Single-cell transcriptomics identifies fibroblast associated immune heterogeneity and prognostic signatures in bladder cancer

X Xiaojuan Tang L Ling Liu M Min Gao P Peng Duan S Sheng Li Z Zilong Yuan Q Qiang Xia L Lei Xi Y Yan Tan

Abstract

Abstract The immune microenvironment and prognosis of bladder cancer (BLCA) remain ongoing challenges in its treatment. This study aimed to establish predictive prognostic indicators and investigate the immune microenvironment to enhance clinical treatment strategies. A single-cell transcriptional atlas was constructed using single-cell RNA-seq data from patients with bladder cancer, focusing on fibroblast-related gene expression, intercellular communication, metabolic pathways inferred by single-cell flux estimation analysis, and transcription factor networks. Fibroblast-associated prognostic gene signatures were validated using data from The Cancer Genome Atlas, and a prognostic model was developed to stratify patients with bladder cancer into high- and low-risk groups. Analysis of three para-carcinoma single-cell samples revealed the presence of 3,603 fibroblasts and 500 fibroblast-associated marker genes. Notably, key fibroblast-specific transcription factors, including MAF, TWIST1, and TCF21, were identified through SCENIC analysis. The incorporation of comprehensive RNA sequencing data enabled the discovery of prognostic markers associated with fibroblasts. Using this classification model, patient survival could be stratified into high- and low-risk categories based on the model. The results of our study highlight the prognostic genetic signatures associated with the fibroblast component of the immune microenvironment in BLCA, offering preliminary insights into prognostic assessment and potential therapeutic implications.

Article Details

Volume / Issue Vol. 16, Issue 1
Published February 03, 2026
ISSN 2045-2322
Publisher Nature Portfolio

Journal Info

Scientific Reports

Nature Portfolio

ISSN: 2045-2322 Open Access Life Sciences

Authors (9)

X

Xiaojuan Tang

L

Ling Liu

M

Min Gao

P

Peng Duan

S

Sheng Li

Z

Zilong Yuan

Q

Qiang Xia

L

Lei Xi

Y

Yan Tan