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/19/2022]
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Lee J, Kim H, Sohn A, Yeo I, Kim Y. Cost-Effective Automated Preparation of Serum Samples for Reproducible Quantitative Clinical Proteomics. J Proteome Res 2019; 18:2337-2345. [PMID: 30985128 DOI: 10.1021/acs.jproteome.9b00023] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/27/2022]
Abstract
Reproducible sample preparation remains a significant challenge in large-scale clinical research using selected reaction monitoring-mass spectrometry (SRM-MS), which enables a highly sensitive multiplexed assay. Although automated liquid-handling platforms have tremendous potential for addressing this issue, the high cost of their consumables is a drawback that renders routine operation expensive. Here we evaluated the performance of a liquid-handling platform in preparing serum samples compared with a standard experiment while reducing the outlay for consumables, such as tips, wasted reagents, and reagent stock plates. A total of 26 multiplex assays were quantified by SRM-MS using four sets of 24 pooled human serum aliquots; the four sets used a fixed number (1, 4, 8, or 24) of tips to dispense digestion reagents. This study demonstrated that the use of 4 or 8 tips is comparable to 24 tips (standard experiment), as evidenced by their coefficients of variation: 13.5% (for 4 and 8 tips) versus 12.0% (24 tips). Thus we can save 37% of the total experimental cost compared with the standard experiment, maintaining nearly equivalent reproducibility. The routine operation of cost-effective liquid-handling platforms can enable researchers to process large-scale samples with high throughput, adding credibility to their findings by minimizing human error.
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Affiliation(s)
| | - Hyunsoo Kim
- Institute of Medical and Biological Engineering, MRC , Seoul National University , Seoul , Korea
| | | | - Injoon Yeo
- Interdisciplinary Program of Bioengineering , Seoul National University College of Engineering , Seoul , Korea
| | - Youngsoo Kim
- Institute of Medical and Biological Engineering, MRC , Seoul National University , Seoul , Korea.,Interdisciplinary Program of Bioengineering , Seoul National University College of Engineering , Seoul , Korea
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Han B, Min H, Jeon M, Kang B, Son J. A rapid non‐target screening method for determining prohibited substances in human urine using liquid chromatography/high‐resolution tandem mass spectrometry. Drug Test Anal 2018; 11:382-391. [DOI: 10.1002/dta.2495] [Citation(s) in RCA: 15] [Impact Index Per Article: 2.1] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/14/2018] [Revised: 08/27/2018] [Accepted: 09/01/2018] [Indexed: 12/23/2022]
Affiliation(s)
- Boyoung Han
- Doping Control CenterKorea Institute of Science and Technology Seoul South Korea
| | - Hophil Min
- Doping Control CenterKorea Institute of Science and Technology Seoul South Korea
| | - Mijin Jeon
- Doping Control CenterKorea Institute of Science and Technology Seoul South Korea
| | - Byeori Kang
- Doping Control CenterKorea Institute of Science and Technology Seoul South Korea
| | - Junghyun Son
- Doping Control CenterKorea Institute of Science and Technology Seoul South Korea
- Department of Biological ChemistryKorea University of Science and Technology (UST) Daejeon Republic of Korea
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Fully validated SRM-MS-based method for absolute quantification of PIVKA-II in human serum: Clinical applications for patients with HCC. J Pharm Biomed Anal 2018; 156:142-146. [DOI: 10.1016/j.jpba.2018.04.025] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.1] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/08/2018] [Revised: 03/28/2018] [Accepted: 04/16/2018] [Indexed: 12/14/2022]
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