Identification of potential core genes in gastric cancer using bioinformatics analysis

Changjiang Shao; Rong Wang; Dandan Kong; Qian Gao; Chunfang Xu

doi:10.21037/jgo-21-628

Identification of potential core genes in gastric cancer using bioinformatics analysis

J Gastrointest Oncol. 2021 Oct;12(5):2109-2122. doi: 10.21037/jgo-21-628.

Authors

Changjiang Shao^#^{1

2}, Rong Wang^#³, Dandan Kong³, Qian Gao³, Chunfang Xu¹

Affiliations

¹ Department of Gastroenterology, The First Affiliated Hospital of Soochow University, Suzhou, China.
² Department of Gastroenterology, The Second People's Hospital of Lianyungang City, Lianyungang, China.
³ Department of Oncology, The First Affiliated Hospital of Soochow University, Suzhou, China.

^# Contributed equally.

Abstract

Background: Gastric cancer is the third leading cause of cancer-related mortality in China. Most patients with gastric cancer have no obvious early symptoms; thus, many of them are in the middle and late stages of gastric cancer at first diagnosis and miss the best treatment opportunity. Molecular targeted therapy is particularly important in changing this status quo.

Methods: Three microarray datasets (GSE29272, GSE33651, and GSE54129) were selected from the Gene Expression Omnibus (GEO) database. Differentially expressed genes (DEGs) were screened using GEO2R. The Database for Annotation, Visualization and Integrated Discovery (DAVID) was used to analyze the functional features of these DEGs and the Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment. The protein-protein interaction (PPI) of these DEGs was visualized by Cytoscape software. The expressions of hub genes were evaluated based on Gene Expression Profiling Interactive Analysis (GEPIA). Moreover, we used the online Kaplan-Meier plotter survival analysis tool to evaluate the prognostic values of hub genes. The Target Scan database was used to predict microRNAs that could regulate the target gene, collagen type IV alpha 1 chain (COL4A1). The OncomiR database was used to analyze the expression levels of three microRNAs, as well as the relationships with tumor stage, grade, and prognosis.

Results: We identified 78 DEGs, including 53 upregulated genes and 25 downregulated genes. The DEGs were mainly enriched in extracellular matrix organization, extracellular structure organization, and response to wounding. Moreover, three KEGG pathways were markedly enriched, including focal adhesion, complement and coagulation cascades, and extracellular matrix (ECM)-receptor interaction. Among these 78 genes, we selected 10 hub genes. The overexpression levels of these hub genes were closely related to poor prognosis and the development of gastric cancer (except for COL3A1, LOX, and CXCL8). Moreover, we found that microRNA-29a-3p, miR-29b-3p, and miR-29c-3p were the potential microRNAs that could regulate the target gene, COL4A1.

Conclusions: Our results showed that FN1, COL1A1, TIMP1, COL1A2, SPARC, COL4A1, and SERPINE1 could contribute to the development of novel molecular targets and biomarker-driven treatments for gastric cancer.

Keywords: Gastric cancer; differentially expressed genes (DEGs); functional enrichment analysis; hub genes; microRNAs.