ORIGINAL RESEARCH article

Front. Immunol.

Sec. Inflammation

Volume 16 - 2025 | doi: 10.3389/fimmu.2025.1636855

This article is part of the Research TopicAdvances in Urobiome and Immunogenomics for Cancer, Infections, Diagnostics, and Personalized TherapeuticsView all 3 articles

Integrated Identification of Immune-Related Therapeutic Targets for Interstitial Cystitis via Multi-Algorithm Machine Learning: Transcriptomic Profiling and In Vivo Experimental Validation

Provisionally accepted
  • 1Zhejiang Provincial People's Hospital, Hangzhou, China
  • 2Zhejiang Chinese Medical University, Hangzhou, China

The final, formatted version of the article will be published soon.

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 immunerelated 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 DSigdb and validated 2 through molecular docking and dynamics simulations. A cyclophosphamide (CYP)/lipopolysaccharide (LPS)-induced IC/BPS murine model was established to evaluate therapeutic efficacy of prioritized compounds (Resiniferatoxin and Acetohexamide) via histopathology, ELISA, and immunohistochemistry. Results: Eight core immune-related genes were identified. The machine learning model achieved AUC >0.9 in both training and validation cohorts. Single-cell analysis revealed IFI27 overexpression in epithelial and immune cells, correlating positively with M1 macrophages and activated CD4+ T cells (p<0.05). Molecular docking demonstrated strong binding affinity between IFI27 and Acetohexamide (-19.91± 0.98 kcal/mol) or Resiniferatoxin (-32.98±1.74 kcal/mol), with dynamics simulations confirming structural stability. In vivo, both compounds significantly reduced bladder inflammation (p<0.05), with Acetohexamide showing superior efficacy in downregulating IFI27 expression and systemic pro-inflammatory cytokines. Conclusions: This multi-omics study deciphered immune dysregulation in IC/BPS and established a robust diagnostic framework. The validation of IFI27-targeting compounds in alleviating inflammation highlights translational potential for repurposed therapeutics. Our findings advance precision immunotherapy strategies for IC/BPS.

Keywords: : Interstitial cystitis, machine learning, Single-Cell Analysis, Molecular Dynamics Simulation, In vivo experiment

Received: 28 May 2025; Accepted: 07 Jul 2025.

Copyright: © 2025 Zhang, Wang, Zhou, Zhang, Yang, Miao, Hu, Zhang and Ji. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

* Correspondence: Qi Zhang, Zhejiang Provincial People's Hospital, Hangzhou, China

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