Bioinformatics analysis of key biomarkers and potential molecular mechanisms in hepatocellular carcinoma induced by hepatitis B virus. Issue 20 (15th May 2020)

• Record Type: Journal Article
• Authors: Li, Zhe; Xu, Jingyong; Cui, Hongyuan; Song, Jinghai; Chen, Jian; Wei, Junmin
• Other Names: Kumar. Surinder section editor.
• Is Part Of: Medicine. Volume 99:Issue 20(2020)
• Publication Date: 2020-05-15
• Subjects: bioinformatical analysis -- differentially expressed genes -- hepatitis B virus -- hepatocellular carcinoma; Medicine -- Periodicals; Medicine -- Periodicals; Médecine -- Périodiques; Geneeskunde; Medicine; Periodicals; Periodicals; 610.5
• DOI: 10.1097/MD.0000000000020302
• Languages: English
• ISSNs: 0025-7974

Abstract

Abstract: Background: Hepatocellular carcinoma (HCC) accounts for up to 90% of all primary hepatic malignancies; it is the sixth most common cancer and the second most common cause of cancer mortality worldwide. Numerous studies have shown that hepatitis B virus and its products, HBV integration, and mutation can induce HCC. However, the molecular mechanisms underpinning the regulation of HCC induced by HBV remain unclear. Methods: We downloaded 2 gene expression profiling datasets, of HBV and of HCC induced by HBV, from the gene expression omnibus (GEO) database. Differentially expressed genes (DEGs) between HCC and HBV were identified to explore any predisposing changes in gene expression associated with HCC. DEGs between HCC and adjacent healthy tissues were investigated to identify genes that may play a key role in HCC. Any overlapping genes among these DEGs were included in our bioinformatics analysis. Gene ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) analyses of overlapping genes were performed using the Metascape online database; the protein–protein interaction (PPI) network was analyzed using the STRING online database; and we obtained the hub genes of the PPI network using Cytoscape software. An overall survival (OS) analysis of hub genes was performed using km-plotter and the gene expression profiling interactive analysis (GEPIA) online database. The expression levels of hub genes were determined using the TCGA and GEPIA databases. Finally, the relationships between hub genes and tumors were analyzed using the comparative toxicogenomics database (CTD). Results: We identified 113 overlapping genes from the 2 datasets. Using functional and pathway analyses, we found that the overlapping genes were mainly related to the AMPK signaling pathway and cellular responses to cadmium ions. C8A, SPP2, KLKB1, PROZ, C6, FETUB, MBL2, HGFAC, C8B, and ANGPTL3 were identified as hub genes and C8A, SPP2, PROZ, C6, HGFAC, and C8B were found to be significant for survival. Conclusion: The DEGs re-analyzed between HCC and hepatitis B enable a systematic understanding of the molecular mechanisms of HCC reliant on hepatitis B virus.