Identification of key genes with prognostic value in gastric cancer by bioinformatics analysis

Background: Gastric cancer (GC) is a digestive system tumor with high morbidity and mortality. It is urgently required to identify genes to elucidate the underlying molecular mechanisms. The aim of this study is to identify the key genes which may affect the prognosis of GC patients and be a therapeutic strategy for GC patients by bioinformatic analysis. Methods: The significant prognostic differentially expressed genes (DEGs) were screened out from The Cancer Genome Atlas (TCGA) and the Gene Expression Omnibus (GEO) datasets. The protein–protein interaction (PPI) network was established by STRING and screening key genes by MCODE and CytoNCA plug-ins in Cytoscape. Functional enrichment analysis, construction of a prognostic risk model, and nomograms verify key genes as potential therapeutic targets. Results: In total, 997 genes and 805 genes were related to the prognosis of GC in the GSE84437 and TCGA datasets, respectively. We define the 128 genes shared by the two datasets as prognostic DEGs (P-DEGs). Then, the first four genes ( MYLK , MYL9 , LUM , and CAV1 ) with great node importance in the PPI network of P-DEGs were identified as key genes. Independent prognostic risk analysis found that patients with high key gene expression had a poor prognosis, excluding their age, gender, and TNM stage. GO and KEGG enrichment analyses showed that key genes may exert influence through the PI3K-Akt pathway, in which extracellular matrix organization and focal adhesion may play important roles in key genes influencing the prognosis of GC patients. Conclusion: We found that MYLK , MYL9 , LUM , and CAV1 are potential and reliable prognostic key genes that affect the invasion and migration of gastric cancer.