Identification of potential prognostic biomarkers for hepatocellular carcinoma

Abstract

Background: The incidence of liver cancer is increasing every year. Hepatocellular carcinoma (HCC) accounts for nearly 90% of liver cancer, and the overall 5-year survival rate of become of Hepatocellular carcinoma patients less than 20%. However, the molecular mechanism of HCC progression and prognosis still requires further exploration. Methods: In this study, we downloaded the gene expression data from the Cancer Genome Atlas (TCGA) Genomic Data and the official website of GEO database. Weighted gene co-expression network analysis (WGCNA) and Pearson's correlation coefficient were utilized to detect the gene modules. The shared differentially-expressed genes (DEGs) were screened out by a Venn diagram, and the hub genes were identified through protein-protein interaction (PPI) network analyses. GO and KEGG enrichment analyses were constructed for these hub genes. Overall survival (OS) and correlation analysis were conducted to investigate the relationship between the hub genes and clinical features. Results: We screened out 27 shared DEGs, and the mainly enriched GO terms were mitotic nuclear division, chromosomal region, and tubulin binding. Furthermore, the top three enriched KEGG pathways were “cell cycle”, “oocyte meiosis”, and “p53 signaling pathway”. According to the Maximal Clique Centrality (MCC) algorithm, the top 10 candidate hub genes were MYC, MCM3, CDC20, CCNB1, BIRC5, UBE2C, TOP2A, RRM2, TK1, and PTTG1, among which BIRC5, CDC20, and UBE2C showed a strong correlation with the OS. Conclusions: Three hub genes (BIRC5, CDC20, and UBE2C) were identified and found to be correlated to the progression and prognosis of HCC. These may become potential targets for HCC therapy.