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Evaluation of Biomarkers in Glioma by Immunohistochemistry on Paraffin-Embedded 3D Glioma Neurosphere Cultures

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Published on: January 9, 2019

Graph informed biomarker discovery framework using transcriptomic machine learning for glioblastoma prognosis.

Osama Mahmoud¹, Mahmoud Mounir², Walaa Gad²

¹Information Systems Department, Faculty of Computer and Information Sciences, Ain Shams University, Cairo, Egypt. osama.mahmoud@cis.asu.edu.eg.

Scientific Reports

|June 23, 2026

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Graph-Informed Biomarker Discovery (GIBD) integrates gene expression and protein networks to identify glioblastoma prognostic signals. This transcriptomics-only framework shows promise for predicting patient risk and guiding future research.

Area of Science:

Bioinformatics
Computational Biology
Genomics

Background:

Identifying reproducible prognostic biomarkers from high-dimensional transcriptomics is difficult due to ignored network context.
Gene-level models often fail to capture complex biological interactions crucial for accurate prognosis.

Purpose of the Study:

To develop and validate a novel framework, Graph-Informed Biomarker Discovery (GIBD), for identifying prognostic signals in primary glioblastoma.
To integrate RNA-sequencing data with protein-protein interaction networks for robust biomarker discovery.

Main Methods:

GIBD framework construction using weighted protein-protein interaction (WPPI) features from STRING and RNA-seq data.
Model development, feature selection, and threshold setting were performed exclusively on The Cancer Genome Atlas (TCGA) cohort.

Keywords:

External validation Glioblastoma Graph-informed machine learning Prognostic modeling Transcriptomics Weighted protein–protein interaction

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External validation of the locked GIBD-XGBoost K100 model was conducted using the Chinese Glioma Genome Atlas (CGGA) cohort.

Main Results:

The locked GIBD-XGBoost K100 model, using 100 features, achieved an out-of-fold AUC of 0.617 in TCGA and an AUC of 0.609 in the CGGA cohort.
External validation in CGGA demonstrated a sensitivity of 73.9%, specificity of 50.6%, and balanced accuracy of 62.3%.
SHAP analysis identified TSPAN13 as a key contributor, and Gene Set Enrichment Analysis (GSEA) revealed significant enrichment of inflammatory, hypoxic, and angiogenic pathways in high-risk groups.

Conclusions:

GIBD successfully developed a locked, transcriptomics-only prognostic model for glioblastoma, demonstrating external validation potential.
The framework identified key biological pathways associated with high-risk glioblastoma, including inflammation and angiogenesis.
The GIBD model provides a promising transcriptomic risk-prioritization signal that warrants further prospective recalibration and multimodal validation for clinical translation.