Network-based analysis of candidate oncogenes and pathways in hepatocellular carcinoma.

Hepatocellular carcinoma (HCC) is a major worldwide health burden due to poor outcomes. Identifying dysregulated molecular circuits in HCC is critical for developing precise treatments. A systems-level approach using multi-omics data is required to reveal the intricate non-linear interactions underlying liver carcinogenesis. Both tumor and control tissues contained differentially expressed genes (DEGs). Hub genes with the strongest connection were identified as potential drivers. Protein-protein interaction (PPI) mapping verified hub connectivity. Perturbed functions were evaluated using Gene Ontology and KEGG pathway enrichment analysis. Cytoscape clustering separated the interactome into modules. Motif discovery indicated a shift in -regulatory logic. Expression analysis, survival analysis, and drug screening were performed on the hub genes. Network hub gene analysis identified 11 hub genes, including DLGAP5, KIF23, KIF11, CCNB1, CDK1, BRCA1, CCNA2, SHCBP1, KIAA0101, FAM83D, and SPC25. Gene set enrichment analysis (GSEA) revealed dysregulation in cell cycle progression, DNA damage response, and metabolic pathways, and an association of these genes with reduced overall survival in HCC patients. Also, drug screening identified potential therapeutic agents targeting these hub genes.The findings increase mechanistic understanding with potential clinical applications. Future validation studies that include multi-omic data may strengthen current hypotheses and enable targeted therapy design against crucial in HCC.