Prognostic value of gastric cancer-associated gene signatures: Evidence based on a meta-analysis using integrated bioinformatics methods.

Selecting differentially expressed genes (DEGs) based on integrated bioinformatics analyses has been used in previous studies to explore potential biomarkers in gastric cancer (GC) with microarray and RNA sequencing data. However, the genes obtained may be inaccurate because of noisy data and errors, as well as insufficient clinical sample sizes. Thus, we aimed to find robust and strong DEGs with prognostic value for GC, where the robust rank aggregation method was employed to select significant DEGs from eight Gene Expression Omnibus data sets with a total of 140 up-regulated and 206 down-regulated genes. Network data mining was then used to screen hub genes, and 11 genes were filtered using Fisher's exact test. Based on these results, we built a prognostic signature with seven genes (FBN1, MMP1, PLAU, SPARC, COL1A2, COL2A1 and ATP4A) using stepwise multivariate Cox proportional hazard regression. According to the risk score for each patient, we found that high-risk group patients had significantly worse survival results compared with those in the low-risk group (log-rank test P-value < 0.001). This seven-gene signature was then validated with an external data set. Thus, we established a signature based on seven DEGs with prognostic value for GC patients using multi-steps bioinformatics methods, which may provide novel insights and potential biomarkers for prognosis, as well as possibly serving as new therapeutic targets in clinical applications.