Discover Oncology · Published 2026-07-26 · DOI 10.1007/s12672-026-05648-z
Liu He, Lilei Zhuang, Shenbao Wu
Abstract Background Given the substantial disease burden of stomach adenocarcinoma (STAD)—the dominant and lethal subtype of gastric cancer—and the pivotal role of folate metabolism reprogramming in malignancy and treatment response, this study aimed to construct a prognostic model utilizing folate metabolism-related genes (FMRGs). Such a model is urgently required to supplement traditional TNM staging and to guide personalized precision medicine. Methods Interrogation of TCGA and GEO transcriptomic profiles enabled the identification of differentially expressed FMRGs. These genes subsequently facilitated the construction of a prognostic signature via LASSO-Cox regression, which was then subjected to external validation. We further evaluated the clinical relevance of this risk signature by exploring its correlations with the tumor immune microenvironment, immunotherapy efficacy, and drug sensitivity, employing CIBERSORT and ssGSEA analytical frameworks. Results A five-gene prognostic model established from 220 FMRGs demonstrated moderate prognostic performance in identifying high-risk stomach adenocarcinoma patients with poorer survival. The high-risk group exhibited immunosuppressive microenvironments with stromal activation, while the low-risk group demonstrated “hot” tumor phenotypes characterized by higher immunogenicity and superior responses to immunotherapy and chemotherapy. Consequently, this model serves as a robust independent prognostic indicator. Conclusion This five-gene folate metabolism signature shows moderate prognostic value in STAD and may help inform future investigations of the tumor immune microenvironment and therapeutic stratification.
Abstract from DOAJ. Public domain (CC0 1.0).
Read the article at the publisher →
He, L., Zhuang, L., Wu, S. (2026). Construction of a risk model based on folate metabolism-related genes to predict prognosis and immunological characteristics of stomach adenocarcinoma. Discover Oncology. https://doi.org/10.1007/s12672-026-05648-z