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.
Highlights
Accounting for 75-85% of all primary liver cancer, hepatocellular carcinoma (HCC) is the main histological classification of liver cancer, which is the fourth most frequent cause of cancer-related death globally (Harris et al, 2019; Yang J.D. et al, 2019)
Compared with normal liver tissues in the the Cancer Genome Atlas (TCGA)-LIHC dataset, 2,898 differentially expressed genes (DEGs) were obtained in LIHC tissues, comprising 1,299 upregulated genes and 1,599 downregulated genes (Figure 1B)
Integrated bioinformatics analysis, which focuses on screening of DEGs, discovering hub node of network-based and doing survival analysis, which has been diffusely used to recognize latent biological markers related to cancer diagnosis, therapy, and prognosis estimation
Summary
Accounting for 75-85% of all primary liver cancer, hepatocellular carcinoma (HCC) is the main histological classification of liver cancer, which is the fourth most frequent cause of cancer-related death globally (Harris et al, 2019; Yang J.D. et al, 2019). The advancement of microarray (Yang X. et al, 2018) and high throughput sequencing technologies (Weinstein et al, 2013) has provided a highly efficient tools to explore key genetic or epigenetic changes in disease to identify biological markers that can be applied to disease diagnosis, therapy, and prognosis (Weinstein et al, 2013; Wang et al, 2018; Yang X. et al, 2018; Li et al, 2019). Hepatocellular 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
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