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

• Xi Chen ^[1] ; Chunyan Liao ^[2] ; Xiong Zou ^[1] ; Bei Zhang ^[2] ; Zengnan Mo ^[1]
  1. [1] Department of Urology, The First Afliated Hospital of Guangxi Medical University, Nanning, China
  2. [2] Department of Ultrasound Medicine, The Afliated Hospital of Guizhou Medical University, Guiyang, China
• Localización: Clinical & translational oncology, ISSN 1699-048X, Vol. 26, Nº. 2, 2024, págs. 477-495
• Idioma: inglés
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• Resumen

  • 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.