Prognostic biomarkers of hepatocellular carcinoma identified by bioinformatics

Abstract

Abstract: Objective To investigate new prognostic biomarkers of hepatocellular carcinoma (HCC) through bioinformatics.Methods The differentially expressed genes (DEGs) in the GSE14520 data set were screened by GEO2R, a protein-protein interaction (PPI) network was constructed by STRING, and key genes were screened out by Cytoscape software. GO and KEGG analyses were performed in DAVID database. Then GEPIA was used to analyze the expression levels of key genes in HCC and their relationship with prognosis.Results A total of 348 DEGs were screened out, including 91 up-regulated genes and 257 down-regulated genes. The top 2 network modules were screen out from the PPI: module 1 and module 2. There were 46 key genes in the center area of the network in the 2 modules. Key genes were significantly enriched in signaling pathways such as cell cycle, DNA replication and drug metabolism. It was verified by GEPIA that PROZ and F9 were significantly under-expressed in HCC patients, and were associated with a decrease in the overall survival rate and disease-free survival rate of HCC patients.Conclusion PROZ and F9 can be regarded as potential prognostic biomarkers for HCC.

Key words: Hepatocellular carcinoma, Differentially expressed genes, Bioinformatics analysis

HE Chun-mei, MO Zhi-jing. Prognostic biomarkers of hepatocellular carcinoma identified by bioinformatics[J]. Chinese Hepatolgy, 2021, 26(5): 522-526.

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