Large-scale bulk and single-cell RNA sequencing combined with machine learning reveals glioblastoma-associated neutrophil heterogeneity and establishes a VEGFA⁺ neutrophil prognostic model.

ABSTRACT:

Background

Neutrophils play a key role in the tumor microenvironment (TME); however, their functions in glioblastoma (GBM) are overlooked and insufficiently studied. A detailed analysis of GBM-associated neutrophil (GBMAN) subpopulations may offer new insights and opportunities for GBM immunotherapy.

Methods

We analyzed single-cell RNA sequencing (scRNA-seq) data from 127 isocitrate dehydrogenase (IDH) wild-type GBM samples to characterize the GBMAN subgroups, emphasizing developmental trajectories, cellular communication, and transcriptional networks. We implemented 117 machine learning combinations to develop a novel risk model and compared its performance to existing glioma models. Furthermore, we assessed the biological and molecular features of the GBMAN subgroups in pa

SUBMITTER: Yang Y 

PROVIDER: S-EPMC11972500 | biostudies-literature | 2025 Apr

REPOSITORIES: biostudies-literature