Heliyon (Feb 2024)

The prognostic significance and clinical relevance of stem cell characteristic in bladder cancer

  • Xia Chen,
  • Yuanyuan Yin,
  • Yuming He,
  • Fanqi Meng,
  • Jing Zhao,
  • Fang Liu,
  • Yu Xu,
  • Guoqiang Wang,
  • Xin Zhu,
  • Sijia Ma,
  • Huafei Lu,
  • Shangli Cai,
  • Yanping Song,
  • Junyong Dai

Journal volume & issue
Vol. 10, no. 3
p. e24858

Abstract

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Background: Bladder cancer (BLCA) is a common malignant tumor of urinary system and prognostic biomarkers are needed for better clinical decision-making and patient management. Cancer stem cells (CSCs) are involved in carcinogenesis, development, metastasis and recurrence of BLCA. This study explored the prognostic and predictive value of CSCs-related genes and laid the groundwork for precision treatment development in BLCA. Methods: The mRNA data and corresponding clinical information obtained from TCGA-BLCA cohort was used to discover biomarkers and develop CSCs-related prognostic model, which was further validated in GSE32548 and GSE32894 datasets. In addition, the association between CSCs-related risk score and therapeutic efficacy was analyzed to explore the potential predictive value of the prognostic model. Results: We identified four CSCs-related subtypes and 900 differentially expressed genes (DEGs) among subtypes. Then the CSCs-related prognostic model was built based on 16 CSCs-related DEGs with the most significant prognostic value. Patients in the low-risk group had better overall survival than those in high-risk group (P < 0.001; HR, 0.42; 95 %CI, 0.31–0.57). Multivariable Cox analysis in training and test sets confirmed the independence of CSCs-related risk score as a prognostic factor (P < 0.05). The difference of survival between two risk groups were probably due to the significantly varied immune microenvironment based on the analysis of infiltrated immune cells. Additionally, the risk score was significantly associated with chemotherapy sensitivity and the response to anti-PD-L1 therapy (P < 0.05) which suggested a potential predictive value of CSCs-related risk model. Conclusion: We established a risk classifier based on 16 CSCs-related genes for predicting survival in patients with BLCA. The CSCs-related risk model has both prognostic value and potential predictive value for therapeutic efficacy, which brings us closer to understanding the important role of CSCs in BLCA and may provide guidance for clinical treatment decision-making and patient management. The clinical utility of the CSCs-related risk classifier warrants further studies.

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