Evidence map›Paper›PMID 41689690›Full record

ArticleDiscover oncology2026

Integration of bulk and single-cell transcriptomic sequencing reveals the neutrophil heterogeneity in bladder cancer and establishes a prognostic model.

Ying-Xue Song, Xiao-Lin Xia, Zhi-Ming Wu, Ye Yao, Jun-Yu Liang, Sheng-Jie Guo, Kai Yao, Hui Chang

Abstract read
In one paragraph

Article in Discover oncology, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it
0cells of the map it votes in
0citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

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.

2 · The registry

The trial behind it

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Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

0 citing papers in PubMed.

No citing paper in PubMed yet.

4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

8 authors.

Ying-Xue Song *Department of Radiation Oncology, State Key Laboratory of Oncology in South China, Collaborative Innovation Center for Cancer Medicine, Sun Yat-sen University Cancer Center, Guangzhou, 510060, China.
Xiao-Lin Xia *Department of Oncology, Yunfu People's Hospital, Southern Medical University, Yunfu, 527300, China.
Zhi-Ming WuDepartment of Urology, State Key Laboratory of Oncology in South China, Collaborative Innovation Center for Cancer Medicine, Sun Yat-sen University Cancer Center, Guangzhou, 510060, China.
Ye YaoDepartment of Radiation Oncology, State Key Laboratory of Oncology in South China, Collaborative Innovation Center for Cancer Medicine, Sun Yat-sen University Cancer Center, Guangzhou, 510060, China.
Jun-Yu LiangDepartment of Radiation Oncology, State Key Laboratory of Oncology in South China, Collaborative Innovation Center for Cancer Medicine, Sun Yat-sen University Cancer Center, Guangzhou, 510060, China.
Sheng-Jie GuoDepartment of Urology, State Key Laboratory of Oncology in South China, Collaborative Innovation Center for Cancer Medicine, Sun Yat-sen University Cancer Center, Guangzhou, 510060, China. guoshj@sysucc.org.cn.
Kai YaoDepartment of Urology, State Key Laboratory of Oncology in South China, Collaborative Innovation Center for Cancer Medicine, Sun Yat-sen University Cancer Center, Guangzhou, 510060, China. yaokai@sysucc.org.cn.
Hui ChangDepartment of Radiation Oncology, State Key Laboratory of Oncology in South China, Collaborative Innovation Center for Cancer Medicine, Sun Yat-sen University Cancer Center, Guangzhou, 510060, China. changhui@sysucc.org.cn.ORCID http://orcid.org/0000-0001-6651-5101

Funding

Guangdong Basic and Applied Basic Research Foundation, China 2020A1515010037
6 · The paper itself

Abstract

Neutrophils are crucial immune components within the tumor microenvironment, significantly impacting tumor progression and anti-tumor immunity. To systematically characterize the heterogeneity of neutrophils in bladder cancer (BLCA), we integrated large-scale single-cell RNA sequencing (scRNA-seq) data of BLCA to define the transcriptomic landscape of neutrophil subtypes. Functional enrichment, pseudotime analysis, cell-cell communication, and deconvolution of bulk RNA sequencing (RNA-seq) samples from BLCA were conducted to comprehensively characterize the biological profiles and functions, as well as the prognostic relevance of neutrophil subtypes. A machine learning-based predictive model was developed based on the balance of prognosis-related neutrophil subtypes. We identified five distinct subtypes of neutrophils in BLCA and focused on two subtypes that were prognostically antagonistic. VEGFA+ neutrophils (Neu_0), characterized by pro-angiogenic, immunosuppressive, and extracellular matrix remodeling signatures, showed a significant correlation with poorer survival. GBP1 + neutrophils (Neu_4), characterized by response to interferon, exhibited increased innate immune activities and the production of cytokines that activate anti-tumor immunity, significantly correlated with improved survival. Pseudotime analysis positioned both Neu_0 and Neu_4 as terminal states. Cell-cell communication further identified Neu_0 as a hub orchestrating multiple pro-tumorigenic interactions. The predictive model based on the balance of Neu_0 and Neu_4 effectively stratified BLCA patients into distinct risk groups with significant differences in clinical outcomes, immune landscapes, and response profiles to antibody-drug conjugate (ADC) treatment. The investigation provided novel insights into the functional profiles of neutrophils in BLCA and offered a novel tool for guiding therapeutic strategies in BLCA.

Indexed as

Bladder cancerImmune infiltrationMachine learningNeutrophilSingle-cell RNA sequencing

Identifiers

PMID41689690
PMCPMC12917091

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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.