Hepatocellular carcinoma (HCC) is a leading cause of cancer mortality, often diagnosed at advanced stages where treatment options are limited. This study undertakes a comprehensive meta-analysis of gene expression profiles from 19 independent datasets sourced from the Gene Expression Omnibus (GEO), encompassing a diverse range of HCC etiologies, including HBV and HCV infections, cirrhosis, and normal liver comparisons. Our analysis identified 125 genes consistently altered across all datasets (e.g., , , ) that represent a pan-etiology HCC signature, implicating retinol metabolism and solute transport as key pathways in HCC pathogenesis. Notably, 14 HBV-specific differentially expressed genes (DEGs) (e.g., , ) and 221 HCV-specific DEGs (e.g., , ) were identified, highlighting etiology-specific molecular signatures. Protein-protein interaction (PPI) networks revealed central hubs (e.g., CDK1, CCNE1, TYMS) involved in cell cycle dysregulation and metabolic reprogramming (Warburg effect). These findings provide a robust molecular framework for HCC subtyping and prioritize novel biomarkers and therapeutic targets for further validation. This resource advances the potential for personalized HCC diagnostics and therapies.