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Single-cell transcriptomics identifies fibroblast associated immune heterogeneity and prognostic signatures in bladder cancer
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  • Published: 03 February 2026

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

  • Xiaojuan Tang1 na1,
  • Ling Liu4 na1,
  • Min Gao4,
  • Peng Duan5,
  • Sheng Li1,
  • Zilong Yuan6,
  • Qiang Xia7,
  • Lei Xi3 &
  • …
  • Yan Tan2,7 

Scientific Reports , Article number:  (2026) Cite this article

We are providing an unedited version of this manuscript to give early access to its findings. Before final publication, the manuscript will undergo further editing. Please note there may be errors present which affect the content, and all legal disclaimers apply.

Subjects

  • Biomarkers
  • Cancer
  • Computational biology and bioinformatics
  • Oncology

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.

Data availability

RNA sequencing data used in this study are available in the Gene Expression Omnibus (GEO) under accession code GSE129845.

Abbreviations

BLCA:

Bladder cancer

TME:

Tumour microenvironment

CAFs:

Cancer-associated fibroblasts

TCGA:

The Cancer Genome Atlas

GEO:

Gene Expression Omnibus

PCA:

Principal component analysis

DEGs:

Differentially expressed genes

GO:

Gene ontology

GRNs:

Genetic regulatory networks

RAS:

Regulon activity score

SEEK:

Search-based exploration of expression

LASSO:

Least absolute shrinkage and selection operator

GC:

Gastric cancer

ROC:

Receiver operating characteristic

MAF:

Mutation annotation format

TMB:

Tumor mutation burden

GTEx:

Genotype-tissue expression

scFEA:

Single-cell flux estimation analysis

EMT:

Epithelial-mesenchymal transition

ECM:

Extracellular matrix

TGF-β:

Transforming growth factor-beta

BMPs:

Bone morphogenetic proteins

AUC:

Area under the curve

FBN1:

Fibrillin-1

PID1 :

Phosphotyrosine interaction domain-containing 1

PRELP :

Proline/arginine-rich end leucine-rich repeat protein

References

  1. Lenis, A. T., Lec, P. M., Chamie, K. & Mshs, M. D. Bladder cancer: A review. Jama 324(19), 1980–1991. https://doi.org/10.1001/jama.2020.17598 (2020).

    Google Scholar 

  2. Sung, H. et al. Global cancer statistics 2020: GLOBOCAN estimates of incidence and mortality worldwide for 36 cancers in 185 countries. CA Cancer J. Clin. 71(3), 209–249. https://doi.org/10.3322/caac.21660 (2021).

    Google Scholar 

  3. Omorphos, N. P., Piedad, J. C. P. & Vasdev, N. Guideline of guidelines: Muscle-invasive bladder cancer. Turk. J. Urol. 47(Supp. 1), S71–s8. https://doi.org/10.5152/tud.2020.20337 (2021).

    Google Scholar 

  4. Powles, T. et al. Bladder cancer: ESMO clinical practice guideline for diagnosis, treatment and follow-up. Ann. Oncol. 33(3), 244–258. https://doi.org/10.1016/j.annonc.2021.11.012 (2022).

    Google Scholar 

  5. Pfannstiel, C. et al. The tumor immune microenvironment drives a prognostic relevance that correlates with bladder cancer subtypes. Cancer Immunol. Res. 7(6), 923–938. https://doi.org/10.1158/2326-6066.CIR-18-0758 (2019).

    Google Scholar 

  6. Chen, Z., Chen, D., Song, Z., Lv, Y. & Qi, D. Mapping the tumor microenvironment in bladder cancer and exploring the prognostic genes by single-cell RNA sequencing. Front. Oncol. 12, 1105026. https://doi.org/10.3389/fonc.2022.1105026 (2022).

    Google Scholar 

  7. Meng, J. et al. Tumor immune microenvironment-based classifications of bladder cancer for enhancing the response rate of immunotherapy. Mol. Ther. Oncolytics. 20, 410–421. https://doi.org/10.1016/j.omto.2021.02.001 (2021).

    Google Scholar 

  8. Zhang, P. et al. Mitochondrial Pathway Signature (MitoPS) predicts immunotherapy response and reveals NDUFB10 as a key immune regulator in lung adenocarcinoma. J. Immunother Cancer.. 13(7). https://doi.org/10.1136/jitc-2025-012069 (2025).

  9. Wang, H. et al. Crosstalk of pyroptosis and cytokine in the tumor microenvironment: from mechanisms to clinical implication. Mol. Cancer. 23(1), 268. https://doi.org/10.1186/s12943-024-02183-9 (2024).

    Google Scholar 

  10. Chen, X. & Song, E. Turning foes to friends: targeting cancer-associated fibroblasts. Nat. Rev. Drug Discov. 18(2), 99–115. https://doi.org/10.1038/s41573-018-0004-1 (2019).

    Google Scholar 

  11. Cañizo, C. G. et al. Characterisation of the tumour microenvironment and PD-L1 granularity reveals the prognostic value of cancer-associated myofibroblasts in non-invasive bladder cancer. Oncoimmunology 14(1), 2438291. https://doi.org/10.1080/2162402x.2024.2438291 (2025).

    Google Scholar 

  12. Caramelo, B., Zagorac, S., Corral, S., Marqués, M. & Real, F. X. Cancer-associated fibroblasts in bladder cancer: Origin, biology, and therapeutic opportunities. Eur. Urol. Oncol. 6(4), 366–375. https://doi.org/10.1016/j.euo.2023.02.011 (2023).

    Google Scholar 

  13. Di Spirito, A., Balkhi, S., Vivona, V. & Mortara, L. Key immune cells and their crosstalk in the tumor microenvironment of bladder cancer: insights for innovative therapies. Explor. Target. Antitumor Ther. 6, 1002304. https://doi.org/10.37349/etat.2025.1002304 (2025).

    Google Scholar 

  14. Ghahremanifard, P., Chanda, A., Bonni, S. & Bose, P. TGF-β mediated immune evasion in cancer—spotlight on cancer-associated fibroblasts. 12(12), 3650. (2020).

  15. Crispen, P. L. & Kusmartsev, S. Mechanisms of immune evasion in bladder cancer. Cancer Immunol. Immunother. 69(1), 3–14. https://doi.org/10.1007/s00262-019-02443-4 (2020).

    Google Scholar 

  16. Ren, X. et al. Insights gained from single-cell analysis of immune cells in the tumor microenvironment. Annu. Rev. Immunol. 39, 583–609. https://doi.org/10.1146/annurev-immunol-110519-071134 (2021).

    Google Scholar 

  17. Luo, H. et al. Pan-cancer single-cell analysis reveals the heterogeneity and plasticity of cancer-associated fibroblasts in the tumor microenvironment. Nat. Commun. 13(1), 6619. https://doi.org/10.1038/s41467-022-34395-2 (2022).

    Google Scholar 

  18. Zhou, Z. et al. Integrated single-cell and bulk RNA sequencing analysis identifies a cancer-associated fibroblast-related gene signature for predicting survival and therapy in gastric cancer. BMC Cancer. 23(1), 108. https://doi.org/10.1186/s12885-022-10332-w (2023).

    Google Scholar 

  19. Dominguez, C. X. et al. Single-cell RNA sequencing reveals stromal evolution into LRRC15(+) myofibroblasts as a determinant of patient response to cancer immunotherapy. Cancer Discov. 10(2), 232–253. https://doi.org/10.1158/2159-8290.Cd-19-0644 (2020).

    Google Scholar 

  20. Zhang, M. et al. Development and validation of cancer-associated fibroblasts-related gene landscape in prognosis and immune microenvironment of bladder cancer. Front. Oncol. 13, 1174252. https://doi.org/10.3389/fonc.2023.1174252 (2023).

    Google Scholar 

  21. Gribov, A. et al. SEURAT: visual analytics for the integrated analysis of microarray data. BMC Med. Genomics. 3, 21. https://doi.org/10.1186/1755-8794-3-21 (2010).

    Google Scholar 

  22. Becht, E. et al. Dimensionality reduction for visualizing single-cell data using UMAP. Nat. Biotechnol. https://doi.org/10.1038/nbt.4314 (2018).

    Google Scholar 

  23. Zhang, X. et al. CellMarker: a manually curated resource of cell markers in human and mouse. Nucleic Acids Res. 47(D1), D721–d8. https://doi.org/10.1093/nar/gky900 (2019).

    Google Scholar 

  24. Lu, H. et al. CommPath: an R package for inference and analysis of pathway-mediated cell-cell communication chain from single-cell transcriptomics. Comput. Struct. Biotechnol. J. 20, 5978–5983. https://doi.org/10.1016/j.csbj.2022.10.028 (2022).

    Google Scholar 

  25. Alghamdi, N. et al. A graph neural network model to estimate cell-wise metabolic flux using single-cell RNA-seq data. Genome Res. 31(10), 1867–1884. https://doi.org/10.1101/gr.271205.120 (2021).

    Google Scholar 

  26. Friedman, J., Hastie, T. & Tibshirani, R. Regularization paths for generalized linear models via coordinate descent. J. Stat. Softw. 33(1), 1–22 (2010).

    Google Scholar 

  27. Shi, Z. D. et al. Integrated single-cell and spatial transcriptomic profiling reveals higher intratumour heterogeneity and epithelial-fibroblast interactions in recurrent bladder cancer. Clin. Transl Med. 13(7), e1338. https://doi.org/10.1002/ctm2.1338 (2023).

    Google Scholar 

  28. Joshi, R. S. et al. The role of Cancer-Associated fibroblasts in tumor progression. Cancers. 13(6). https://doi.org/10.3390/cancers13061399 (2021).

  29. Greco, L. et al. Epithelial to mesenchymal transition: A challenging playground for translational research. Current models and focus on TWIST1 relevance and gastrointestinal cancers. Int. J. Mol. Sci.. 22(21). https://doi.org/10.3390/ijms222111469 (2021).

  30. Chen, Z. et al. Single-cell RNA sequencing highlights the role of inflammatory cancer-associated fibroblasts in bladder urothelial carcinoma. Nat. Commun. 11(1), 5077. https://doi.org/10.1038/s41467-020-18916-5 (2020).

    Google Scholar 

  31. Sahai, E. et al. A framework for advancing our understanding of cancer-associated fibroblasts. Nat. Rev. Cancer. 20(3), 174–186. https://doi.org/10.1038/s41568-019-0238-1 (2020).

    Google Scholar 

  32. Chen, Y., McAndrews, K. M. & Kalluri, R. Clinical and therapeutic relevance of cancer-associated fibroblasts. Nat. Rev. Clin. Oncol. 18(12), 792–804. https://doi.org/10.1038/s41571-021-00546-5 (2021).

    Google Scholar 

  33. Naba, A. et al. The extracellular matrix: tools and insights for the omics era. Matrix Biol. 49, 10–24. https://doi.org/10.1016/j.matbio.2015.06.003 (2016).

    Google Scholar 

  34. Costanza, B., Umelo, I. A., Bellier, J., Castronovo, V. & Turtoi, A. Stromal modulators of TGF-β in cancer. J. Clin. Med. 6(1). https://doi.org/10.3390/jcm6010007 (2017).

  35. Yin, C., Liu, W. H., Liu, Y., Wang, L. & Xiao, Y. PID1 alters the antilipolytic action of insulin and increases lipolysis via inhibition of AKT/PKA pathway activation. PLoS One. 14(4), e0214606. https://doi.org/10.1371/journal.pone.0214606 (2019).

    Google Scholar 

  36. Yang, J. et al. Dual role of PID1 in regulating apoptosis induced by distinct anticancer-agents through AKT/Raf-1-dependent pathway in hepatocellular carcinoma. Cell. Death Discov. 9(1), 139. https://doi.org/10.1038/s41420-023-01405-1 (2023).

    Google Scholar 

  37. Hong, R. et al. PRELP has prognostic value and regulates cell proliferation and migration in hepatocellular carcinoma. J. Cancer. 11(21), 6376–6389. https://doi.org/10.7150/jca.46309 (2020).

    Google Scholar 

  38. Hopkins, J. et al. PRELP regulates Cell-Cell adhesion and EMT and inhibits retinoblastoma progression. Cancers. 14(19). https://doi.org/10.3390/cancers14194926 (2022).

  39. Guertin, D. A. & Wellen, K. E. Acetyl-CoA metabolism in cancer. Nat. Rev. Cancer. 23(3), 156–172. https://doi.org/10.1038/s41568-022-00543-5 (2023).

    Google Scholar 

  40. Zhang, S., Hulver, M. W., McMillan, R. P., Cline, M. A. & Gilbert, E. R. The pivotal role of pyruvate dehydrogenase kinases in metabolic flexibility. Nutr. Metab. (Lond). 11(1), 10. https://doi.org/10.1186/1743-7075-11-10 (2014).

    Google Scholar 

  41. Yang, G. et al. Metabolic heterogeneity in clear cell renal cell carcinoma revealed by single-cell RNA sequencing and spatial transcriptomics. J. Transl Med. 22(1), 210. https://doi.org/10.1186/s12967-024-04848-x (2024).

    Google Scholar 

  42. Gupta, S., Roy, A. & Dwarakanath, B. S. Metabolic cooperation and competition in the tumor microenvironment: implications for therapy. Front. Oncol. 7, 68. https://doi.org/10.3389/fonc.2017.00068 (2017).

    Google Scholar 

  43. Mishra, D. & Banerjee, D. Lactate dehydrogenases as metabolic links between tumor and stroma in the tumor microenvironment. Cancers. 11(6). https://doi.org/10.3390/cancers11060750 (2019).

  44. Strickler, J. H., Hanks, B. A. & Khasraw, M. Tumor mutational burden as a predictor of immunotherapy response: is more always better? Clin. Cancer Res. 27(5), 1236–1241. https://doi.org/10.1158/1078-0432.Ccr-20-3054 (2021).

    Google Scholar 

  45. Xiong, X., Liu, W. & Yao, C. Development of an alkaliptosis-related LncRNA risk model and immunotherapy target analysis in lung adenocarcinoma. Front. Genet. 16, 1573480. https://doi.org/10.3389/fgene.2025.1573480 (2025).

    Google Scholar 

  46. Cao, D., Xu, H., Xu, X., Guo, T. & Ge, W. High tumor mutation burden predicts better efficacy of immunotherapy: a pooled analysis of 103078 cancer patients. Oncoimmunology 8(9), e1629258. https://doi.org/10.1080/2162402x.2019.1629258 (2019).

    Google Scholar 

  47. Jardim, D. L., Goodman, A., de Melo Gagliato, D. & Kurzrock, R. The challenges of tumor mutational burden as an immunotherapy biomarker. Cancer Cell. 39(2), 154–173. https://doi.org/10.1016/j.ccell.2020.10.001 (2021).

    Google Scholar 

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Acknowledgements

We thank the funding support. And thank all of the authors participate in the study and the time that they devoted to the study.

Funding

This work was supported by grants from the Natural Science Foundation of Hubei Province (2025AFD054), Shiyan Municipal Science and Technology Bureau Project (25Y132), and Innovative Research Program for Graduates of Basic Medical College, Hubei University of Medicine (NO. JC2024007).

Author information

Author notes

  1. Xiaojuan Tang and Ling Liu contributed equally to this work.

Authors and Affiliations

  1. Department of Radiology, Renmin Hospital, Hubei University of Medicine, Shiyan, 442000, Hubei, P.R. China

    Xiaojuan Tang & Sheng Li

  2. Department of Andrology, Renmin Hospital, Hubei University of Medicine, Shiyan, 442000, Hubei, P.R. China

    Yan Tan

  3. Department of Anesthesiology, Renmin Hospital, Hubei University of Medicine, Shiyan, 442000, Hubei, P.R. China

    Lei Xi

  4. Hubei Key Laboratory of Embryonic Stem Cell Research, School of Basic Medical Sciences, Hubei University of Medicine, No. 30 Renmin South Road, Shiyan, 442000, Hubei, China

    Ling Liu & Min Gao

  5. Key Laboratory of Zebrafish Modeling and Drug Screening for Human Diseases of Xiangyang City, Department of Obstetrics and Gynaecology, Xiangyang No. 1 People’s Hospital, Hubei University of Medicine, Xiangyang, 441000, Hubei, China

    Peng Duan

  6. Department of Radiology, Hubei Cancer Hospital, Tongji Medical College, Huazhong University of Science and Technology, No 116 Zhuodaoquan South Load, Hongshan District, Wuhan, 430000, Hubei, China

    Zilong Yuan

  7. Biomedical Engineering College, Hubei University of Medicine, No. 30 Renmin South Road, Shiyan, 442000, Hubei, China

    Qiang Xia & Yan Tan

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Contributions

Conceptualization: Yan Tan, Lei Xi and Peng Duan; Data curation: Xiaojuan Tang and Ling Liu; Methodology: Xiaojuan Tang, Ling Liu and Min Gao; Funding acquisition: Peng Duan and Lei Xi; Formal analysis and investigation: Xiaojuan Tang, Lei Xi and Sheng Li; Project administration: Yan Tan and Peng Duan; Writing original draft: Xiaojuan Tang and Ling Liu; Writing – review & editing: Min Gao, Lei Xi, Sheng Li, Zilong Yuan and Qiang Xia; All authors read and approved the final manuscript.

Corresponding authors

Correspondence to Lei Xi or Yan Tan.

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All authors contributed to the article and approved the submitted version.

Ethics approval

All procedures were performed in compliance with relevant laws and institutional guidelines and have been approved by the Professional Committee of Scientific Research and Academic Ethics of Shiyan People’s Hospital, consent was obtained for experimentation with human subjects. The date and reference number of the ethical approval(s) obtained: May 19, 2025; SYRMYY-2025-059.

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The authors declare no competing interests.

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Tang, X., Liu, L., Gao, M. et al. Single-cell transcriptomics identifies fibroblast associated immune heterogeneity and prognostic signatures in bladder cancer. Sci Rep (2026). https://doi.org/10.1038/s41598-026-38219-x

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  • Received: 30 August 2025

  • Accepted: 29 January 2026

  • Published: 03 February 2026

  • DOI: https://doi.org/10.1038/s41598-026-38219-x

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Keywords

  • Bladder cancer
  • Single-cell RNA sequencing
  • Fibroblast marker
  • Immune microenvironment
  • Prognostic biomarkers
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