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PMID: 41689690 已发表 · epublish 英语

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

Discover oncology ·第 17 卷 ·第 1 期 ·2026-02-14

Song YX, Xia XL, Wu ZM, Yao Y, Liang JY, Guo SJ, Yao K, Chang H

摘要

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.

关键词
Bladder cancer Immune infiltration Machine learning Neutrophil Single-cell RNA sequencing
文献信息
期刊
Discover oncology
期刊简称
Discov Oncol
ISSN
2730-6011
发表日期
2026-02-14
语言
英语
国家/地区
United States
NLM ID
101775142
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