Identification of potential metabolic biomarkers and immune cell infiltration for metabolic associated steatohepatitis by bioinformatics analysis and machine learning

• Published in: Scientific reports Vol. 15; no. 1; pp. 16596 - 15
• Main Authors: Xie, Haoran, Wang, Junjun, Zhao, Qiuyan
• Format: Journal Article
• Language: English
• Published: London Nature Publishing Group UK 13.05.2025; Nature Publishing Group; Nature Portfolio
• Subjects: 631/208/199; 692/4020/4021; Algorithms; Bioinformatics; Biomarkers; Biomarkers - metabolism; Biopsy; CD8 antigen; Cirrhosis; Computational Biology - methods; Correlation analysis; Diagnosis; Fatty liver; Fatty Liver - diagnosis; Fatty Liver - genetics; Fatty Liver - immunology; Fatty Liver - metabolism; Gene Expression Profiling; Gene set enrichment analysis; Genes; Glucose metabolism; Glycerol-3-phosphate dehydrogenase; Hepatocellular carcinoma; Humanities and Social Sciences; Humans; Infiltration; Invasiveness; Learning algorithms; Lipid metabolism; Liver diseases; Lymphocytes T; Machine Learning; Metabolic associated steatohepatitis; Metabolism; multidisciplinary; Natural killer cells; Non-alcoholic Fatty Liver Disease - diagnosis; Non-alcoholic Fatty Liver Disease - genetics; Non-alcoholic Fatty Liver Disease - immunology; Non-alcoholic Fatty Liver Disease - metabolism; Protein interaction; Protein Interaction Maps; Protein turnover; ROC Curve; Science; Science (multidisciplinary); Biomarkers; Metabolism; Metabolic associated steatohepatitis; Bioinformatics; Machine learning

Background: Metabolic associated steatohepatitis (MASH) represents a severe subtype of metabolic associated fatty liver disease (MASLD), with an increased risk of progression to cirrhosis and hepatocellular carcinoma. The nomenclature shift from nonalcoholic steatohepatitis (NASH)/nonalcoholic fatty...