Identification of the angiogenesis related genes for predicting prognosis of patients with gastric cancer

IntroductionAngiogenesis is a key factor in promoting tumor growth, invasion and metastasis. In this study we aimed to investigate the prognostic value of angiogenesis-related genes (ARGs) in gastric cancer (GC).MethodsmRNA sequencing data with clinical information of GC were downloaded from The Cancer Genome Atlas (TCGA) and the Gene Expression Omnibus (GEO) databases. The differentially expressed ARGs between normal and tumor tissues were analyzed by limma package, and then prognosis-associated genes were screened using Cox regression analysis. Nine angiogenesis genes were identified as crucially related to the overall survival (OS) of patients through least absolute shrinkage and selection operator (LASSO) regression. The prognostic model and corresponding nomograms were establish based on 9 ARGs and verified in in both TCGA and GEO GC cohorts respectively.ResultsEighty-five differentially expressed ARGs and their enriched pathways were confirmed. Significant enrichment analysis revealed that ARGs-related signaling pathway genes were highly related to tumor angiogenesis development. Kaplan-Meier analysis revealed that patients in the high-risk group had worse OS rates compared with the low-risk group in training cohort and validation cohort. In addition, RS had a good prognostic effect on GC patients with different clinical features, especially those with advanced GC. Besides, the calibration curves verified fine concordance between the nomogram prediction model and actual observation.ConclusionsWe developed a nine gene signature related to the angiogenesis that can predict overall survival for GC. It's assumed to be a valuable prognosis model with high efficiency, providing new perspectives in targeted therapy.

Automated summary

This study identified nine angiogenesis-related genes crucially related to overall survival in gastric cancer. These genes were found to be highly related to tumor angiogenesis development. The prognostic model showed good efficacy in predicting survival outcomes for patients with different clinical features.