Integrated bioinformatics analysis for exploring hub genes and related mechanisms affecting the progression of gastric cancer.

Objective Gastric cancer (GC) is a high-risk tumor disease worldwide. The goal of the current study was to explore new diagnostic and prognostic indicators for gastric cancer. Methods Database GSE19826 and GSE103236 were gained from the Gene Expression Omnibus (GEO) to screen for differentially expressed genes (DEGs), which were then grouped together as co-DEGs. GO and KEGG pathway analysis were used to investigate the function of these genes. The protein-protein interaction (PPI) network of DEGs was constructed by STRING. Results GSE19826 selected 493 DEGs in GC and gastric normal tissues, including 139 up-regulated genes and 354 down-regulated genes. A total of 478 DEGs were selected by GSE103236, including 276 up-regulated genes and 202 downregulated genes. 32 co-DEGs were overlapped from two databasesand involved in digestion, regulation of response to wounding, wound healing, potassium ion imports across plasma membrane, regulation of wound healing, anatomical structure homeostasis, and tissue homeostasis. KEGG analysis revealed that co-DEGs were mainly involved in ECM-receptor interaction, tight junction, protein digestion and absorption, gastric acid secretion and cell adhesion molecules. Twelve hub genes were screened by Cytoscape, including cholecystokinin B receptor (CCKBR), Collagen type I alpha 1 (COL1A1), COL1A2, COL2A1, COL6A3, COL11A1, matrix metallopeptidase 1 (MMP1), MMP3, MMP7, MMP10, tissue inhibitor of matrix metalloprotease 1 (TIMP1) and secreted phosphoprotein 1 (SPP1). Conclusions Twelve key genes affecting the progression of gastric cancer were obtained by bioinformatics, which may be potential biomarkers for the diagnosis and prognosis of GC.