Identification and validation of the prognostic value of cyclic GMP-AMP synthase-stimulator of interferon (cGAS-STING) related genes in gastric cancer

The cyclic GMP-AMP synthase-stimulator of interferon genes (cGAS-STING) pathway play a significant role in the production of inflammatory cytokines and type I interferons. This study aims to develop a cGAS-STING pathway-related genes (CSRs) prediction model to predict prognosis in gastric cancer (GC). In the present study, we used The Cancer Genome Atlas (TCGA), Gene Expression Omnibus databases (GEO), CIBERSORT and Tumor Immune Estimation Resource databases (TIMER). The risk model based on five hub genes (IFNB1, IFNA4, IL6, NFKB2, and TRIM25) was constructed to predict the overall survival (OS) of GC. Further univariate Cox regression (URC) and multivariate Cox regression (MCR) analyses revealed that this risk scoring model was an independent factor. The results were verified by GEO external validation set. Multiple immune pathways were assessed by Gene Set Enrichment Analysis (GSEA). TIMER analysis demonstrated that risk score strongly correlated with Macrophage, B cells and CD8 + T cells infiltration. In addition, through 'CIBERSORT' package, the higher levels of infiltration of T cell follicular assistance (P = 0.011), NK cells-activated (P = 0.034), and Dendritic cells resting (P = 0.033) exhibited in high-risk group. Kaplan-Meier (K-M) survival analysis illustrated T cells CD4 memory resting and T cells follicular helper infiltration correlated with overall survival (OS) of GC patients in TCGA and GEO databases. Altogether, the risk score model can be conveniently used to predict prognosis. The immunocyte infiltration analysis provided a novel horizon for monitoring the status of the GC immune microenvironment.

Automated summary

This study aimed to develop a prediction model for gastric cancer prognosis based on cGAS-STING pathway-related genes, using TCGA, GEO, CIBERSORT, and TIMER databases. The risk model constructed from five hub genes was proven to be an independent prognostic factor. Immune cell infiltration analysis revealed differences in the immune microenvironment between high and low-risk groups.