Identification of signature gene set as highly accurate determination of metabolic dysfunction-associated steatotic liver disease progression
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Background/Aims: Metabolic dysfunction-associated steatotic liver disease (MASLD) is characterized by fat accumulation in the liver. MASLD encompasses both steatosis and MASH. Since MASH can lead to cirrhosis and liver cancer, steatosis and MASH must be distinguished during patient treatment. Here, we investigate the genomes, epigenomes, and transcriptomes of MASLD patients to identify signature gene set for more accurate tracking of MASLD progression. Methods: Biopsy-tissue and blood samples from patients with 134 MASLD, comprising 60 steatosis and 74 MASH patients were performed omics analysis. SVM learning algorithm were used to calculate most predictive features. Linear regression was applied to find signature gene set that distinguish the stage of MASLD and to validate their application into independent cohort of MASLD. Results: After performing WGS, WES, WGBS, and total RNA-seq on 134 biopsy samples from confirmed MASLD patients, we provided 1,955 MASLD-associated features, out of 3,176 somatic variant callings, 58 DMRs, and 1,393 DEGs that track MASLD progression. Then, we used a SVM learning algorithm to analyze the data and select the most predictive features. Using linear regression, we identified a signature gene set capable of differentiating the various stages of MASLD and verified it in different independent cohorts of MASLD and a liver cancer cohort. Conclusions: We identified a signature gene set (i.e., CAPG, HYAL3, WIPI1, TREM2, SPP1, and RNASE6) with strong potential as a panel of diagnostic genes of MASLD-associated disease.
Abstract
Background/Aims: Metabolic dysfunction-associated steatotic liver disease (MASLD) is characterized by fat accumulation in the liver. MASLD encompasses both steatosis and MASH. Since MASH can lead to cirrhosis and liver cancer, steatosis and MASH must be distinguished during patient treatment. Here, we investigate the genomes, epigenomes, and transcriptomes of MASLD patients to identify signature gene set for more accurate tracking of MASLD progression. Methods: Biopsy-tissue and blood samples from patients with 134 MASLD, comprising 60 steatosis and 74 MASH patients were performed omics analysis. SVM learning algorithm were used to calculate most predictive features. Linear regression was applied to find signature gene set that distinguish the stage of MASLD and to validate their application into independent cohort of MASLD. Results: After performing WGS, WES, WGBS, and total RNA-seq on 134 biopsy samples from confirmed MASLD patients, we provided 1,955 MASLD-associated features, out of 3,176 somatic variant callings, 58 DMRs, and 1,393 DEGs that track MASLD progression. Then, we used a SVM learning algorithm to analyze the data and select the most predictive features. Using linear regression, we identified a signature gene set capable of differentiating the various stages of MASLD and verified it in different independent cohorts of MASLD and a liver cancer cohort. Conclusions: We identified a signature gene set (i.e., CAPG, HYAL3, WIPI1, TREM2, SPP1, and RNASE6) with strong potential as a panel of diagnostic genes of MASLD-associated disease.
참고문헌 (30)
- Eslam M, "MAFLD : A consensus-driven proposed nomenclature for metabolic associated fatty liver disease", Gastroenterology, 158, pp.1999~2014 (2020)
- Badmus OO, "Molecular mechanisms of metabolic associated fatty liver disease(MAFLD) : functional analysis of lipid metabolism pathways", Clin Sci(Lond), 136, pp.1347~1366 (2022)
- Yew KC, "Letter regarding"Waiting for the changes after the adoption of steatotic liver disease"", Clin Mol Hepatol, 30, pp.118~120 (2024)
- Nassir F, "Pathogenesis and prevention of hepatic steatosis", Gastroenterol Hepatol(N Y), 11, pp.167~175 (2015)
- Mazzolini G, "Significance of simple steatosis : An update on the clinical and molecular evidence", Cells, 9, pp.2458~ (2020)
- Kim GA, "Critical appraisal of metabolic dysfunction-associated steatotic liver disease : Implication of Janusfaced modernity", Clin Mol Hepatol, 29, pp.831~843 (2023)
- Younossi ZM, "Non-alcoholic fatty liver disease-A global public health perspective", J Hepatol, 70, pp.531~544 (2019)
- Maurice J, "Non-alcoholic fatty liver disease", Clin Med(Lond), 18, pp.245~250 (2018)
- Feng G, "Recompensation in cirrhosis : unravelling the evolving natural history of nonalcoholic fatty liver disease", Nat Rev Gastroenterol Hepatol, 21, pp.46~56 (2024)
- Hoang SA, "Gene expression predicts histological severity and reveals distinct molecular profiles of nonalcoholic fatty liver disease", Sci Rep, 9, pp.12541~ (2019)
- Alkhouri N, "Noninvasive diagnosis of NASH and liver fibrosis within the spectrum of NAFLD", Gastroenterol Hepatol(N Y), 8, pp.661~668 (2012)
- Li G, "Non-invasive tests of non-alcoholic fatty liver disease", Chin Med J(Engl), 135, pp.532~546 (2022)
- Oh S, "From genome sequencing to the discovery of potential biomarkers in liver disease", BMB Rep, 53, pp.299~310 (2020)
- Sliz E, "NAFLD risk alleles in PNPLA3, TM6SF2, GCKR and LYPLAL1 show divergent metabolic effects", Hum Mol Genet, 27, pp.2214~2223 (2018)
- Yoo T, "Diseasespecific eQTL screening reveals an anti-fibrotic effect of AGXT2in non-alcoholic fatty liver disease", J Hepatol, 75, pp.514~523 (2021)
- Atanasovska B, "Long non-coding rnas involved in progression of non-alcoholic fatty liver disease to steatohepatitis", Cells, 10, pp.1883~ (2021)
- Govaere O, "Transcriptomic profiling across the nonalcoholic fatty liver disease spectrum reveals gene signatures for steatohepatitis and fibrosis", Sci Transl Med, 12, pp.eaba4448~ (2020)
- Loomba R, "DNA methylation signatures reflect aging in patients with nonalcoholic steatohepatitis", JCI Insight, 3, pp.e96685~ (2018)
- Ma J, "A peripheral blood DNA methylation signature of hepatic fat reveals a potential causal pathway for nonalcoholic fatty liver disease", Diabetes, 68, pp.1073~1083 (2019)
- Sterling RK, "Development of a simple noninvasive index to predict significant fibrosis in patients with HIV", HCV coinfection, 2006, pp.43, 1317~1325
- Lee JH, "Hepatic steatosis index : a simple screening tool reflecting nonalcoholic fatty liver disease", Dig Liver Dis, 42, pp.503~508 (2010)
- Angulo P, "The NAFLD fibrosis score : a noninvasive system that identifies liver fibrosis in patients with NAFLD", Hepatology, 45, pp.846~854 (2007)
- Liu G, "Potential diagnostic and prognostic marker dimethylglycine dehydrogenase(DMGDH)suppresses hepatocellular carcinoma metastasis in vitro and in vivo", Oncotarget, 7, pp.32607~32616 (2016)
- Kang B, "The chromatin accessibility landscape of nonalcoholic fatty liver disease progression", Mol Cells, 45, pp.343~352 (2022)
- Jiang F, "Artificial intelligence in healthcare : past, present and future", Stroke Vasc Neurol, 2, pp.230~243 (2017)
- Heinemann F, "Deep learning enables pathologist-like scoring of NASH models", Sci Rep, 9, pp.18454~ (2019)
- Palekar NA, "Clinical model for distinguishing nonalcoholic steatohepatitis from simple steatosis in patients with nonalcoholic fatty liver disease", Liver Int, 26, pp.151~156 (2006)
- Castañé H, "Coupling machine learning and lipidomics as a tool to investigate metabolic dysfunction-associated fatty liver disease. A general overview", Biomolecules, 11, pp.473~ (2021)
- Hou C, "Bioinformatics analysis of key differentially expressed genes in nonalcoholic fatty liver disease mice models", Gene Expr, 19, pp.25~35 (2018)
- Docherty M, "Development of a novel machine learning model to predict presence of nonalcoholic steatohepatitis", J Am Med Inform Assoc, 28, pp.1235~1241 (2021)