ArticleClinical & translational oncology : official publication of the Federation of Spanish Oncology Societies and of the National Cancer Institute of Mexico2024
Construction of a disulfidptosis-related glycolysis gene risk model to predict the prognosis and immune infiltration analysis of gastric adenocarcinoma.
Article in Clinical & translational oncology : official publication of the Federation of Spanish Oncology Societies and of the National Cancer Institute of Mexico, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.
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5 citing papers in PubMed.
- Disulfidptosis: molecular mechanisms and therapeutic targets.Signal transduction and targeted therapy · 2026Review
- High ALG1 Expression Is Correlated With Poor Prognosis and the Immune Microenvironment in Glioma.Journal of cellular and molecular medicine · 2026Article
- Proteomics reveals the regulation of ALG1 expression by lentivirus-mediated THBS1 overexpression and its mechanism in meibomian gland carcinoma.Oncology letters · 2025Article
- Advances in the study of disulfidptosis in digestive tract tumors.Discover oncology · 2025Review
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Abstract
backgroundThe pattern of cell death known as disulfidptosis was recently discovered. Disulfidptosis, which may affect the growth of tumor cells, represents a potential new approach to treating tumors. Glycolysis affects tumor proliferation, invasion, chemotherapy resistance, the tumor microenvironment (TME), and immune evasion. However, the efficacy and therapeutic significance of disulfidptosis-related glycolysis genes (DRGGs) in stomach adenocarcinoma (STAD) remain uncertain.
methodsSTAD clinical data and RNA sequencing data were downloaded from the TCGA database. DRGGs were screened using Cox regression and Lasso regression analysis to construct a prognostic risk model. The accuracy of the model was verified using survival studies, receiver operating characteristic (ROC) curves, column plots, and calibration curves. Additionally, our study investigated the relationships between the risk scores and immune cell infiltration, tumor mutational burden (TMB), and anticancer drug sensitivity.
resultsWe have successfully developed a prognosis risk model with 4 DRGGs (NT5E, ALG1, ANKZF1, and VCAN). The model showed excellent performance in predicting the overall survival of STAD patients. The DRGGs prognostic model significantly correlated with the TME, immune infiltrating cells, and treatment sensitivity.
conclusionsThe risk model developed in this work has significant clinical value in predicting the impact of immunotherapy in STAD patients and assisting in the choice of chemotherapeutic medicines. It can correctly estimate the prognosis of STAD patients.
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