Decoding the Glioma Microenvironment: Single-Cell RNA Sequencing Reveals the Impact of Cell-to-Cell Communication on Tumor Progression and Immunotherapy Response

Abstract

Background Glioma is the most common primary tumor of the central nervous system, characterized by high heterogeneity that poses significant challenges to therapeutic strategies and prognostic assessment. This study investigates the cell-cell communication between malignant glioma cells and macrophages/monocytes and its impact on tumor progression and treatment response through in-depth single-cell RNA sequencing analysis. Methods We integrated RNA-seq data from the TCGA and CGGA databases and conducted a comprehensive analysis of glioma samples using single-cell RNA sequencing, functional enrichment analysis, developmental trajectory analysis, cell-cell communication analysis, and gene regulatory network analysis. Additionally, we constructed a prognostic model based on risk scores and evaluated the predictive performance of the model through analyses of immune cell infiltration and immune treatment response. Results We successfully identified 14 glioma cellular subpopulations and 7 primary cell types, as well as 4 subtypes of macrophages/monocytes. Developmental trajectory analysis revealed the origins and heterogeneity of malignant cells and macrophages/monocytes. Cellular communication analysis found that macrophages and monocytes interact with malignant cells through multiple pathways, including MIF (Macrophage Migration Inhibitory Factor) and SPP1 (Secreted Phosphoprotein 1), engaging in several key ligand-receptor pairs that influence tumor behavior. Subgroup stratification based on cellular communication characteristics showed a significant association with overall survival (OS). Immune cell infiltration analysis indicated differences in the abundance of immune cells among various subgroups, which may correlate with responses to immunotherapy. A predictive model composed of 29 prognostic genes demonstrated excellent accuracy and robustness across multiple independent cohorts. Conclusion Our study reveals the complex heterogeneity of the glioma microenvironment and strengthens the understanding of the diversity and characteristics of glioma cell subpopulations, preliminarily establishing a prognostic model related to prognosis. These findings provide a basis for the development of therapeutic strategies and prognostic models targeting the glioma microenvironment.