Glioblastoma (GBM) is the most aggressive primary malignant brain tumor in adults and remains associated with marked heterogeneity and poor prognosis. To identify prognostic biomarkers and characterize their cellular contexts, we integrated public proteomic and single-nucleus transcriptomic datasets. Protein expression data from the TCPA GBM cohort were used to construct a prognostic model by univariate and multivariable Cox regression analyses, followed by Kaplan–Meier survival, time-dependent ROC, and nomogram analyses. Single-nucleus RNA-seq data from GSE138794 were used to define the cellular distribution of model proteins and to perform marker-centered functional analyses. A four-protein signature consisting of BCL2, CTNNB1, CHEK2, and NDRG1ₚT346 was established. The derived risk score significantly stratified patients into high- and low-risk groups with distinct overall survival and progression-free survival outcomes, and remained independently associated with prognosis after adjustment for clinical covariates. In the single-cell atlas, inferCNV-guided refinement identified malignant glioma cells and further resolved them into GliomaMES, GliomaPN, and GliomaCL states. The four prognostic markers showed distinct cellular preferences: BCL2 and NDRG1 were mainly enriched in oligodendroglial-related populations and selected glioma states, whereas CHEK2 and CTNNB1 were more highly expressed in myeloid-associated populations. Among the four markers, only CHEK2- and NDRG1-defined malignant glioma subgroups showed sufficient transcriptional divergence for downstream analysis. CHEK2-positive glioma cells were enriched for DNA replication, chromosome segregation, DNA damage response, and checkpoint-related programs, whereas NDRG1-positive glioma cells were characterized by hypoxia response, glycolysis, and metabolic adaptation. CellChat analysis further revealed distinct communication patterns for CHEK2- and NDRG1-associated malignant glioma cells.
Yang et al. (Sat,) studied this question.