Predicting Treatment Outcomes in Glioblastoma: A Risk Score Model for TMZ Resistance and Immune Checkpoint Inhibition

Abstract

Glioblastoma (GBM) presents significant therapeutic challenges due to its invasivenature and resistance to standard chemotherapy, i.e., temozolomide (TMZ). This studyaimed to identify gene signatures that predict poor TMZ response and high PD−L1/PD−1tumor expression, and explore potential sensitivity to alternative drugs. We analyzedThe Cancer Genome Atlas (TCGA) biopsy data to identify differentially expressed genes(DEGs) linked to these characteristics. Among 33 upregulated DEGs, 5 were significantlycorrelated with overall survival. A risk score model was built using these 5 DEGs, classifyingpatients into low-, medium-, and high-risk groups. We assessed immune cellinfiltration, immunosuppressive mediators, and epithelial–mesenchymal transition (EMT)markers in each group using correlation analysis, Gene Set Enrichment Analysis (GSEA),and machine learning. The model demonstrated strong predictive power, with high-riskpatients exhibiting poorer survival and increased immune infiltration. GSEA revealedupregulation of immune and EMT-related pathways in high-risk patients. Our analyses suggest that high-risk patients may exhibit limited response to PD−1 inhibitors, but couldshow sensitivity to etoposide and paclitaxel. This risk score model provides a valuabletool for guiding therapeutic decisions and identifying alternative chemotherapy options toenable the development of personalized and cost-effective treatments for GBM patients.