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A gene signature of cancer-associated fibroblasts predicts prognosis and treatment response in bladder cancer

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Abstract

Objective

Due to the pivotal role cancer-associated fibroblasts (CAFs) play in tumor progression, our study aimed to develop a signature of CAFs-related gene (CRG) to predict the survival outcomes and treatment response of bladder cancer (BLCA).

Methods

The transcriptome data and relevant clinical information about BLCA were collected from publicly available databases, including The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) datasets. Weighted gene co-expression network analysis was utilized to uncover CAFs-associated hub genes, and subsequently, a risk model for survival prognosis was constructed using LASSO-Cox regression. The immune microenvironment, immune infiltration, immunotherapy response, and drug sensitivity were explored using ESTIMATE, CIBERSORT, TIDE, and oncoPredict algorithms. To verify the expression of the CRGs, additional analyses were performed using online databases (HPA, CCLE, TIMER, cBioPortal, and TISCH).

Results

Our study developed a CRG signature and constructed a prognostic model. Significant differences in overall survival were observed between the two risk stratifications. The risk score increased with the infiltration of CAFs and tumor staging progression, while closely correlating with immune checkpoint expression and infiltration of CD8 T cells, follicular helper T cells, regulatory T cells, activated dendritic cells, M0 macrophages, M2 macrophages, and resting mast cells. Furthermore, a higher proportion of patients in the low-risk stratification exhibited responsiveness to immunotherapy, and significant variances in sensitivity to multiple chemotherapy medications were observed between the two risk stratifications.

Conclusion

The construction of the risk model based on the CRG signature offers new avenues for the prognosis evaluation and development of personalized treatment strategies for BLCA.

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Data availability

The data to support the finding of this work is available at public online databases (https://portal.gdc.cancer.gov/, https://www.ncbi.nlm.nih.gov/geo/, https://portals.broadinstitute.org/ccle/, https://www.proteinatlas.org/, https://cistrome.shinyapps.io/timer/, https://www.cbioportal.org/, and http://tisch.comp-genomics.org/).

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Funding

This work was supported by the Guangxi Key Research and Development Project (Grant No. Guike AB21196022) and the Major Project of Guangxi Innovation Driven (AA18118016).

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Author notes

  1. Xi Chen and Chunyan Liao have contributed equally to this work and share first authorship.

Authors and Affiliations

  1. Department of Urology, The First Affiliated Hospital of Guangxi Medical University, Nanning, 530021, China

    Xi Chen, Xiong Zou & Zengnan Mo

  2. Center for Genomic and Personalized Medicine, Guangxi Key Laboratory for Genomic and Personalized Medicine, Guangxi Collaborative Innovation Center for Genomic and Personalized Medicine, Guangxi Medical University, Nanning, China

    Xi Chen, Xiong Zou & Zengnan Mo

  3. Department of Urology, The Affiliated Hospital of Guizhou Medical University, Guiyang, China

    Xi Chen

  4. Department of Ultrasound Medicine, The Affiliated Hospital of Guizhou Medical University, Guiyang, 550000, China

    Chunyan Liao & Bei Zhang

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Contributions

ZM and BZ: conceived and led the study. XC and CL: responsible for data collection, interpretation, and analysis, and wrote the manuscript. XZ: critically revised the manuscript. All authors contributed to the article and approved the submitted version.

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Correspondence to Bei Zhang or Zengnan Mo.

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Chen, X., Liao, C., Zou, X. et al. A gene signature of cancer-associated fibroblasts predicts prognosis and treatment response in bladder cancer. Clin Transl Oncol 26, 477–495 (2024). https://doi.org/10.1007/s12094-023-03270-x

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