What question did this study set out to answer?

The research aims to classify glioblastoma into molecular subtypes that provide insights into prognosis and treatment.

January 21, 2026Current Issues in Molecular Biology0 citationsOpen Access

Identification of Tumor- and Immunosuppression-Driven Glioblastoma Subtypes Characterized by Clinical Prognosis and Therapeutic Targets

PZPei ZhangMinistry of Industry and Information Technology DLDan LiuMinistry of Industry and Information Technology XLXiaoyu LiuJinan University

Key Points

The research aims to classify glioblastoma into molecular subtypes that provide insights into prognosis and treatment.
Used RNA-seq data for pathway-based signatures to identify glioblastoma subtypes.
Implemented unsupervised consensus clustering to classify tumors into C1-GBM and C2-GBM.
Developed a neural network model to predict glioblastoma subtype classification.
Utilized Weighted Correlation Network Analysis to identify prognostic biomarkers.
Validated findings with both TCGA and CGGA-GBM datasets.
C1-GBM (immune-infiltrated) showed an average survival time of 313 days, while C2-GBM (inherent driving) had 452 days.
The neural network model accurately predicted glioblastoma subtypes and prognosis.
Identified nine hub genes as potential prognostic biomarkers.
Validation demonstrated strong drug sensitivity for C1-GBM with Methotrexate and Cisplatin, and Cytarabine for C2-GBM.

Abstract

Glioblastoma multiforme (GBM) is the most aggressive primary brain cancer (with a median survival time of 14.5 months), characterized by heterogeneity. Identifying prognostic molecular subtypes could provide a deeper exposition of GBM biology with potential therapeutic implications. In this study, we classified GBM into two prognostic subtypes, C1-GBM (n = 57; OS: 313 days) and C2-GBM (n = 109; OS: 452 days), using pathway-based signatures derived from RNA-seq data. Unsupervised consensus clustering revealed that only binary classification (cluster number, CN = 2; mean cluster consensus score = 0.84) demonstrated statistically prognostic differences. We characterized C1 and C2 based on oncogenic pathway and immune signatures. Specifically, C1-GBM was categorized as an immune-infiltrated “hot” tumor, with high infiltration of immune cells, particularly macrophages and CD4+ T cells, while C2-GBM as an “inherent driving” subtype, showing elevated activity in G2/M checkpoint genes. To predict the C1 or C2 classification and explore therapeutic interventions, we developed a neural network model. By using Weighted Correlation Network Analysis (WGCNA), we obtained the gene co-expression module based on both gene expression pattern and distribution among patients in TCGA dataset (n = 166) and identified nine hub genes as potentially prognostic biomarkers for the neural network. The model showed strong accuracy in predicting C1/C2 classification and prognosis, validated by the external CGGA-GBM dataset (n = 85). Based on the classification of the BP neural network model, we constructed a Cox nomogram prognostic prediction model for the TCGA-GBM dataset. We predicted potential therapeutic small molecular drugs by targeting subtype-specific oncogenic pathways and validated drug sensitivity (C1-GBM: Methotrexate and Cisplatin; C2-GBM: Cytarabine) by assessing IC50 values against GBM cell lines (divided into C1/C2 subtypes based on the nine hub genes) from the Genomics of Drug Sensitivity in Cancer database. This study introduces a pathway-based prognostic molecular classification of GBM with “hot” (C1-GBM) and “inherent driving” (C2-GBM) tumor subtypes, providing a prediction model based on hub biomarkers and potential therapeutic targets for treatments.

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