Work

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

Year: 2026

Type: article

Abstract: 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.

Source: Discover Oncology

DOI: 10.1007/s12672-026-04685-y