Identification of core genes and outcomes in hepatocellular carcinoma by bioinformatics analysis

Abstract

Hepatocellular carcinoma (HCC) is the most common malignant liver disease in the world. However, the mechanistic relationships among various genes and signaling pathways are still largely unclear. In this study, we aimed to elucidate potential core candidate genes and pathways in HCC. The expression profiles GSE14520, GSE25097, GSE29721, and GSE62232, which cover 606 tumor and 550 nontumour samples, were downloaded from the Gene Expression Omnibus (GEO) database. Furthermore, HCC RNA-seq datasets were also downloaded from the Cancer Genome Atlas (TCGA) database. The differentially expressed genes (DEGs) were filtered using R software, and we performed gene ontology (GO) and Kyoto Encyclopedia of Gene and Genome (KEGG) pathway analysis using the online databases DAVID 6.8 and KOBAS 3.0. Furthermore, the protein-protein interaction (PPI) network complex of these DEGs was constructed by Cytoscape software, the molecular complex detection (MCODE) plug-in and the online database STRING. First, a total of 173 DEGs (41 upregulated and 132 downregulated) were identified that were aberrantly expressed in both the GEO and TCGA datasets. Second, GO analysis revealed that most of the DEGs were significantly enriched in extracellular exosomes, cytosol, extracellular region, and extracellular space. Signaling pathway analysis indicated that the DEGs had common pathways in metabolism-related pathways, cell cycle, and biological oxidations. Third, 146 nodes were identified from the DEG PPI network complex, and two important modules with a high degree were detected using the MCODE plug-in. In addition, 10 core genes were identified, TOP2A, NDC80, FOXM1, HMMR, KNTC1, PTTG1, FEN1, RFC4, SMC4, and PRC1. Finally, Kaplan-Meier analysis of overall survival and correlation analysis were applied to these genes. The abovementioned findings indicate that the identified core genes and pathways in this bioinformatics analysis could significantly enrich our understanding of the development and recurrence of HCC; furthermore, these candidate genes and pathways could be therapeutic targets for HCC treatment. K E Y W O R D S bioinformatical analysis, differentially expressed genes (DEGs), hepatocellular carcinoma (HCC), pathways J Cell Biochem. 2019;120:10069-10081.wileyonlinelibrary.com/journal/jcb

Transcriptome analysis revealed key prognostic genes and microRNAs in hepatocellular carcinoma

“…Genomic, epigenetic and transcriptional alterations, as well as subsequent multistep processes, play crucial roles in the occurrence, development, metastasis and prognosis of HCC (Dhanasekaran et al, 2019;Llovet et al, 2018). Combined with public biological databases (e.g., GO and KEGG), the development of high-throughput detection technology provides us with an opportunity to systematically explore ''interesting'' gene lists on a genome-wide scale and sort out correlative biological processes (Shan, Chen & Jia, 2019; Shen et al, 2019). To identify reproducible and robust genetic alterations, integrated analyses based on multiple datasets gradually replace single cohort analyses.…”

Identification of novel genes associated with a poor prognosis in pancreatic ductal adenocarcinoma via a bioinformatics analysis

“…STRING (https://string-db.org/) is a user-friendly online system that provides predicted and experimental interactions of proteins [25]. We used the STRING database to analyze functional interactions between the overlapping DEGs with a confidence score of ≥0.4 for significant differences.…”

Peer Review #2 of "Identification of Flap endonuclease 1 as a potential core gene in hepatocellular carcinoma by integrated bioinformatics analysis (v0.1)"

“…DTL and MCM2 were also identified as hub genes for HCC in another study (Sang, Wang, Xu, & Zhao, 2018). Shen S (Shen et al, 2019) identified FEN1 and RFC4 as core genes by analyzing four HCC datasets. The above studies indicated the core genes screened and identified in our study were of value.…”