Multi-omics and machine learning integration of diverse cell death pathways optimize risk stratification and inform drug therapy in Wilms tumor.

introductionDespite 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.

methodsKey tumor-associated genes were identified through limma differential analysis and WGCNA, and subsequently integrated with 12 distinct PCD patterns. TARGET-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 GSE200256.

resultsKey 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 worse 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.

conclusionThese 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.