Integrated transcriptomic and network biology analysis reveals druggable hub genes as candidate therapeutic targets in hepatocellular carcinoma

Abstract Background Hepatocellular carcinoma (HCC) remains one of the most lethal malignancies worldwide, with survival improvements lagging behind other solid tumors. Its molecular heterogeneity and complex signaling architecture continue to limit the success of targeted therapies. To address this challenge, we applied an integrated transcriptomic and systems biology framework to identify key oncogenic drivers, characterize their functional roles, and evaluate their potential as druggable therapeutic targets. Methods Gene expression data from the GEO dataset GSE101685 were processed using R/Bioconductor pipelines with MAS5.0 normalization. Differentially expressed genes (DEGs) were identified using the limma package with adjusted p < 0.05 and |log₂FC|≥ 1.5. Protein–protein interaction (PPI) networks were reconstructed via STRING and analyzed in Cytoscape to identify hub genes using multiple centrality metrics. Network clustering was performed with the Louvain algorithm. Functional annotation and pathway enrichment were conducted using Enrichr. Clinical significance was assessed through GEPIA and Human Protein Atlas survival data. Druggability of top hub genes was examined using DGIdb, Pharos, and PockDrug. Results We identified 5562 DEGs, including 748 upregulated genes. Centrality analysis revealed 106 hub genes, with AURKA, AURKB, TOP2A, PLK1, CDC6, CCNA2, CDC20, CCNB1, CDK1, BUB1, CDC45, and KIF23 emerging as top candidates. These genes were strongly enriched in pathways involved in cell cycle regulation, p53 signaling, DNA replication, and cellular senescence. Survival analyses demonstrated consistently poorer outcomes among patients with high expression of these genes. Druggability assessment highlighted several promising targets, and computational predictions suggested previously underexplored druggable pockets for CDC6, CDC20, and CDC45. Conclusion Our integrated systems analysis identifies a focused panel of druggable hub genes that may serve as promising therapeutic targets in HCC, providing a foundation for future experimental validation and rational drug design.