Identification of Potential Core Genes Associated With the Progression of Stomach Adenocarcinoma Using Bioinformatic Analysis - Yang, Biao; Zhang, Meijing; Luo, Tianhang (Frontiers in Genetics, 2020)
Purpose Stomach adenocarcinoma (STAD) is a type of the most frequently diagnosed cancer in the world with both high mortality and high metastatic capacity. Therefore, we aimed to investigate novel therapeutic targets and prognostic biomarkers for STAD treatment. Materials and methods We acquired four original gene chip profiles GSE13911, GSE19826, GSE54129 and GSE65801 from Gene Expression Omnibus (GEO). The datasets included a total of 114 STAD tissues and 110 adjacent normal tissues. The GEO2R online tool and Venn diagram software were used to discriminate differentially expressed genes (DEGs). Gene ontology (GO) and Kyoto Encyclopedia of Gene and Genome (KEGG) enriched pathways were also performed for annotation and visualization with DEGs. The STRING online database was used to identify the functional interactions of DEGs. Then we selected the most significant DEGs to construct the protein-protein interaction (PPI) network and to reveal core genes. Finally, the Kaplan-Meier Plotter online database and Gene Expression Profiling Interactive Analysis (GEPIA) were used to analyze the prognostic information of the core DEGs. Results A total of 114 DEGs (35 upregulated and 79 downregulated) were identified, which were abnormally expressed in the GEO datasets. Then, GO analysis demonstrated that most of the up-regulated DEGs were remarkably enriched in collagen trimer, cell adhesion and identical protein binding; and down-regulated DEGs in extracellular space, digestion, and inward rectifier potassium channel activity. Signaling pathway analysis indicated that up-regulated DEGs were mainly enriched in receptor interaction; and down-regulated DEGs in gastric acid secretion. There were 80 DEGs were screened into the PPI network complex, and one most important module with a high degree was detected. Furthermore, 10 core genes were identified, COL1A1, COL1A2, FN1, COL5A2, BGN, COL6A3, COL12A1, THBS2, CDH11 and SERPINH1. Finally, the result of prognostic information also showed that all 10 core genes had significantly higher expressed in STAD tissue than normal tissue. Conclusion The multiple molecular mechanisms of these novel core genes in STAD is worthy of further research and may reveal novel therapeutic targets and biomarkers for STAD treatment.