Abstract
Background
Epstein–Barr virus–associated gastric cancer (EBVaGC) is a distinct molecular subtype of gastric cancer, but reliable biomarkers linking EBV‐related biology, prognosis, and microenvironmental remodeling remain limited.
Methods
Differentially expressed genes associated with EBVaGC were identified from TCGA and GEO datasets. Weighted gene co‐expression network analysis and machine learning were used to screen core genes. Functional enrichment, diagnostic and prognostic modeling, immune infiltration analysis, single‐cell transcriptomic analysis of a public scRNA‐seq dataset, cell–cell communication analysis, drug sensitivity prediction, external cohort validation, and RT‐qPCR validation in gastric cancer cell lines were subsequently performed.
Results
Five core genes (ASPA, CHODL, GNG7, P2RY14, and PI16) were identified. The combined classifier showed high apparent diagnostic performance in the discovery datasets (AUC = 1.000 in the EBV cohort and 0.981 in TCGA–STAD) and retained value in GSE27342 (AUC = 0.771). RT‐qPCR confirmed differential expression of these genes between a gastric epithelial cell line and a gastric cancer cell line, providing general tumor‐versus‐normal experimental support rather than EBV‐specific validation. A five‐gene risk signature stratified overall survival, with 1‐, 2‐, and 3‐year AUCs of 0.634, 0.645, and 0.622, which improved to 0.716, 0.768, and 0.693 after integration with age and stage. Single‐cell analysis localized the strongest signature signal to fibroblasts and B cells and revealed enhanced MIF‐, PTN‐, and TNFSF13B‐related communication in tumors. High‐risk tumors showed higher predicted IC50 values for paclitaxel and 5‐fluorouracil.
Conclusion
We identified a five‐gene signature with strong diagnostic value and biologically meaningful prognostic relevance in EBVaGC, highlighting fibroblast‐ and B‐cell‐associated microenvironmental programs in aggressive disease.