Integrative multi-omics and machine learning identify a robust signature for discriminating prognosis and therapeutic targets in bladder cancer.

ABSTRACT: Background: Bladder cancer (BLCA) is a common malignant tumor whose pathogenesis has not yet been fully elucidated. This study analyzed prognostic genes in BLCA by integrating transcriptomics and proteomics data, and established prognostic models, aiming to offer novel insights for BLCA therapy. Methods: Transcriptomic, proteomic, and protein acetylation sequencing were conducted on six BLCA tumor tissues and six paraneoplastic tissue samples. Furthermore, data from TCGA-BLCA, GSE13507, and single-cell RNA sequencing (scRNA-seq) datasets were integrated. Initially, differential expression analysis identified candidate genes regulated by acetylation. These genes were further refined by intersecting with scRNA-DEG obtained from the scRNA-seq dataset, resulting in the identifica

SUBMITTER: Tan Z 

PROVIDER: S-EPMC11843231 | biostudies-literature | 2025

REPOSITORIES: biostudies-literature