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Huang G, Wallace DF, Powell EE, Rahman T, Clark PJ, Subramaniam VN. Gene Variants Implicated in Steatotic Liver Disease: Opportunities for Diagnostics and Therapeutics. Biomedicines 2023; 11:2809. [PMID: 37893185 PMCID: PMC10604560 DOI: 10.3390/biomedicines11102809] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/14/2023] [Revised: 10/05/2023] [Accepted: 10/09/2023] [Indexed: 10/29/2023] Open
Abstract
Non-alcoholic fatty liver disease (NAFLD) describes a steatotic (or fatty) liver occurring as a consequence of a combination of metabolic, environmental, and genetic factors, in the absence of significant alcohol consumption and other liver diseases. NAFLD is a spectrum of conditions. Steatosis in the absence of inflammation is relatively benign, but the disease can progress into more severe forms like non-alcoholic steatohepatitis (NASH), liver cirrhosis, and hepatocellular carcinoma. NAFLD onset and progression are complex, as it is affected by many risk factors. The interaction between genetic predisposition and other factors partially explains the large variability of NAFLD phenotype and natural history. Numerous genes and variants have been identified through large-scale genome-wide association studies (GWAS) that are associated with NAFLD and one or more subtypes of the disease. Among them, the largest effect size and most consistent association have been patatin-like phospholipase domain-containing protein 3 (PNPLA3), transmembrane 6 superfamily member 2 (TM6SF2), and membrane-bound O-acyltransferase domain containing 7 (MBOAT7) genes. Extensive in vitro and in vivo studies have been conducted on these variants to validate these associations. The focus of this review is to highlight the genetics underpinning the molecular mechanisms driving the onset and progression of NAFLD and how they could potentially be used to improve genetic-based diagnostic testing of the disease and develop personalized, targeted therapeutics.
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Affiliation(s)
- Gary Huang
- Hepatogenomics Research Group, Queensland University of Technology (QUT), Brisbane, QLD 4059, Australia;
- Centre for Genomics and Personalised Health, Queensland University of Technology (QUT), Brisbane, QLD 4059, Australia;
- School of Biomedical Sciences, Queensland University of Technology (QUT), Brisbane, QLD 4059, Australia
| | - Daniel F. Wallace
- Centre for Genomics and Personalised Health, Queensland University of Technology (QUT), Brisbane, QLD 4059, Australia;
- School of Biomedical Sciences, Queensland University of Technology (QUT), Brisbane, QLD 4059, Australia
- Metallogenomics Laboratory, Queensland University of Technology (QUT), Brisbane, QLD 4059, Australia
| | - Elizabeth E. Powell
- QIMR Berghofer Medical Research Institute, Brisbane, QLD 4006, Australia;
- Department of Gastroenterology and Hepatology, Princess Alexandra Hospital, Brisbane, QLD 4102, Australia
- Centre for Liver Disease Research, Translational Research Institute, Faculty of Medicine, The University of Queensland, Brisbane, QLD 4101, Australia
| | - Tony Rahman
- Department of Gastroenterology and Hepatology, Prince Charles Hospital, Brisbane, QLD 4032, Australia;
| | - Paul J. Clark
- Mater Adult Hospital, Faculty of Medicine, The University of Queensland, Brisbane, QLD 4101, Australia;
| | - V. Nathan Subramaniam
- Hepatogenomics Research Group, Queensland University of Technology (QUT), Brisbane, QLD 4059, Australia;
- Centre for Genomics and Personalised Health, Queensland University of Technology (QUT), Brisbane, QLD 4059, Australia;
- School of Biomedical Sciences, Queensland University of Technology (QUT), Brisbane, QLD 4059, Australia
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Xu Q, Feng M, Ren Y, Liu X, Gao H, Li Z, Su X, Wang Q, Wang Y. From NAFLD to HCC: Advances in noninvasive diagnosis. Biomed Pharmacother 2023; 165:115028. [PMID: 37331252 DOI: 10.1016/j.biopha.2023.115028] [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: 05/13/2023] [Revised: 06/10/2023] [Accepted: 06/14/2023] [Indexed: 06/20/2023] Open
Abstract
Non-alcoholic fatty liver disease (NAFLD) has gradually become one of the major liver health problems in the world. The dynamic course of the disease goes through steatosis, inflammation, fibrosis, and carcinoma. Before progressing to carcinoma, timely and effective intervention will make the condition better, which highlights the importance of early diagnosis. With the further study of the biological mechanism in the pathogenesis and progression of NAFLD, some potential biomarkers have been discovered, and the possibility of their clinical application is gradually being discussed. At the same time, the progress of imaging technology and the emergence of new materials and methods also provide more possibilities for the diagnosis of NAFLD. This article reviews the diagnostic markers and advanced diagnostic methods of NAFLD in recent years.
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Affiliation(s)
- Qinchen Xu
- Department of Clinical Laboratory, The Second Hospital, Cheeloo College of Medicine, Shandong University, Jinan, Shandong 250033, China
| | - Maoxiao Feng
- Department of Clinical Laboratory, Shandong Provincial Hospital Affiliated to Shandong First Medical University, 250021, Jinan, Shandong Province, China
| | - Yidan Ren
- Department of Clinical Laboratory, The Second Hospital, Cheeloo College of Medicine, Shandong University, Jinan, Shandong 250033, China
| | - Xiaoyan Liu
- Department of Clinical Laboratory, Shandong Provincial Hospital Affiliated to Shandong First Medical University, 250021, Jinan, Shandong Province, China
| | - Huiru Gao
- Department of Clinical Laboratory, The Second Hospital, Cheeloo College of Medicine, Shandong University, Jinan, Shandong 250033, China
| | - Zigan Li
- Department of Clinical Laboratory, The Second Hospital, Cheeloo College of Medicine, Shandong University, Jinan, Shandong 250033, China
| | - Xin Su
- Department of Clinical Laboratory, The Second Hospital, Cheeloo College of Medicine, Shandong University, Jinan, Shandong 250033, China
| | - Qin Wang
- Department of Anesthesiology, Qilu Hospital, Shandong University, 107 Wenhua Xi Road, Jinan 250012, China.
| | - Yunshan Wang
- Department of Clinical Laboratory, Shandong Provincial Hospital Affiliated to Shandong First Medical University, 250021, Jinan, Shandong Province, China.
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Identification of Key Genes in the HBV-Related HCC Immune Microenvironment Using Integrated Bioinformatics Analysis. JOURNAL OF ONCOLOGY 2022; 2022:2797033. [DOI: 10.1155/2022/2797033] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Received: 09/08/2022] [Accepted: 09/28/2022] [Indexed: 12/24/2022]
Abstract
Purpose. Hepatocellular carcinoma (HCC) has poor prognosis and high mortality among gastrointestinal tumors because of its insidious onset and strong invasiveness. However, there was little understanding of their pathogenesis. The purpose of this study was to use bioinformatics analysis to identify genes associated with the immune microenvironment in HBV-related HCC and to develop new therapeutic targets to prevent and treat cancer. Methods. RNA-seq data of HBV-related HCC cases were downloaded from TCGA-LIHC database. ESTIMATE and Deseq2 algorithms were used to screen out differentially expressed genes (DEGs). WGCNA was used to construct gene coexpression networks. In key modules, functional enrichment analysis was performed. Protein-protein interaction (PPI) was used to screen hub genes, and survival analysis was conducted to assess their prognostic significance. Following, we search for key genes differentially expressed between cancerous and paracancerous tissues in GSE136247 and GSE121248 datasets. Reveal the potential links between key genes in immune infiltration by using TIMER. Finally, in TCGA-LIHC database, integration of key genes with clinical data were used to further validate their correlation with prognosis. Results. In the cohort of HBV-related HCC patients, immune/stromal/ESTIMATE scores were not significantly associated with patient prognosis. After bioinformatics analysis, screening out five key genes was significantly related to the prognosis of HBV-related HCC. Downregulation of SLAMF1 and TRAF3IP3 suggested poor prognosis and was related to a variety of immune cell infiltration. Furthermore, compared with adjacent nontumor tissues, TRAF3IP3 and SLAMF1 were highly expressed in tumor tissues and were linked to tumor recurrences. Conclusion. In conclusion, SLAMF1 and TRAF3IP3 were identified with higher expression in tumor tissues and associated with tumor recurrence. It will be a new research direction of tumor progress and treatment.
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