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<p>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 NDRG1_pT346 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 Glioma_MES, Glioma_PN, and Glioma_CL 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.</p>

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Integrated proteomic and single-nucleus transcriptomic profiling identifies prognostic markers and therapeutic targets in glioblastoma

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2026年5月30日

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  • Chengyong Yang,
  • Junchi Luo,
  • Sha Yang,
  • Mei Deng,
  • Tao Xiong,
  • Junheng Zhang,
  • Wenjing Yang,
  • Jiqin Zhang,
  • Xin Xiang,
  • Liangzhao Chu,
  • Ying Tan

摘要

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 NDRG1_pT346 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 Glioma_MES, Glioma_PN, and Glioma_CL 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.