Screening and Identification of Key Genes in Hepatitis B Virus-Related Hepatocellular Carcinoma Through an Integrated Bioinformatics Approach

Abstract

Objective: Primary liver cancer is one of the main causes of cancer mortality globally, with hepatocellular carcinoma (HCC) being the most frequent type. Chronic hepatitis B virus (HBV) infection is leading cause of HCC. This study aimed to identify significant genes for predicting prognosis in HBV-associated HCC. Methods: The GSE121248 gene expression profile was obtained from the Gene Expression Omnibus (GEO) database. Differentially expressed genes (DEGs) for HBV-associated HCC were identified by analyzing this expression profile. Enrichment analyses were performed to discover the role of DEGs in biological processes, cell components, molecular functions, and pathways. Then, protein-protein interaction (PPI) was constructed and 5 hub genes were identified. Finally, survival analysis was conducted to validate the prognostic value of these genes. Results: A total of 20188 official gene symbols were found, and 119 DEGs were identified between HBV-associated HCC and normal liver tissues. The PPI network identified CCNB1, CDK1, TOP2A, RACGAP1, and ASPM as hub genes. Kaplan-Meier curves showed that the high expression of the hub genes had significantly lower survival. Conclusion: CCNB1, CDK1, TOP2A, RACGAP1, and ASPM could be potential prognostic biomarkers and therapeutic targets for HBV-associated HCC.

Keywords

• hepatitis b virus
• hepatocellular carcinoma
• bioinformatics
• hub genes
• geo