Integration of single-nuclei and spatial transcriptomics to decipher tumor phenotype predictive of relapse-free survival in Wilms tumor.

ABSTRACT:

Background

Wilms tumor (WT) is the most common childhood renal malignancy, with recurrence linked to poor prognosis. Identifying the molecular features of tumor phenotypes that drive recurrence and discovering novel targets are crucial for improving treatment strategies and enhancing patient outcomes.

Methods

Single-nuclei RNA sequencing (snRNA-seq), spatial transcriptomics (ST), bulk RNA-seq, and mutation/copy number data were curated from public databases. The Seurat package was used to process snRNA-seq and ST data. Scissor analysis was applied to identify tumor subpopulations associated with poor relapse-free survival (RFS). Univariate Cox and LASSO analyses were utilized to reduce features. A prognostic ensemble machine learning model was developed. Immunohistochemistry

SUBMITTER: Yang R 

PROVIDER: S-EPMC11911335 | biostudies-literature | 2025

REPOSITORIES: biostudies-literature