Mapping Glioma Progression: Single-cell RNA Sequencing Illuminates Cell-cell Interactions and Immune Response Variability

Overview

Journal Discov Oncol

Publisher Springer

Specialty Oncology

Date 2025 Mar 12

PMID 40072722

Authors

Xia Li

Shenbo Chen

Ming Ding

Hui Ding

Kun Yang

Affiliations

Soon will be listed here.

Abstract

Background: Glioma, the most common primary cancer of the central nervous system, characterizes significant heterogeneity, presenting major challenges for therapeutic approaches and prognosis. In this study, the interactions between malignant glioma cells and macrophages/monocytes, as well as their influence on tumor progression and treatment responses, were explored using comprehensive single-cell RNA sequencing analysis.

Methods: RNA-seq data from the TCGA and CGGA databases were integrated and an in-depth analysis of glioma samples was performed using single-cell RNA sequencing, functional enrichment analysis, developmental trajectory analysis, cell-cell communication analysis, and gene regulatory network analysis. Furthermore, a prognostic model based on risk scores was developed, and its predictive performance was assessed through immune cell infiltration analysis and immune treatment response evaluation.

Results: Fourteen distinct glioma cellular subpopulations, seven primary cell types, and four macrophage/monocyte subtypes were identified. Developmental trajectory analysis offered insights into the origins and heterogeneity of malignant cells as well as macrophages/monocytes. Cell communication analysis revealed the interaction of macrophages and monocytes with malignant cells through several pathways, including the macrophage migration inhibitory factor and secreted phosphoprotein 1 pathways, engaging in key ligand-receptor interactions that influence tumor behavior. Categorization based on these communication characteristics was significantly correlated with overall survival. Immune cell infiltration analysis highlighted variations in immune cell abundance across different subgroups, possibly linked to differing responses to immunotherapy. This predictive model, comprising 29 prognostic genes, demonstrated high accuracy and robustness across multiple independent cohorts.

Conclusion: This study reveals the complex heterogeneity of the glioma microenvironment and enhances the understanding of diverse characteristics of glioma cell subsets. At the same time, it lays a foundation for the development of therapeutic strategies and prognostic models targeting the glioma microenvironment.

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