Bioinformatics analysis of molecular genetic targets and key pathways for hepatocellular carcinoma
Received 19 December 2018
Accepted for publication 27 May 2019
Published 2 July 2019 Volume 2019:12 Pages 5153—5162
Checked for plagiarism Yes
Review by Single-blind
Peer reviewer comments 2
Editor who approved publication: Dr Gaetano Romano
Junxue Tu,1 Jingjing Chen,2 Meimei He,1 Hongfei Tong,3 Haibin Liu,3 Bin Zhou,3 Yi Liao,3 Zhaohong Wang3
1Department of Pharmacy, The Second Affiliated Hospital and Yuying Children’s Hospital of Wenzhou Medical University, Wenzhou, Zhejiang 325000, People’s Republic of China; 2Department of Breast Surgery, The Second Affiliated Hospital and Yuying Children’s Hospital of Wenzhou Medical University, Wenzhou, Zhejiang 325000, People’s Republic of China; 3Department of Hepatobiliary Surgery, The Second Affiliated Hospital and Yuying Children’s Hospital of Wenzhou Medical University, Wenzhou, Zhejiang 325000, People’s Republic of China
Background: Hepatocellular carcinoma (HCC) is the second leading cause of death among cancers worldwide. In this study, we aimed to identify the molecular target genes and detect the key mechanisms of HCC. Three gene expression profiles (GSE84006, GSE14323, GSE14811) and two miRNA expression profiles (GSE40744, GSE36915) were analyzed to determine the molecular target genes, microRNAs (miRNAs) and the potential molecular mechanisms in HCC.
Methods: All profiles were extracted from the Gene Expression Omnibus database. The identification of the differentially expressed genes (DEGs) was analyzed by the GEO2R method. Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway and gene ontology (GO) enrichment analysis performed database for Integrated Discovery, Visualization and Annotation. The miRNA-gene network and protein–protein interaction (PPI) network were correlated by the Cytoscape software. The key target genes were identified by the CytoHubba plugin, Molecular Complex Detection (MCODE) plugin and miRNA-gene network. The identified hub genes were testified for survival curve using the Kaplan–Meier plotter database.
Results: Expression profiles had 592 overlapped DEGs. The majority of the DEGs were enriched in membrane-bounded organelles and intracellular membrane-bounded organelles. These DEGs were significantly enriched in metabolic, protein processing in the endoplasmic reticulum and thyroid cancer pathways. PPI network analysis showed these genes were mostly involved in the pathogenic Escherichia coli infection and the regulation of actin cytoskeleton pathways. Combining these results, we identified 10 key genes involving in the progression of HCC. Finally, PLK1, PRCC, PRPF4 and PSMA7 exhibited higher expression levels in HCC patients with poor prognosis than those for lower expression via Kaplan–Meier plotter database.
Conclusion: PLK1, PRCC, PRPF4 and PSMA7 could be potential biomarkers or therapeutic targets for HCC. Meanwhile, the metabolic pathway, protein processing in the endoplasmic reticulum and the thyroid cancer pathway may play vital roles in the progression of HCC.
Keywords: hepatocellular carcinoma, DEGs, bioinformatic analysis, KEGG pathway, PPI network, miRNA-gene network
This work is published and licensed by Dove Medical Press Limited. The full terms of this license are available at https://www.dovepress.com/terms.php and incorporate the Creative Commons Attribution - Non Commercial (unported, v3.0) License. By accessing the work you hereby accept the Terms. Non-commercial uses of the work are permitted without any further permission from Dove Medical Press Limited, provided the work is properly attributed. For permission for commercial use of this work, please see paragraphs 4.2 and 5 of our Terms.Download Article [PDF] View Full Text [HTML][Machine readable]