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

Abstract

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.

Fig. 1

Construction of coexpressed gene networks with weighted gene coexpression network analysis. A, The tumor (pink boxed) and nontumor (green boxed) samples were distinctly separated and clustered. B, In total, five coexpression gene networks were inferred by the analysis.

Fig. 2

Clinical correlation of the gene coexpression networks. A, Correlation between coexpressed gene modules and stage of hepatocellular carcinoma (HCC). Here, X axis is the tumor-node-metastasis stage, and the Y axis is the module eigengene (ME). (B) Kaplan-Meier analysis showed that the coexpressed gene network turquoise was significantly correlated with HCC prognosis.

Fig. 3

Functional annotation and pathway enrichment analysis of the turquoise coexpressed gene network. BP = biological process; CC = cellular component; GO = gene ontology; MF = molecular function.

Fig. 4

Protein-protein interaction (PPI) network. A, The prognosis associated with the turquoise coexpressed gene network was utilized to construct a PPI network. Orange nodes are hub genes. B, The most connected cluster is composed of ten hub genes: ANLN, ASPM, CCNB1, CDK1, CDKN3, CENPF, ECT2, KIF4A, NEK2, and TOP2A. Color, MNC of nodes.

Fig. 5

Validation of hub genes in the TCGA-LIHC dataset. A, Gene expression of each of the 10 hub genes. The hub genes were upregulated in tumor samples vs nontumor samples. *p < 0.01l; red, tumor group; gray, nontumor group; TPM, transcripts per million. B, Kaplan-Meier analysis of each hub gene for overall survival. * p < 0.01; ** <0.001. C, Survival analysis showed that lower expression of the 10-gene signature had better overall survival. D, A lower expression in the 10-gene signature had better recurrence-free survival. The blue line is the lower expression group, and the red line is the higher expression group. Dotted line, 95% CI.