Abstract
Differentiation states of glioma cells correlated with prognosis and tumor-immune microenvironment (TIME) in patients with gliomas. We aimed to identify differentiation related genes (DRGs) for predicting the prognosis and immunotherapy response in patients with gliomas. We identified three differentiation states and the corresponding DRGs in glioma cells through single-cell transcriptomics analysis. Based on the DRGs, we separated glioma patients into three clusters with distinct clinicopathological features in combination with bulk RNA-seq data. Weighted correlation network analysis, univariate cox regression analysis and least absolute shrinkage and selection operator analysis were involved in the construction of the prognostic model based on DRGs. Distinct clinicopathological characteristics, TIME, immunogenomic patterns and immunotherapy responses were identified across three clusters. A DRG signature composing of 12 genes were identified for predicting the survival of glioma patients and nomogram model integrating the risk score and multi-clinicopathological factors were constructed for clinical practice. Patients in high-risk group tended to get shorter overall survival and better response to immune checkpoint blockage therapy. We obtained 9 candidate drugs through comprehensive analysis of the differentially expressed genes between the low and high-risk groups in the model. Our findings indicated that the risk score may not only contribute to the determination of prognosis but also facilitate in the prediction of immunotherapy response in glioma patients.
Highlights
Differentiation states of glioma cells correlated with prognosis and tumor-immune microenvironment (TIME) in patients with gliomas
The cells distributed in 23 clusters were annotated with cell types according to marker genes (Fig. 1E,F), in which cluster 14 and 18 were macrophages, cluster 10 were monocytes, cluster 12, 13 and 15 tended to be closely related to stem cells, cluster 19 tended to be related to epithelial cells, and the remaining clusters tended to be close to astrocytes
We identified three cell differentiation states in glioma tissues through analysis of single-cell RNAseq data from GEO database and the marker genes for specific cell states were subsequently merged as differentiation related genes (DRGs), based on which we separated glioma patients into three clusters with distinct clinicopathological features based on the bulk RNA-seq data from TCGA and GEO database
Summary
Differentiation states of glioma cells correlated with prognosis and tumor-immune microenvironment (TIME) in patients with gliomas. The multiple factors in the tumor-immune microenvironment play a great role in the differentiation of cancer stem cells (CSCs) and drive progression of tumors[22–24] It has been well-established that single-cell transcriptomics analysis provides a new approach for investigating the heterogeneity of tumors at cellular r esolution[25]. Considering that the effect of conventional chemotherapy against gliomas is not satisfactory due to drug resistance, comprehensive analysis of DRGs facilitates in the development of novel therapeutic gene targets for predicting candidate drugs[11]. We attended to explore the multiple differentiation states of glioma cells through analysis of single cell RNA sequencing (scRNA-seq) of gliomas to identify DRGs for predicting prognosis, immunotherapy response and candidate targeted drugs combining with bulk RNA-seq data
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