ArticleFrontiers in oncology2026
Interpretable machine learning-driven multi-omics risk stratification and drug repurposing nominates Treg/Th17 with gluconeogenesis/lactylation integration as a prognostic and druggable biomarker for glioblastoma patients.
Article in Frontiers in oncology, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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Abstract
Objective: Dysregulation of Treg/Th17 balance and gluconeogenesis/lactylation contributes to glioblastoma (GBM) progression. Hence, it is essential for gaining insights into their mechanisms in GBM. Methods: By integrating ssGSEA, Limma and WGCNA frameworks and GBM cerebral public bulk profiles from GEO database with gluconeogenesis and lactylation gene list acquired from Genecard database, we identified Treg/Th17 and gluconeogenesis/lactylation (TGL)-associated shared DEGs for GBM patients. Next, Lasso-cox regression analysis pointed out a TGL-associated risk stratification model in TCGA-GBM training cohort and GEO independent validation dataset. Besides, the immune and intratumoral heterogeneity between high-risk and low-risk groups were assessed. Besides, SHAP made Lasso-cox regression analysis interpretable and identification of TGL-associated hub gene. The expression value, and the association of hub gene with TGL and intratumoral features of GBM were further validated Results: Integrated TGL can guide the risk stratification and prognostic model construction for GBM patients. ODC1 can be considered as up-regulated TGL-related regulator involved in GBM pathogenesis. THZ-2-102-1 can be considered as potential drug targeting ODC1 for the treatment of GBM. Conclusion: Our study first integrated TGL-associated gene signature in GBM patient risk stratification and therapeutic framework for GBM patients via machine learning and multi-omics, which provides novel ideas into GBM patient clinical translation.
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