We aim to identify molecular clusters related to O-GlcNAcylation and establish a novel scoring system for predicting prognosis and immunotherapy efficacy in patients with gastric cancer (GC). The transcriptomic and clinical data are obtained from XENA-UCSC and GEO databases. The O-GlcNAcylation-related genes are obtained from the GSEA database. Consensus clustering analysis is employed to identify O-GlcNAcylation-related molecular clusters, and principal component analysis (PCA) is utilized to develop a novel prognostic scoring system for predicting GC outcomes and immunotherapy efficacy. The prognostic accuracy of the scoring system is assessed across five real-world cohorts. The biological function of actin alpha 2, smooth muscle (ACTA2) in GC is determined through experimental verification. Using 34 O-GlcNAcylation-related genes associated with prognosis in GC patients, these individuals are divided into two distinct subgroups characterized by different outcomes, tumor microenvironment profiles, and clinical case characteristics. The DEGs between the two subgroups are subsequently used to further divide the GC patients into two subgroups by consensus cluster analysis. PCA is used to construct a prognostic scoring system, which reveal that patients in the low-score subgroup have a better prognosis and greater benefit from immunotherapy. The accuracy of the scoring system is confirmed through validation in a cohort of patients receiving immunotherapy in the real world. ACTA2 promotes proliferation and inhibits apoptosis in GC cells. These findings suggest that we successfully establish molecular clusters associated with O-GlcNAcylation and develop a scoring system that demonstrates strong performance in predicting the prognosis of patients with GC and the effect of immunotherapy interventions.