Identifying Potential Prognostic Biomarkers Associated With Clinicopathologic Characteristics of Hepatocellular Carcinoma by Bioinformatics Analysis

Abstract

Background: Hepatocellular carcinoma (HCC) is one of the most common malignant tumors worldwide. However, the molecular mechanisms of HCC remain largely unknown so far. Methods: To unravel the underlying carcinogenic mechanisms, we utilized Robust Rank Aggregation analysis (RRA) to identify a set of overlapping differentially expressed genes (DEGs) from 5 microarray datasets on Gene Expression Omnibus (GEO) database. Enriched Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathways of DEGs were conducted. The protein‐protein interaction (PPI) network was constructed and Cytoscape V3.8.0 was used for selecting hub genes. The expression of hub genes was validated in TCGA datasets and HCC samples in our center by qPCR and immunohistochemistry analysis. Results: Totally 126 DEGs were identified. GO and KEGG pathways of DEGs mostly associated with “organelle fission”, “nuclear division” and “caffeine metabolism. Ten hub genes (BUB1B, CDKN3, CCNB1, CCNB2, CDK1, TOP2A, CDC20, MELK, NUSAP1, AURKA) were selected. Overall survival (OS) and progression-free survival (PFS) analysis suggested the good value of these genes for HCC diagnosis and prognosis. These genes were upregulated in HCC samples from TCGA, which were associated with higher tumor grades and possibly resulted from hypomethylation. Moreover, these hub genes were markedly dysregulated in HCC samples in our center and significantly associated with clinicopathologic characteristics of HCC patients. Conclusions: In conclusion, our study identified several hub genes as novel candidate biomarkers for diagnosis and prognosis of HCC, which may provide new insight into HCC pathogenesis in order to search for better treatments.