The Analysis of Potential Diagnostic and Therapeutic Targets for the Occurrence and Development of Gastric Cancer Based on Bioinformatics

Objective. Gastric cancer is among the most common malignant tumors of the digestive system. This study explored the molecular mechanisms and potential therapeutic targets for gastric cancer occurrence and progression using bioinformatics. Methods. The gastric cancer microarray dataset was downloaded from the Gene Expression Omnibus (GEO) database. The R package was used for data mining and screening differentially expressed genes (DEGs). Gene Ontology (GO) analysis and Kyoto Encyclopedia of Gene and Genome (KEGG) pathway analysis were performed using the Database for Annotation, Visualization, and Integrated Discovery (DAVID). Based on the protein-protein interaction (PPI) network analysis, core targets and core subsets were screened. Then, the relationship between the expression level of the core genes and the prognosis of gastric cancer patients was analyzed using the Gene Expression Profiling Interactive Analysis (GEPIA) database. Results. Using the GSE19826 and GSE54129 datasets, a total of 550 DEGs were identified, including 248 upregulated and 302 downregulated genes. GO and KEGG analyses showed that the upregulated DEGs were mainly enriched in the extracellular matrix (ECM) organization of the biological process (BP), the collagen-containing ECM of cellular component (CC), and the ECM structural constituent of molecular function (MF). DEGs were also enriched in human papillomavirus infections, the focal adhesion pathway, PI3K-Akt signaling pathway, and among others. The downregulated DEGs were mainly enriched in digestion, basal part of the cell, and aldo-keto reductase (NADP) activity. And the above pathways were enriched primarily in the metabolism of xenobiotics by cytochrome P450, drug metabolism-cytochrome P450, and retinol metabolism. Five core genes, including COL1A2, COL3A1, BGN, FN1, and VCAN, were significantly highly expressed in gastric cancer patients and were associated with poor prognosis. Conclusion. This study identified new potential molecular targets closely related to gastric cancer occurrence and development via mining public data using bioinformatics analysis methods.