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Objectives: To establish a glutamine metabolism (GM)-based classification for glioblastoma (GBM) and evaluate its prognostic and immunotherapeutic implications. Methods: A total of 237 GBM patients from the Chinese Glioma Genome Atlas (CGGA) database were included as the training set, and 219 patients from the Gene Expression Omnibus (GEO) database served as the validation set. Consensus clustering was performed based on the expression profiles of 13 GM-associated genes to identify robust subgroups. Differences between clusters were analyzed using clinical indices, genomic and transcriptomic biomarkers. Tumor response to immune checkpoint inhibitors (ICIs) was predicted using the tumor immune dysfunction and exclusion (TIDE) algorithm, tumor microenvironment (TME) score, T cell inflammation score, and SubMap algorithm. A GM-based classifier was subsequently developed and validated. Results: Consensus clustering of the training set revealed 2 distinct subgroups (cluster 1 and cluster 2) with significant prognostic differences; cluster 2 exhibited poorer overall survival. Immunotherapy response prediction indicated that cluster 2 had a lower likelihood of benefiting from ICIs. The newly developed GM-based classifier demonstrated high accuracy (AUC > 0.9) and maintained strong consistency with the original clustering in terms of subtype classification and immunotherapy prediction across both datasets. Conclusion: This study establishes a robust classification system for GBM based on glutamine metabolism-related genes, which effectively stratifies patients into prognostic subgroups and predicts immunotherapy response. The GM-based classifier offers a valuable tool for guiding clinical prognosis and treatment decisions in GBM.
Wang et al. (Fri,) studied this question.