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<p>Bladder cancer is the fourth most common malignant tumor in men, with limited therapeutic biomarkers and heterogeneous responses to immunotherapy. Disulfide bond-driven cell death has emerged as a critical regulator of tumor progression and immune microenvironment remodeling. By integrating data from TCGA and GEO cohorts, we developed a Disulfide-Related Prognostic Signature (DRPS) using ten machine learning algorithms. Single-cell RNA sequencing (scRNA-seq) elucidated the cell subtype-specific expression patterns of disulfide bond regulatory genes, while immune microenvironment and drug sensitivity analyses validated its clinical translational potential. qRT-PCR experiments confirmed differential expression patterns of core genes in bladder cancer cell lines. The DRPS model, optimized by the StepCox[backward] algorithm, demonstrated robust prognostic accuracy across four validation cohorts (mean C-index = 0.658). High-risk patients exhibited an enhanced immunosuppressive microenvironment characterized by infiltrated activated cancer-associated fibroblasts, upregulated APC co-inhibition pathways, and elevated immune checkpoint expression. Notably, high DRPS scores were associated with primary resistance to immunotherapy but showed increased sensitivity to anti-tumor agents such as Elephantine and Leflunomide. This study establishes a novel DRPS that serves as a predictive indicator for bladder cancer prognosis and pan-cancer immunotherapy efficacy.</p>

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Disulfide bond-related gene signature development for bladder cancer prognosis prediction and immune microenvironment characterization

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2025年5月29日

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  • Hua Huang,
  • Haiyan Shao,
  • Yifan Wang,
  • Lili Ge

摘要

Bladder cancer is the fourth most common malignant tumor in men, with limited therapeutic biomarkers and heterogeneous responses to immunotherapy. Disulfide bond-driven cell death has emerged as a critical regulator of tumor progression and immune microenvironment remodeling. By integrating data from TCGA and GEO cohorts, we developed a Disulfide-Related Prognostic Signature (DRPS) using ten machine learning algorithms. Single-cell RNA sequencing (scRNA-seq) elucidated the cell subtype-specific expression patterns of disulfide bond regulatory genes, while immune microenvironment and drug sensitivity analyses validated its clinical translational potential. qRT-PCR experiments confirmed differential expression patterns of core genes in bladder cancer cell lines. The DRPS model, optimized by the StepCox[backward] algorithm, demonstrated robust prognostic accuracy across four validation cohorts (mean C-index = 0.658). High-risk patients exhibited an enhanced immunosuppressive microenvironment characterized by infiltrated activated cancer-associated fibroblasts, upregulated APC co-inhibition pathways, and elevated immune checkpoint expression. Notably, high DRPS scores were associated with primary resistance to immunotherapy but showed increased sensitivity to anti-tumor agents such as Elephantine and Leflunomide. This study establishes a novel DRPS that serves as a predictive indicator for bladder cancer prognosis and pan-cancer immunotherapy efficacy.