SGlycosylation Gene Signatures as Prognostic Biomarkers in Glioblastoma
Glioblastoma (GBM) is an aggressive brain tumor characterized by significant heterogeneity. This study investigates the role of glycosylation-related genes in GBM subtyping, prognosis, and response to therapy.
We analyzed mRNA expression data and clinical information from The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) databases. Glycosylation-related genes were selected for differential expression analysis, sample clustering, and survival analysis. Immune cell infiltration and drug sensitivity were evaluated using CIBERSORT and oncoPredict, respectively. A prognostic model was constructed with Lasso regression.
GBM samples were stratified into two glycosylation-related subtypes, showing distinct survival outcomes, with higher glycosylation expression correlating with poorer prognosis. Immune microenvironment analysis revealed differences in T-cell infiltration and immune checkpoint expression between subtypes, indicating variable immunotherapy responses. The prognostic model based on glycosylation genes demonstrated significant predictive value for patient survival.
Glycosylation-related gene expression contributes to GBM heterogeneity and is a valuable biomarker for prognosis and treatment stratification. This study provides insights into personalized treatment approaches for GBM based on glycosylation-related molecular subtypes.
Lasso regression; glioblastoma; glycosylation genes; immune microenvironment; prognostic model; survival analysis.
© 2025 The Author(s). Annals of Clinical and Translational Neurology published by Wiley Periodicals LLC on behalf of American Neurological Association.
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