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

ABSTRACT:

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. 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 perfo

SUBMITTER: Liu Z 

PROVIDER: S-EPMC13473039 | biostudies-literature | 2026 Jun

REPOSITORIES: biostudies-literature