Identification of hub genes associated with hepatitis B virus-related hepatocellular cancer using weighted gene co-expression network analysis and protein-protein interaction network analysis

Background. Chronic hepatitis B virus (HBV) infection is the main pathogen of hepatocellular carcinoma. However, the mechanisms of HBV-related hepatocellular carcinoma (HCC) progression are practically unknown. Materials and Methods. The results of RNA-sequence and clinical data for GSE121248 and GSE17548 were accessed from the Gene Expression Omnibus data library. We screened Sangerbox 3.0 for differentially expressed genes (DEGs). The weighted gene co-expression network analysis (WGCNA) was employed to select core modules and hub genes, and protein-protein interaction network module analysis also played a significant part in it. Validation was performed using RNA-sequence data of cancer and normal tissues of HBV-related HCC patients in the cancer genome atlas-liver hepatocellular cancer database (TCGA-LIHC). Results. 787 DEGs were identified from GSE121248 and 772 DEGs were identified from GSE17548. WGCNA analysis indicated that black modules (99 genes) and grey modules (105 genes) were significantly associated with HBV-related HCC. Gene ontology analysis found that there is a direct correlation between DEGs and the regulation of cell movement and adhesion; the internal components and external packaging structure of plasma membrane; signaling receptor binding, calcium ion binding, etc. Kyoto Encyclopedia of Genes and Genomes pathway analysis found out the association between cytokine receptors, cytokine-cytokine receptor interactions, and viral protein interactions with cytokines were important and HBV-related HCC. Finally, we further validated 6 key genes including C7, EGR1, EGR3, FOS, FOSB, and prostaglandin-endoperoxide synthase 2 by using the TCGA-LIHC. Conclusions. We identified 6 hub genes as candidate biomarkers for HBV-related HCC. These hub genes may act as an essential part of HBV-related HCC progression.