Identification and Analysis of Potential Key Genes Associated With Hepatocellular Carcinoma Based on Integrated Bioinformatics Methods

Abstract

BackgroundHepatocellular carcinoma (HCC) is a type of primary liver tumor with poor prognosis and high mortality, and its molecular mechanism remains incompletely understood. This study aimed to use bioinformatics technology to identify differentially expressed genes (DEGs) in HCC pathogenesis, hoping to identify novel biomarkers or potential therapeutic targets for HCC research.MethodsThe bioinformatics analysis of our research mostly involved the following two datasets: Gene Expression Omnibus (GEO) and The Cancer Genome Atlas (TCGA). First, we screened DEGs based on the R packages (limma and edgeR). Using the DAVID database, the Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analyses of DEGs were carried out. Next, the protein-protein interaction (PPI) network of the DEGs was built in the STRING database. Then, hub genes were screened through the cytoHubba plug-in, followed by verification using the GEPIA and Oncomine databases. We demonstrated differences in levels of the protein in hub genes using the Human Protein Atlas (HPA) database. Finally, the hub genes prognostic values were analyzed by the GEPIA database. Additionally, using the Comparative Toxicogenomics Database (CTD), we constructed the drug-gene interaction network.ResultsWe ended up with 763 DEGs, including 247 upregulated and 516 downregulated DEGs, that were mainly enriched in the epoxygenase P450 pathway, oxidation-reduction process, and metabolism-related pathways. Through the constructed PPI network, it can be concluded that the P53 signaling pathway and the cell cycle are the most obvious in module analysis. From the PPI, we filtered out eight hub genes, and these genes were significantly upregulated in HCC samples, findings consistent with the expression validation results. Additionally, survival analysis showed that high level gene expression of CDC20, CDK1, MAD2L1, BUB1, BUB1B, CCNB1, and CCNA2 were connected with the poor overall survival of HCC patients. Toxicogenomics analysis showed that only topotecan, oxaliplatin, and azathioprine could reduce the gene expression levels of all seven hub genes.ConclusionThe present study screened out the key genes and pathways that were related to HCC pathogenesis, which could provide new insight for the future molecularly targeted therapy and prognosis evaluation of HCC.

The Predictive Competing Endogenous RNA Regulatory Networks and Potential Prognostic and Immunological Roles of Cyclin A2 in Pan-Cancer Analysis

“…Accumulating evidence has documented CCNA2 is an important differentially expressed gene (DEG, mainly overexpressed) in various cancer types compared to normal tissues, such as LIHC ( Li et al, 2021a ), human epidermal growth factor 2 (HER2)+ breast cancer ( Weng et al, 2021 ), LUSC ( Gao et al, 2020 ), COAD ( Li et al, 2021b ), PRAD ( Feng et al, 2021 ), HNSC ( Zhang et al, 2020 ), KIRC ( Zhan et al, 2021 ), THCA ( Li et al, 2020b ), medulloblastoma ( Guo et al, 2020a ), gastric cancer ( Ji et al, 2021 ; Lu et al, 2021 ), and mantle cell lymphoma ( Guo et al, 2020b ). These bioinformatic results are credible because they are not only from the TCGA dataset ( Li et al, 2020a ; Gao et al, 2020 ; Zhang et al, 2020 ; Li et al, 2021a ; Li et al, 2021b ; Feng et al, 2021 ; Ji et al, 2021 ; Weng et al, 2021 ) but also from Gene Expression Omnibus (GEO) datasets or series ( Guo et al, 2020a ; Li et al, 2020a ; Guo et al, 2020b ; Li et al, 2020b ; Gao et al, 2020 ; Li et al, 2021a ; Li et al, 2021b ; Lu et al, 2021 ; Weng et al, 2021 ; Zhan et al, 2021 ). However, it is unable to retrieve any available reports about a pan-cancer analysis of the CCNA2 expression across all cancer types.…”

The Prognostic Value and Immune Landscapes of a m6A/m5C/m1A-Related LncRNAs Signature in Head and Neck Squamous Cell Carcinoma

“…Gene enrichment analysis (GSEA) was used to understand the differentially functional enrichment pathways of samples with different types ( Tang et al, 2021 ). In order to explore the characteristics of immune microenvironment of different types of samples, the R package “limma” was used to filter the differences in immune cell infiltration of different types of samples ( Li Z. et al, 2021 ). In addition, we further analyzed the expression of 29 immune checkpoint moleclues in different types of samples, including CD70, TNFSF9, FGL1, CD276, NT5E, HHLA2, VTCN1, TNFRSF18, CD274, PDCDL1LG2, IDO1, CTLA4, ICOS, HAVCR, PDCD1, LAG3, SIGLEC15, TNFSF4, TNFRSF9, ENTPD1, VSIR, ICOSLG, TNFSF14, TMIGD2, CD27, NCR3, BTLA, CD40LG, CD40, TNFRSF4 ( Zhang C. et al, 2020 ).…”

CCNB1 promotes the development of hepatocellular carcinoma by mediating DNA replication in the cell cycle

“…23 As a result of various studies, CCNB1 is believed to be overexpressed in HCC. 21,22,24–30 However, a large sample size is needed to verify the expression patterns of CCNB1 in HCC, and the mechanisms of CCNB1 in HCC are still unknown. Moreover, the potential clinical implications of CCNB1 expression in HCC patients need to be further studied.…”