Integrative Single-Cell and Bulk Transcriptome Analysis Reveals Molecular Mechanisms, Prognostic Signatures, and Therapeutic Response in Glioma.
TL;DR
This integrative analysis decodes the cellular architecture of glioma and establishes a robust neutrophil-associated prognostic model, offering a clinically relevant tool for risk stratification and precision treatment in glioma.
Abstract
Background
Glioma is a highly aggressive primary brain tumor, with its complex tumor microenvironment and cellular heterogeneity posing major obstacles to effective treatment. Although single-cell and bulk transcriptome sequencing have advanced our understanding of glioma biology, integrating these modalities to uncover dynamic immune mechanisms and develop clinically actionable prognostic tools remains a critical unmet need.
Methods
We integrated single-cell RNA sequencing data with bulk transcriptomic data sets from GEO and TCGA. A comprehensive single-cell landscape of glioma was constructed using Seurat, followed by cell type annotation and identification of neutrophil-associated genes. A novel prognostic risk model was developed using LASSO regression and validated in multiple independent cohorts. We further evaluated the model's relationship with immune cell infiltration, pathway activity, drug sensitivity, and ligand-receptor interactions. A nomogram integrating the risk score and clinical features was also established.
Results
We annotated 7 major cell types, with neutrophils exhibiting the highest contribution to glioma pathogenesis. A 5-gene prognostic model (MTPN, BHLHE40, UPP1, G0S2, LSP1) was constructed. The resulting risk score stratified patients into high- and low-risk groups, with high-risk patients showing significantly worse overall survival across training, test, and external validation data sets. The risk score was an independent prognostic factor and correlated with key pathways (p53, IL6-JAK-STAT3, MAPK) and altered immune infiltration, including increased neutrophils and Tregs. Drug sensitivity analysis revealed significant associations with vinblastine, cytarabine, and olaparib. A nomogram accurately predicted 1- and 3-year survival. Single-cell analysis confirmed model gene expression across multiple cell types, with strong ligand-receptor interactions between macrophages, dendritic cells, and microglia.
Conclusion
This integrative analysis decodes the cellular architecture of glioma and establishes a robust neutrophil-associated prognostic model. The risk score serves as an independent predictor of patient survival, immune landscape, and therapeutic response, offering a clinically relevant tool for risk stratification and precision treatment in glioma.