Transcriptomic Analysis of Shared Lipid Metabolism in Chronic Hepatitis B and Non‐Alcoholic Fatty Liver Disease Using Weighted Gene Expression Network Analysis

Chronic hepatitis B (CHB) and non-alcoholic fatty liver disease (NAFLD) frequently coexist, and lipid metabolic dysregulation may represent a shared biological feature between these conditions. However, the molecular mechanisms linking these conditions remain poorly defined. Transcriptomic datasets of CHB and NAFLD were analyzed to identify differentially expressed genes, followed by weighted gene co-expression network analysis (WGCNA), functional enrichment, and machine-learning-based feature selection. Gene set variation analysis (GSVA) was performed to assess pathway activity, and experimental validation was conducted in an FFA-induced steatosis model using HepG2 cells. Shared genes and lipid metabolism-related modules common to both CHB and NAFLD were identified. Functional enrichment revealed pathways associated with lipid metabolism and cellular stress regulation. Machine-learning analysis highlighted TRIM35 and MPP1 as potential key genes, which were further validated in vitro as upregulated in steatotic conditions. This integrative approach advances understanding of shared lipid metabolic features between CHB and NAFLD and suggests potential candidate genes for future investigation in disease diagnosis and therapy.