Interpretable prognostic modeling of glioblastoma using cross-cohort transcriptome integration and machine learning approaches

Clinical Snapshot

Key Findings

• P Value: Significant survival differences between risk groups (specific p-value not provided)

• Effect Size: Concordance index of 0.72

• Primary Outcome: Overall survival prediction using 22-gene signature

• Nnt Or Sensitivity: C-index 0.72 indicates moderate discriminative ability for survival prediction

• Confidence Interval: Not reported

Clinical Application

Requires transcriptomic profiling capabilities, which may limit routine clinical implementation Would need validation in Australian GBM cohorts and integration with existing neuro-oncology pathways. TGA approval would be required for diagnostic use, and PBS funding considerations for genomic testing would apply Patients with newly diagnosed glioblastoma requiring prognostic stratification

Abstract

Glioblastoma (GBM) is an aggressive brain tumor with highly variable patient outcomes due to pronounced molecular heterogeneity. Prognosis remains dismal (median survival ∼15 months) and current prognostic models often function as "black boxes," lacking interpretability and limiting clinical utility. There is an urgent need for interpretable prognostic tools to better stratify GBM patients. This study performed cross-cohort, cross-platform transcriptome data integration (TCGA RNA-seq and GEO microarrays) and incorporated inferred immunogenomic features to capture GBM's complexity. An automated machine learning (AutoML) pipeline tested over 100 algorithmic combinations to build an optimal survival prediction model. The final model is a 22-gene signature, and SHAP (SHapley Additive exPlanations) analysis was applied to explain each gene's contribution to risk. Key genes identified by the model (e.g. UBE2W, EID1, HS2ST1) were validated by qRT-PCR and Western blot, confirming their dysregulated expression in GBM cell lines. The 22-gene model achieved a concordance index of ∼0.72 and was validated on independent cohorts (TCGA training and GEO validation), demonstrating robust performance. It effectively stratified patients into high- and low-risk groups with significant survival differences. High-risk tumors were associated with an immune-cell-enriched yet immune-evasive microenvironment, showing greater infiltration of immunosuppressive cells and higher TIDE scores (indicating immune escape). In contrast, low-risk patients had a more favorable immune profile, and their tumors were predicted to be more sensitive to multiple chemotherapeutic agents. This interpretable transcriptome-based integrative prognostic model can serve as a valuable tool for GBM risk stratification and may guide therapeutic decision-making by highlighting potential targets. Not only does it improve outcome prediction, but it also identifies novel prognostic biomarkers, holding promise for personalized treatment and clinical translation in glioblastoma.

References

1. 1.Shan, M., Zhao, Z. L., Liang, S. M., Du, N. D., Sheng, W. W., Shen, J., & Chen, X. H. (2026). Interpretable prognostic modeling of glioblastoma using cross-cohort transcriptome integration and machine learning approaches. Biochemical and Biophysical Research Communications, 153458. https://doi.org/10.1016/j.bbrc.2026.153458