Identification of mitochondria-related biomarkers in liver fibrosis via interpretable machine learning and WGCNA: transcriptomic analysis and

Background: Hepatic fibrosis is a key pathological stage in the progression of many chronic liver diseases; timely intervention is critical to preventing cirrhosis and hepatocellular carcinoma. Mitochondria regulate energy metabolism, lipid homeostasis, and redox balance, and their dysfunction is increasingly recognized as a driver of fibrogenesis. Objective: To identify key mitochondria-related genes associated with liver fibrosis and explore their mechanistic roles and therapeutic potential using a multi-omics mining strategy. Methods: Fibrosis-related bulk RNA-seq datasets (GSE152329, GSE167216, GSE119953, GSE254610) and scRNA-seq datasets (GSE145086, GSE233084) were retrieved from GEO. WGCNA-derived modules were intersected with DEGs and a mitochondrial gene set to obtain candidate genes. GO and KEGG enrichment analyses were performed with clusterProfiler, and TF activity was inferred with decoupleR. An XGBoost algorithm was utilized to prioritize critical mitochondrial targets. Cell-cell communication was analyzed using CellChat. A CCl Results: Bulk RNA-seq and WGCNA identified 38 mitochondria-related DEGs in CCl Conclusions: Acot9 was identified as a key mitochondrial target associated with liver fibrosis. Its consistent upregulation in fibrotic liver tissue and, notably, the ACOT9-dependent modulation of fibrosis markers in hepatic stellate cells highlight its mechanistic relevance and potential as a therapeutic target for further study.