ArticleBiology direct2025
Large-scale bulk and single-cell RNA sequencing combined with machine learning reveals glioblastoma-associated neutrophil heterogeneity and establishes a VEGFA
Article in Biology direct, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
10 citing papers in PubMed.
- MRI-based radiomics model for predicting VEGFA expression and prognosis in lower-grade glioma.Medical physics · 2026Article
- Leveraging single-cell and spatial omics for brain tumour insights to improve therapeutic strategies.Molecular brain · 2026Review
- Integration of bulk and single-cell transcriptomic sequencing reveals the neutrophil heterogeneity in bladder cancer and establishes a prognostic model.Discover oncology · 2026Article
- Spatiotemporal dynamics of tumor-associated neutrophils: bridging the gap between cancer progression and immunotherapy.Molecular cancer · 2026Review
- Immunosuppressive mechanisms and therapeutic interventions shaping glioblastoma immunity.Nature cancer · 2026Review
- Neutrophils in the hepatocellular carcinoma microenvironment: orchestrators of progression and immunity.Frontiers in immunology · 2026Review
- Comprehensive analysis of mitochondria-associated genes in glioblastoma via single-cell and bulk RNA sequencing.Discover oncology · 2025Article
- Integrating Machine Learning and Multi-Omics to Explore Neutrophil Heterogeneity.Biomedicines · 2025Review
- Progress in Transcriptomics and Metabolomics in Plant Responses to Abiotic Stresses.Current issues in molecular biology · 2025Review
- Single-cell sequencing combined with machine learning to identify glioma biomarkers and therapeutic targets.Frontiers in oncology · 2025Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
8 authors.
Funding
Abstract
backgroundNeutrophils 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.
methodsWe 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 patients.
resultsFrom integrated large-scale scRNA-seq data (498,747 cells), we identified 5,032 neutrophils and classified them into four distinct subtypes. VEGFA
conclusionOur study highlights the critical role of neutrophils in the TME of GBM, allowing for a better understanding of the composition and characteristics of GBMAN. The developed VNRS model serves as an effective tool for evaluating the risk and guiding clinical treatment strategies for GBM. CLINICAL TRIAL NUMBER: Not applicable.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.