Machine learning model in multi-omics perspective demystifies the prognostic significance of crotonylation heterogeneity in clear cell renal cell carcinoma.

ABSTRACT:

Background

Crotonylation, a post-translational modification, is implicated in cancer progression, but its prognostic significance in clear cell renal cell carcinoma (ccRCC) remains unclear. This study aimed to demystify crotonylation heterogeneity and establish a robust prognostic model for ccRCC.

Methods

Using multi-omics approaches, we analyzed transcriptomic data from TCGA-KIRC and GEO cohorts (GSE40435, GSE167573, GSE29609). Crotonylation scores were calculated via ssGSEA, with related gene modules identified through WGCNA. We integrated 10 machine learning algorithms to develop a prognostic model. Immune microenvironment was profiled using Cibersort, mutation landscapes via maftools, and drug sensitivity through oncoPredict. Spatial transcriptomics and single-cell data

SUBMITTER: Dai H 

PROVIDER: S-EPMC12434922 | biostudies-literature | 2025 Sep

REPOSITORIES: biostudies-literature