Objective Although N6-methyladenosine (m6A) RNA methylation is the most common mRNA modification process, few studies have examined the role of m6A in stomach adenocarcinomas (STADs). Methods In this retrospective study, we analyzed 293 STAD samples from The Cancer Genome Atlas with complete clinicopathological feature profiles. The m6A methylation risk signature was derived from LASSO–Cox regression analyses with 15 m6A regulators. Statistical analysis was performed and figures were prepared using R software ( https://www.R-project.org/ ). Results The m6A signature was established as follows: risk score = FTO × 0.127 + YTHDF1 × 0.004 + KIAA1429 × 0.044 + YTHDC2 × 0.112 − RBM15 × 0.135 − ALKBH5 × 0.019 − YTHDF2 × 0.028, which was confirmed as an independent prognostic indicator to predict overall survival of patients with STAD. Risk scores and tumor grades were closely associated. Cell cycle, p53 signaling pathways, DNA mismatch repair, and RNA degradation were enriched in the low-risk subgroup. This subgroup showed significantly higher expression of immune checkpoint molecules including PD-1 (programmed death 1), PD-L1 (programmed death-ligand 1), and CTLA-4 (cytotoxic T-lymphocyte–associated antigen 4), suggesting that the signature may be a useful immunotherapy predictor. Conclusions We established an m6A methylation signature as an independent prognostic tool to predict overall survival, which may also be useful as an immunotherapy predictor.

Tumor-infiltrating lymphocytes (TILs) in gastric cancer are closely related to clinical prognosis; however, little is known regarding the immune microenvironment in this disease. Thus, RNA-sequencing data from gastric cancer patients were downloaded from the Gene Expression Omnibus (GEO). The proportion of immune cells was determined based on a deconvolution algorithm (CIBERSORT), and gene expression profiles were analyzed in the context of clinical outcomes to construct an immune risk score. Data were analyzed using least absolute shrinkage and selection operator (LASSO) and multivariable Cox regression, to identify prognostic markers of gastric cancer survival. The model included four immune cell types: neutrophils, plasma cells, activated CD4 + memory T cells, and T follicular helper cells. Patients were classified into two subgroups based on risk score, and a significant difference in overall survival (OS) was seen between the subgroups in both the training and testing cohorts, particularly in patients with tumor stages ≥T3. Multivariable analysis revealed that both T-stage and risk score were independent prognostic factors for gastric cancer survival [hazard ratio (HR) 1.505; 95% confidence interval (CI) 1.043-2.173, HR 1.686; 95% CI 1.367-2.080]. Risk scores and clinical factors were then integrated into a nomogram to build a model with both good discriminatory power and accuracy in predicting clinical outcomes. Further analysis using gene set enrichment analysis (GSEA) identified strong associations of immune risk with TGF-β and tumor metastasis-related pathways, which could inform research on the molecular mechanisms of gastric cancer. Collectively, the data presented here suggest that an immune risk model can make an important contribution to predictions prognosis in gastric cancer patients.