Introduction
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.
Methods
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.
Results
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.
Conclusion
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.
BackgroundAberrant endoplasmic reticulum stress (ERS) plays an important role in multiple cardiovascular diseases. However, their implication in intracranial aneurysms (IAs) remains unclear. We designed this study to explore the general expression pattern and potential functions of ERS in IAs.MethodsFive Gene Expression Omnibus (GEO) microarray datasets were used as the training cohorts, and 3 GEO RNA sequencing (RNA-seq) datasets were used as the validating cohorts. Differentially expressed genes (DEGs), functional enrichment, Lasso regression, logistic regression, ROC analysis, immune cell profiling, vascular smooth muscle cell (VSMC) phenotyping, weighted gene coexpression network analysis (WGCNA), and protein-protein interaction (PPI) analysis were applied to investigate the role of ERS in IA. Finally, we predicted the upstream transcription factor (TF)/miRNA and potential drugs targeting ERS.ResultsSignificant DEGs were majorly associated with ERS, autophagy, and metabolism. Eight-gene ERS signature and IRE1 pathway were identified during the IA formation. WGCNA showed that ERS was highly associated with a VSMC synthesis phenotype. Next, ERS-VSMC-metabolism-autophagy PPI and ERS-TF-miRNA networks were constructed. Finally, we predicted 9 potential drugs targeting ERS in IAs.ConclusionERS is involved in IA formation. Upstream and downstream regulatory networks for ERS were identified in IAs. Novel potential drugs targeting ERS were also proposed, which may delay IA formation and progress.
Accumulating evidence has demonstrated that the immune cells have an emerging role in controlling anti-tumor immune responses and tumor progression. The comprehensive role of mast cell in glioma has not been illustrated yet. In this study, 1,991 diffuse glioma samples were collected from The Cancer Genome Atlas (TCGA) and the Chinese Glioma Genome Atlas (CGGA). xCell algorithm was employed to define the mast cell-related genes. Based on mast cell-related genes, gliomas were divided into two clusters with distinct clinical and immunological characteristics. The survival probability of cluster 1 was significantly lower than that of cluster 2 in the TCGA dataset, three CGGA datasets, and the Xiangya cohort. Meanwhile, the hypoxic and metabolic pathways were active in cluster 1, which were beneficial to the proliferation of tumor cells. A potent prognostic model based on mast cell was constructed. Via machine learning, DRG2 was screened out as a characteristic gene, which was demonstrated to predict treatment response and predict survival outcome in the Xiangya cohort. In conclusion, mast cells could be used as a potential effective prognostic factor for gliomas.
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