Integrating machine learning models with multi-omics analysis to decipher the prognostic significance of mitotic catastrophe heterogeneity in bladder cancer.

ABSTRACT:

Background

Mitotic catastrophe is well-known as a major pathway of endogenous tumor death, but the prognostic significance of its heterogeneity regarding bladder cancer (BLCA) remains unclear.

Methods

Our study focused on digging deeper into the TCGA and GEO databases. Through differential expression analysis as well as Weighted Gene Co-expression Network Analysis (WGCNA), we identified dysregulated mitotic catastrophe-associated genes, followed by univariate cox regression as well as ten machine learning algorithms to construct robust prognostic models. Based on prognostic stratification, we revealed intergroup differences by enrichment analysis, immune infiltration assessment, and genomic variant analysis. Subsequently by multivariate cox regression as well as survshap(t)

SUBMITTER: Dai H 

PROVIDER: S-EPMC12012998 | biostudies-literature | 2025 Apr

REPOSITORIES: biostudies-literature