Identification of hub genes and potential molecular mechanisms in gastric cancer by integrated bioinformatics analysis

PeerJ

article

Ling Cao¹  Yan Chen⁵  Miao Zhang⁴  De-quan Xu⁴  Yan Liu⁶  Tonglin Liu⁷  Shi-xin Liu⁴  Ping Wang¹ 

[1] Department of Radiation Oncology, Tianjin Medical University Cancer Institute and Hospital, National Clinical Research Center for Cancer;Key Laboratory of Cancer Prevention and Therapy;Tianjin’s Clinical Research Center for Cancer;Department of Radiation Oncology, Cancer Hospital of Jilin Province;Department of Gastrointestinal Surgery, First Hospital of Jilin University;Medical Oncology Translational Research Lab, Cancer Hospital of Jilin Province;Information Centre, Cancer Hospital of Jilin Province

关键词: Gastric cancer;    Bioinformatic analysis;    Differentially expressed genes;    Protein–protein interaction network;    Bioinformatic mining;    miRNA-gene network;    KEGG pathway;   

DOI  :  10.7717/peerj.5180

学科分类：社会科学、人文和艺术（综合）

来源: Inra

【 摘 要 】

Objective Gastric cancer (GC) is the fourth most common cause of cancer-related deaths in the world. In the current study, we aim to identify the hub genes and uncover the molecular mechanisms of GC. Methods The expression profiles of the genes and the miRNAs were extracted from the Gene Expression Omnibus database. The identification of the differentially expressed genes (DEGs), including miRNAs, was performed by the GEO2R. Database for Annotation, Visualization and Integrated Discovery was used to perform GO and KEGG pathway enrichment analysis. The protein–protein interaction (PPI) network and miRNA-gene network were constructed using Cytoscape software. The hub genes were identified by the Molecular Complex Detection (MCODE) plugin, the CytoHubba plugin and miRNA-gene network. Then, the identified genes were verified by Kaplan–Meier plotter database and quantitative real-time PCR (qRT-PCR) in GC tissue samples. Results A total of three mRNA expression profiles (GSE13911, GSE79973 and GSE19826) were downloaded from the Gene Expression Omnibus (GEO) database, including 69, 20 and 27cases separately. A total of 120 overlapped upregulated genes and 246 downregulated genes were identified. The majority of the DEGs were enriched in extracellular matrix organization, collagen catabolic process, collagen fibril organization and cell adhesion. In addition, three KEGG pathways were significantly enriched, including ECM-receptor interaction, protein digestion and absorption, and the focal adhesion pathways. In the PPI network, five significant modules were detected, while the genes in the modules were mainly involved in the ECM-receptor interaction and focal adhesion pathways. By combining the results of MCODE, CytoHubba and miRNA-gene network, a total of six hub genes including COL1A2, COL1A1, COL4A1, COL5A2, THBS2 and ITGA5 were chosen. The Kaplan–Meier plotter database confirmed that higher expression levels of these genes were related to lower overall survival, except for COL5A2. Experimental validation showed that the rest of the five genes had the same expression trend as predicted. Conclusion In conclusion, COL1A2, COL1A1, COL4A1, THBS2 and ITGA5 may be potential biomarkers and therapeutic targets for GC. Moreover, ECM-receptor interaction and focal adhesion pathways play significant roles in the progression of GC.

【 授权许可】

CC BY