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 - Report - MDSpire
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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
Machine Learning-Based Multi-Omics Analysis for Risk Assessment in GBM
Overview
This study identifies Treg/Th17 and gluconeogenesis/lactylation as key biomarkers in glioblastoma (GBM) patients. A risk stratification model based on these biomarkers was developed.
Background
Glioblastoma (GBM) is the most aggressive brain tumor, with a poor prognosis and limited treatment options. Understanding the molecular mechanisms, particularly the roles of Treg/Th17 balance and metabolic pathways like gluconeogenesis, is crucial.
Data Highlights
No numerical data or trial data provided in the source material.
Key Findings
Integration of Treg/Th17 and gluconeogenesis/lactylation signatures can guide risk stratification in GBM patients.
ODC1 was identified as an up-regulated regulator associated with TGL in GBM pathogenesis.
The study utilized machine learning and multi-omics approaches to develop a prognostic model for GBM.
Hub gene heterogeneity was assessed at single-cell levels, providing insights into intratumoral features.
Clinical Implications
The findings suggest that Treg/Th17 balance and metabolic pathways may serve as biomarkers for risk assessment in GBM.
Conclusion
This study presents a framework for integrating multi-omics data to enhance risk stratification in glioblastoma patients.
