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<p>In recent years, research on the relationship between coagulation system abnormalities and tumor immunity has been widely reported. Bladder cancer (BC), as an immunogenic tumor, holds great promise in immunotherapy. The role of coagulation-related genes in predicting the prognosis of BC patients and the efficacy of immunotherapy remains to be explored. In this study, we utilized the TCGA data system to analyze the biological functions of coagulation-related genes (CRGs) in BC patients. Additionally, we employed multiple machine learning methods (LASSO regression, SVM-RFE regression, and random forest) to establish diagnostic and prognostic signatures for BC based on CRGs. Univariate and multivariate Cox analyses indicated that the prognostic signature is an independent risk factor. Combining risk scores and independent clinical parameters, we established a Nomogram with a C-index of 0.812. Furthermore, we applied CRGs to cluster BC patients, and significant differences were observed between the two clusters in terms of immune cell infiltration, immune cell pathway activity, and response to immunotherapy. In conclusion, we systematically analyzed the value of CRGs in the diagnosis, prognosis, and personalized treatment of BC patients.</p>

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Comprehensive analysis of coagulation-associated gene signature in bladder cancer diagnosis, prognosis, and immunotherapy

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2025年10月29日

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  • Guicao Yin,
  • Shengqi Zheng,
  • Jialong Wang,
  • Tianchi Hua,
  • Wei Zhang,
  • Xiang Pan,
  • Wei Sha,
  • Yifan Li

摘要

In recent years, research on the relationship between coagulation system abnormalities and tumor immunity has been widely reported. Bladder cancer (BC), as an immunogenic tumor, holds great promise in immunotherapy. The role of coagulation-related genes in predicting the prognosis of BC patients and the efficacy of immunotherapy remains to be explored. In this study, we utilized the TCGA data system to analyze the biological functions of coagulation-related genes (CRGs) in BC patients. Additionally, we employed multiple machine learning methods (LASSO regression, SVM-RFE regression, and random forest) to establish diagnostic and prognostic signatures for BC based on CRGs. Univariate and multivariate Cox analyses indicated that the prognostic signature is an independent risk factor. Combining risk scores and independent clinical parameters, we established a Nomogram with a C-index of 0.812. Furthermore, we applied CRGs to cluster BC patients, and significant differences were observed between the two clusters in terms of immune cell infiltration, immune cell pathway activity, and response to immunotherapy. In conclusion, we systematically analyzed the value of CRGs in the diagnosis, prognosis, and personalized treatment of BC patients.