ArticleCancer medicine2025
Integrated Analysis to Reveal Heterogeneity of Tumor-Associated Neutrophils in Glioma.
Article in Cancer medicine, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.
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
1 citing paper in PubMed.
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
10 authors.
Funding
Abstract
backgroundGlioma, characterized by its cellular and molecular heterogeneity, presents formidable challenges in treatment strategy and prognostic assessment. The tumor microenvironment (TME) profoundly influences tumor behavior and treatment response, with tumor-associated neutrophils (TANs) playing a complex but understudied role. This study aimed to investigate the heterogeneity and role of TANs in glioma and to develop a prognostic model.
methodsAnalysis of scRNA-seq data identified cellular subpopulations and differentially expressed neutrophil-related genes (DE-NRGs). Bulk RNA-seq was obtained from four independent datasets. Molecular subtypes of glioma samples were determined by consensus clustering. WGCNA was conducted to elucidate the association between gene modules and subtypes. We developed a risk score model. Expression of selected genes was confirmed using immunohistochemistry (IHC). In vitro experiments were also performed for functional verification, including CCK8, EdU, Transwell, and TUNEL assays.
resultsA total of 108 DE-NRGs for TANs were identified based on scRNA-seq data. Two molecular subtypes were characterized, showing significant differences in prognosis and clinical features. Immune-related analyses demonstrated varied immunological characteristics between subtypes. The risk score model was constructed with 7 genes, including AEBP1, CAVIN1, DCTD, DEPP1, DUSP6, FKBP9, and UGCG. It showed significant prognostic value and was validated across three external datasets. The mutation landscape highlighted higher IDH mutation prevalence in low-risk groups. Drug sensitivity analysis indicated TMZ resistance in high-risk groups. In vitro experiments showed that UGCG could promote glioma cell proliferation, migration, and invasion, while decreasing apoptosis.
conclusionThis study explored the heterogeneity of TANs and developed a prognostic model, providing insights for prognostic prediction and guiding personalized treatment strategies in glioma. Declaration of Generative AI in Scientific Writing: The authors declare nonuse of generative AI and AI-assisted technologies in the writing process.
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.