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<p>Better prognostic tools are needed to improve the clinical management of clear cell renal cell carcinoma (ccRCC). To address this, we developed a novel prognostic model by integrating pathomics features with pyroptosis-related signaling, a strategy not previously explored in ccRCC. Analysis of The Cancer Genome Atlas (TCGA) whole-slide images identified 59 quantitative image features significantly correlated with a pyroptosis gene set. Based on these features, a prognostic risk score was developed using the StepCox[forward]+Lasso algorithm and validated as an independent predictor of patient survival. This model demonstrated robust predictive performance, with time-dependent AUCs of 0.744, 0.729, and 0.716 for 1-, 3-, and 5-year survival and C-indexes of 0.71 and 0.64 in the training and validation sets. This model implicates key pyroptosis-related genes (e.g., GSDMD, GSDME, CASP5, and several CHMP family genes), whose expression links pathological phenotypes to patient outcomes. Single-cell sequencing revealed their specific expression patterns in the ccRCC microenvironment, and functional exploration highlighted GSDMD’s potential role. By providing a novel, biologically integrated signature, this model offers a refined tool for prognostic assessment that complements conventional clinical parameters. Ultimately, this pyroptosis-based pathomics model could help guide personalized treatment strategies for ccRCC patients in the future.</p>

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A pathomics-based pyroptosis signature predicts survival in clear cell renal cell carcinoma

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2026年2月18日

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  • Kaibin Wang,
  • Shiyao Wei,
  • Dingkun Hou,
  • Lili Wang,
  • Lijuan Kang,
  • Hongzheng Li,
  • Changying Li,
  • Haitao Wang

摘要

Better prognostic tools are needed to improve the clinical management of clear cell renal cell carcinoma (ccRCC). To address this, we developed a novel prognostic model by integrating pathomics features with pyroptosis-related signaling, a strategy not previously explored in ccRCC. Analysis of The Cancer Genome Atlas (TCGA) whole-slide images identified 59 quantitative image features significantly correlated with a pyroptosis gene set. Based on these features, a prognostic risk score was developed using the StepCox[forward]+Lasso algorithm and validated as an independent predictor of patient survival. This model demonstrated robust predictive performance, with time-dependent AUCs of 0.744, 0.729, and 0.716 for 1-, 3-, and 5-year survival and C-indexes of 0.71 and 0.64 in the training and validation sets. This model implicates key pyroptosis-related genes (e.g., GSDMD, GSDME, CASP5, and several CHMP family genes), whose expression links pathological phenotypes to patient outcomes. Single-cell sequencing revealed their specific expression patterns in the ccRCC microenvironment, and functional exploration highlighted GSDMD’s potential role. By providing a novel, biologically integrated signature, this model offers a refined tool for prognostic assessment that complements conventional clinical parameters. Ultimately, this pyroptosis-based pathomics model could help guide personalized treatment strategies for ccRCC patients in the future.