Multi-Omics and Machine Learning Integration of Diverse Cell Death Pathways Optimize Risk Stratification and Inform Drug Therapy in Wilms Tumor

Introduction: Despite significant improvements in the overall survival of Wilms tumor (WT), a subset of patients still experiences poor outcomes. Programmed cell death (PCD) pathways are pivotal in cancer progression. A deeper understanding of their roles in WT is crucial for harnessing these mechanisms to optimize risk stratification. Methods: Key tumor-associated genes were identified through limma differential analysis and WGCNA, and subsequently integrated with 12 distinct PCD patterns. TAGET-WT transcriptomic data was divided into training and validation sets to construct and validate a risk stratification model. It was subsequently integrated with clinical information to build a comprehensive prediction model. Immune infiltration and drug sensitivity analyses were performed. External validation was performed using scRNA-seq data from the GSE200256 dataset. Results: Key tumor-associated genes were enriched in multiple PCD pathways. The risk stratification model was constructed using 4 genes selected via the machine learning algorithm, stratifying the cohort into high- and low-risk groups. In the overall WT cohort, the high-risk group exhibited poorer prognosis, with 1-, 3-, and 5-year AUC values of 0.820, 0.721, and 0.728, respectively. DCA demonstrated the superior predictive accuracy of the comprehensive prediction model. The high-risk group showed lower infiltration of TH17 cells and increased sensitivity to paclitaxel and sorafenib. Finally, the expression landscape of hub genes was validated in the single-cell dataset. Conclusion: These results highlight a critical role for PCD genes in the progression and immune regulation of WT. Targeting these genes offers a promising avenue for improving clinical management of patients identified as high-risk.