From Differential Expression to Interaction Networks: Uncovering Hub Genes in Hepatocellular Carcinoma

Objective: Hepatocellular carcinoma (HCC) is a molecularly heterogeneous malignancy with high mortality. This study aimed to identify differentially expressed genes (DEGs) between tumor and non-tumor liver tissue and, through protein–protein interaction (PPI) network analysis, to reveal hub genes central to HCC pathogenesis. Materials and Methods: Using the GSE76427 dataset from the Gene Expression Omnibus, 115 HCC tumor tissues were compared with 52 non-tumor liver tissues. Differential expression analysis was performed with the limma package (adj. P. Val < 0.05, |logFC| ≥ 1). The most strongly dysregulated genes were submitted to the STRING database to construct a PPI network, and the network topology was evaluated. Results: A total of 362 probes were differentially expressed (95 up-regulated, 267 down-regulated). The roughly three-fold excess of down-regulated genes reflects loss of liver-specific metabolic and detoxification programs. PPI analysis revealed a network significantly richer in interactions than expected (enrichment p = 4.67 × 10⁻⁵); the cell-cycle– and mitosis-related proteins PCLAF, PRC1, MCM2, UBE2T and TUBA1C formed a tightly connected proliferation module that constituted the network core, whereas down-regulated genes associated with sinusoidal endothelium, innate immunity and hepatocyte metabolism appeared largely as isolated nodes. Conclusion: The findings indicate that the HCC phenotype is driven mainly by a proliferation-centered interaction network accompanied by loss of liver-specific differentiation. The hub proteins within this proliferation module stand out as candidate biomarkers and therapeutic targets for HCC.