A risk-scoring prognostic model of gastric cancer based on angiogenesis.

Abstract

e16058 Background: Gastric cancer (GC) is a worldwide malignancy that represents a serious threat to human health. However, there is a lack of research on prognostic signatures of angiogenesis and prognosis, immune cell infiltration, and individualized treatment guidance in GC. Methods: Based on NCBI and MsigDB, Angiogenesis-related genes (ARGs) were obtained, and GO and KEGG enrichment analysis were performed. The risk scoring model was constructed by screening ARGs prognosis-related genes through a single-factor COX regression model, combined with Lasso regression to remove redundant factors. The prognostic value was assessed by combining TCGA and GEO databases. Immune infiltration difference analysis was performed. Furthermore, the variability of mutations was analyzed, and CNV differential display was performed. The predictive efficacy for immunotherapy was assessed by analogy to the melanoma immunotherapy suppression data from the GEO. The predictive efficacy of the TIDE assessment risk score for immunotherapy was additionally assessed. Finally, independent prognostic factors of risk scores were analyzed and a nomogram was construction. Results: We finally obtained 575 ARGs, and GO annotation showed that they were associated with angiogenesis, cell migration movement and ligand receptor activity. KEGG showed that it was related to Ras, MAPK and PI3K pathways. A risk score model containing 9 genes was constructed. It was found that the evaluation had a good prognostic value. Analysis of immunoinfiltration differences showed that there were differences. In addition, there was some variability in mutations between the high-risk and low-risk subgroups. It was found to have a better predictive effect of immunotherapy. TIDE assessment risk score was also a good predictor of immunotherapy efficacy. Finally, a nomogram was constructed based on clinical characteristics and risk scores. Conclusions: In this study, a prognostic risk score model for gastric cancer was established, and it was found to have a good predictive effect on immunotherapy effect.