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Background <p>Bladder cancer is a prevalent malignant tumor in the urinary system, ranking tenth among malignant tumors globally. Currently, the primary treatment methods include surgery, chemotherapy, immunotherapy, and emerging neoadjuvant therapies. These treatments have effectively reduced the mortality rate associated with bladder tumors. However, the prognosis for bladder cancer patients remains poor due to the high rates of recurrence, metastasis, and drug resistance. Therefore, identifying the pathological features and tumor immune environment of each patient to develop optimal treatment strategies is an urgent health issue.</p> Methods <p>We analyzed a single-cell dataset of bladder cancer to identify key signaling pathways and chromosomal mutations among different subgroups of bladder tumors. Additionally, we sought to identify ligand-receptor pairs between malignant bladder tumor epithelial cells and other cell subgroups. Using the TCGA-BLCA database and COX regression analysis, we screened key ligand-receptor pairs closely related to patient prognosis. We then used LASSO regression analysis to construct a bladder cancer risk prediction model based on these key ligand-receptor pairs (LR.Riskscore). To evaluate the impact of different LR.Riskscores on prognosis and clinical benefits for bladder cancer patients, we used Kaplan-Meier survival curves, risk forest plots, nomograms, and decision curve analysis. We also conducted cellular experiments, including clone formation, CCK8 assays, Transwell migration assays, and scratch assays, to elucidate the important cellular role of the key receptor CXCR4 in bladder tumor cells. Finally, we utilized the IMvigor210 bladder cancer immunotherapy dataset to reveal the correlation between the LR.Riskscore risk equation and bladder cancer immunotherapy.</p> Results <p>We constructed a bladder cancer risk prediction model based on key ligand-receptor pairs, including CXCL12_CXCR4, VEGFA_FLT1, PDGFRA_PDGFC, WNT2_SFRP4, and HLA-E_KLRC1. This model effectively predicted the prognosis and immunotherapy responsiveness of bladder cancer patients, significantly enhancing clinical benefits compared to conventional clinical features. Additionally, our cellular experiments confirmed the oncogenic role of the key receptor CXCR4 in bladder cancer.</p> Conclusion <p>Our risk prediction model significantly improves the evaluation of prognosis and the prediction of immunotherapy responsiveness in bladder cancer patients. This advancement promotes the development of precise, individualized treatment strategies for bladder cancer.</p>

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Prognostic analysis and immunotherapy prediction based on key receptor-ligand pairs of bladder cancer

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2025年9月30日

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  • Yuhang Wang,
  • Tailai Zhou,
  • Chen Hu,
  • Hengxin Chen,
  • Minfeng Chen

摘要

Background

Bladder cancer is a prevalent malignant tumor in the urinary system, ranking tenth among malignant tumors globally. Currently, the primary treatment methods include surgery, chemotherapy, immunotherapy, and emerging neoadjuvant therapies. These treatments have effectively reduced the mortality rate associated with bladder tumors. However, the prognosis for bladder cancer patients remains poor due to the high rates of recurrence, metastasis, and drug resistance. Therefore, identifying the pathological features and tumor immune environment of each patient to develop optimal treatment strategies is an urgent health issue.

Methods

We analyzed a single-cell dataset of bladder cancer to identify key signaling pathways and chromosomal mutations among different subgroups of bladder tumors. Additionally, we sought to identify ligand-receptor pairs between malignant bladder tumor epithelial cells and other cell subgroups. Using the TCGA-BLCA database and COX regression analysis, we screened key ligand-receptor pairs closely related to patient prognosis. We then used LASSO regression analysis to construct a bladder cancer risk prediction model based on these key ligand-receptor pairs (LR.Riskscore). To evaluate the impact of different LR.Riskscores on prognosis and clinical benefits for bladder cancer patients, we used Kaplan-Meier survival curves, risk forest plots, nomograms, and decision curve analysis. We also conducted cellular experiments, including clone formation, CCK8 assays, Transwell migration assays, and scratch assays, to elucidate the important cellular role of the key receptor CXCR4 in bladder tumor cells. Finally, we utilized the IMvigor210 bladder cancer immunotherapy dataset to reveal the correlation between the LR.Riskscore risk equation and bladder cancer immunotherapy.

Results

We constructed a bladder cancer risk prediction model based on key ligand-receptor pairs, including CXCL12_CXCR4, VEGFA_FLT1, PDGFRA_PDGFC, WNT2_SFRP4, and HLA-E_KLRC1. This model effectively predicted the prognosis and immunotherapy responsiveness of bladder cancer patients, significantly enhancing clinical benefits compared to conventional clinical features. Additionally, our cellular experiments confirmed the oncogenic role of the key receptor CXCR4 in bladder cancer.

Conclusion

Our risk prediction model significantly improves the evaluation of prognosis and the prediction of immunotherapy responsiveness in bladder cancer patients. This advancement promotes the development of precise, individualized treatment strategies for bladder cancer.