Abstract
BACKGROUND: Among global cancer statistics, gastric cancer (GC) holds the fifth spot for incidence and is the fourth most common contributor to cancer mortality. The RAS genes constitute the most commonly altered gene family in human malignancies, accounting for nearly 19% of cancer patients carrying RAS mutations. Therefore, this report investigated the relationship between RAS and GC, for providing a new perspective on the outcomes associated with GC. METHODS: Transcriptome profiles and matching clinical information of GC patients were retrieved from The Cancer Genome Atlas (TCGA) database. Univariate Cox analysis was combined with the least absolute shrinkage and selection operator (LASSO) regression to construct the prognostic model. The prognostic model's effectiveness was assessed using Kaplan-Meier survival curves and receiver operating characteristic (ROC) analysis. To further validate the predictive capability, calibration plots and decision curve analysis (DCA) were utilized. We designed a nomogram model to evaluate survival probabilities in individuals with GC. Immune landscape differences between high- and low-risk groups were explored using single-sample gene set enrichment analysis (ssGSEA). The mutational landscape of high- and low-risk groups was examined through tumor mutation burden (TMB) analysis, and drug sensitivity prediction was carried out to assess potential therapeutic responses. RESULTS: Six characterised genes were validated for use as prognostic key biomarkers for GC. According to ROC analysis, the identified RAS pathway genes effectively forecasted the tumor immune dysfunction and exclusion (TIDE) patient outcomes. Furthermore, the high-risk group exhibited significantly elevated TIDE scores compared to the low-risk group. Besides, we identified potential drugs and evaluated the drug sensitivity for GC. CONCLUSIONS: In brief, our investigation identified distinct RAS-related subtypes in GC, offering a novel perspective on the disease's underlying prognostic factors and supporting the progression of personalized therapeutic strategies aimed at improving outcomes in GC patients.