Identification of crucial genes based on expression profiles of hepatocellular carcinomas by bioinformatics analysis

Abstract

Hepatocellular carcinoma (HCC) is one of the most heterogeneous malignant cancers with no effective targets and treatments. However, the molecular pathogenesis of HCC remains largely uncertain. The aims of our study were to find crucial genes involved in HCC through multidimensional methods and revealed potential molecular mechanisms. Here, we reported the gene expression profile GSE121248 findings from 70 HCC and 37 adjacent normal tissues, all of which had chronic hepatitis B virus (HBV) infection, we were seeking to identify the dysregulated pathways, crucial genes and therapeutic targets implicated in HBV-associated HCC. We found 164 differentially expressed genes (DEGs) (92 downregulated genes and 72 upregulated genes). Gene ontology (GO) analysis of DEGs revealed significant functional enrichment of mitotic nuclear division, cell division, and the epoxygenase P450 pathway. Kyoto Encyclopedia of Genes and Genomes (KEGG) analysis showed that the DEGs were mainly enriched in metabolism, cell cycle regulation and the p53 signaling pathway. The Mcode plugin was calculated to construct a module complex of DEGs, and the module was mainly enriched in cell cycle checkpoints, RHO GTPase effectors and cytochrome P450. Considering a weak contribution of each gene, gene set enrichment analysis (GSEA) was performed, revealing results consistent with those described above. Six crucial proteins were selected based on the degree of centrality, including NDC80, ESR1, ZWINT, NCAPG, ENO3 and CENPF. Real-time quantitative PCR analysis validated the six crucial genes had the same expression trend as predicted. Furthermore, the methylation data of The Cancer Genome Atlas (TCGA) with HCC showed that mRNA expression of crucial genes was negatively correlated with methylation levels of their promoter region. The overall survival reflected that high expression of NDC80, CENPF, ZWINT, and NCAPG significantly predicted poor prognosis, whereas ESR1 high expression exhibited a favorable prognosis. The identification of the crucial genes and pathways would contribute to the development of novel molecular targets and biomarker-driven treatments for HCC.

Prognostic Value of Enolase Gene Family in Colon Cancer

“…Nevertheless, some research indicated that the effect of ENO3 varies among cancers. ENO3 protein levels were found to be lower in liver cancer tissues than in normal tissues [ 32 ].…”

Identification of potential hub genes related to the progression and prognosis of hepatocellular carcinoma through integrated bioinformatics analysis

“…Furthermore, the overexpression of ZWINT increased the proliferation of HCC cells by modulating cell cycle-related proteins (58). Using bioinformatics analysis, the hub gene ZWINT was identified and higher expression of ZWINT in HCC predicted poor prognosis in several studies (34,35). The results of our studies also indicated that ZWINT could exert oncogenic effects rather than tumor inhibitory effects on HCC.…”

Identification of Potential Biomarkers Associated with Basal Cell Carcinoma

“…The parameters were set as follows: (i) hypergeometric test was used as the selected statistical test; (ii) Benjamini and Hochberg FDR correction was set as selected correction; (iii) selected significance level was 0.05; and (iv) for testing option, we used whole annotation as the reference set. In addition, the Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analysis was undertaken using KOBAS (http://kobas.cbi.pku.edu.cn/index.php), which is a webaccessible tool and could identify enriched pathways for an input set of genes by mapping to genes using known In addition, the MCODE plugin (http://apps.cytoscape.org/apps/mcode) in cytoscape software was employed to further identify several PPI submodules using the default parameters of degree cutoff = 2, node score cutoff = 0:2, K-core = 2, and max: depth = 100 [18]. Notably, the PPI score > 3 was considered as the cutoff for screening significantly enriched functional modules from the PPI network.…”