Abstract
Glioma stem cells (GSCs) play an important role in glioma recurrence and chemo-radiotherapy (CRT) resistance. Currently, there is a lack of efficient treatment approaches targeting GSCs. This study aimed to explore the potential personalized treatment of patients with GSC-enriched gliomas. Single-cell RNA sequencing (scRNA-seq) was used to identify the GSC-related genes. Then, machine learning methods were applied for clustering and validation. The least absolute shrinkage and selection operator (LASSO) and COX regression were used to construct the risk scores. Survival analysis was performed. Additionally, the incidence of chemo-radiotherapy resistance, immunotherapy status, and tumor treating field (TTF) therapy response were evaluated in high- and low-risk scores groups. Two GSC clusters exhibited significantly different stemness indices, immune microenvironments, and genomic alterations. Based on GSC clusters, 11-gene GSC risk scores were constructed, which exhibited a high predictive value for prognosis. In terms of therapy, patients with high GSC risk scores had a higher risk of resistance to chemotherapy. TTF therapy can comprehensively inhibit the malignant biological characteristics of the high GSC-risk-score gliomas. Our study constructed a GSC signature consisting of 11 GSC-specific genes and identified its prognostic value in gliomas. TTF is a promising therapeutic approach for patients with GSC-enriched glioma.
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