Gene coexpression network analysis identifies hubs in hepatitis B virus-associated hepatocellular carcinoma

Background: Hepatocellular carcinoma (HCC) is among the leading causes of cancer-related death worldwide. The molecular pathogenesis of HCC involves multiple signaling pathways. This study utilizes systems and bioinformatic approaches to investigate the pathogenesis of HCC. Methods: Gene expression microarray data were obtained from 50 patients with chronic hepatitis B and HCC. There were 1649 differentially expressed genes inferred from tumorous and nontumorous datasets. Weighted gene coexpression network analysis (WGCNA) was performed to construct clustered coexpressed gene modules. Statistical analysis was used to study the correlation between gene coexpression networks and demographic features of patients. Functional annotation and pathway inference were explored for each coexpression network. Network analysis identified hub genes of the prognostic gene coexpression network. The hub genes were further validated with a public database. Result: Five distinct gene coexpression networks were identified by WGCNA. A distinct coexpressed gene network was significantly correlated with HCC prognosis. Pathway analysis of this network revealed extensive integration with cell cycle regulation. Ten hub genes of this gene network were inferred from protein-protein interaction network analysis and further validated in an external validation dataset. Survival analysis showed that lower expression of the 10-gene signature had better overall survival and recurrence-free survival. Conclusion: This study identified a crucial gene coexpression network associated with the prognosis of hepatitis B virus-related HCC. The identified hub genes may provide insights for HCC pathogenesis and may be potential prognostic markers or therapeutic targets.