Integrated identification of immune-related therapeutic targets for interstitial cystitis via multi-algorithm machine learning: transcriptomic profiling and <i>in vivo</i> experimental validation.

ABSTRACT:

Background

Interstitial cystitis/bladder pain syndrome (IC/BPS) is a complex urological disorder characterized by chronic pelvic pain and urinary dysfunction, with limited diagnostic biomarkers and therapeutic options. Emerging evidence implicates immune microenvironment dysregulation in its pathogenesis, yet the identification of key driver genes and cross-omics integration remains underexplored.

Methods

This study integrated three transcriptomic datasets to identify immune-related gene modules via weighted gene co-expression network analysis (WGCNA). A diagnostic model was constructed using 113 machine learning algorithms. Immune cell infiltration was assessed via CIBERSORT, and single cell sequencing elucidated cellular heterogeneity. Drug candidates were predicted using

SUBMITTER: Wang Y 

PROVIDER: S-EPMC12328188 | biostudies-literature | 2025

REPOSITORIES: biostudies-literature