Integrated identification of immune-related therapeutic targets for interstitial cystitis via multi-algorithm machine learning: transcriptomic profiling and in vivo experimental validation
BackgroundInterstitial 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 pathogen...
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Frontiers Media S.A.
2025-07-01
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| Series: | Frontiers in Immunology |
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| Online Access: | https://www.frontiersin.org/articles/10.3389/fimmu.2025.1636855/full |
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| author | Yifan Wang Chuanzan Zhou Facai Zhang Yunkai Yang Jia Miao Xuanhan Hu Xinyu Zhang Alin Ji Qi Zhang |
| author_facet | Yifan Wang Chuanzan Zhou Facai Zhang Yunkai Yang Jia Miao Xuanhan Hu Xinyu Zhang Alin Ji Qi Zhang |
| author_sort | Yifan Wang |
| collection | DOAJ |
| description | BackgroundInterstitial 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.MethodsThis 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 DSigdb and validated 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.ResultsEight 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.ConclusionsThis 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. |
| format | Article |
| id | doaj-art-4e09ef47da9b49f18c2ca4cc3329128a |
| institution | Kabale University |
| issn | 1664-3224 |
| language | English |
| publishDate | 2025-07-01 |
| publisher | Frontiers Media S.A. |
| record_format | Article |
| series | Frontiers in Immunology |
| spelling | doaj-art-4e09ef47da9b49f18c2ca4cc3329128a2025-08-20T03:56:00ZengFrontiers Media S.A.Frontiers in Immunology1664-32242025-07-011610.3389/fimmu.2025.16368551636855Integrated identification of immune-related therapeutic targets for interstitial cystitis via multi-algorithm machine learning: transcriptomic profiling and in vivo experimental validationYifan Wang0Chuanzan Zhou1Facai Zhang2Yunkai Yang3Jia Miao4Xuanhan Hu5Xinyu Zhang6Alin Ji7Qi Zhang8Urology and Nephrology Center, Department of Urology, Zhejiang Provincial People’s Hospital, Hangzhou Medical College, Hangzhou, Zhejiang, ChinaUrology and Nephrology Center, Department of Urology, Zhejiang Provincial People’s Hospital, Hangzhou Medical College, Hangzhou, Zhejiang, ChinaUrology and Nephrology Center, Department of Urology, Zhejiang Provincial People’s Hospital, Hangzhou Medical College, Hangzhou, Zhejiang, ChinaUrology and Nephrology Center, Department of Urology, Zhejiang Provincial People’s Hospital, Hangzhou Medical College, Hangzhou, Zhejiang, ChinaThe Second Clinical Medical College, Zhejiang Chinese Medical University, Hangzhou, Zhejiang, ChinaThe Second Clinical Medical College, Zhejiang Chinese Medical University, Hangzhou, Zhejiang, ChinaUrology and Nephrology Center, Department of Urology, Zhejiang Provincial People’s Hospital, Hangzhou Medical College, Hangzhou, Zhejiang, ChinaUrology and Nephrology Center, Department of Urology, Zhejiang Provincial People’s Hospital, Hangzhou Medical College, Hangzhou, Zhejiang, ChinaUrology and Nephrology Center, Department of Urology, Zhejiang Provincial People’s Hospital, Hangzhou Medical College, Hangzhou, Zhejiang, ChinaBackgroundInterstitial 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.MethodsThis 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 DSigdb and validated 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.ResultsEight 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.ConclusionsThis 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.https://www.frontiersin.org/articles/10.3389/fimmu.2025.1636855/fullinterstitial cystitismachine learningsingle-cell analysismolecular dynamics simulationin vivo experiment |
| spellingShingle | Yifan Wang Chuanzan Zhou Facai Zhang Yunkai Yang Jia Miao Xuanhan Hu Xinyu Zhang Alin Ji Qi Zhang Integrated identification of immune-related therapeutic targets for interstitial cystitis via multi-algorithm machine learning: transcriptomic profiling and in vivo experimental validation Frontiers in Immunology interstitial cystitis machine learning single-cell analysis molecular dynamics simulation in vivo experiment |
| title | Integrated identification of immune-related therapeutic targets for interstitial cystitis via multi-algorithm machine learning: transcriptomic profiling and in vivo experimental validation |
| title_full | Integrated identification of immune-related therapeutic targets for interstitial cystitis via multi-algorithm machine learning: transcriptomic profiling and in vivo experimental validation |
| title_fullStr | Integrated identification of immune-related therapeutic targets for interstitial cystitis via multi-algorithm machine learning: transcriptomic profiling and in vivo experimental validation |
| title_full_unstemmed | Integrated identification of immune-related therapeutic targets for interstitial cystitis via multi-algorithm machine learning: transcriptomic profiling and in vivo experimental validation |
| title_short | Integrated identification of immune-related therapeutic targets for interstitial cystitis via multi-algorithm machine learning: transcriptomic profiling and in vivo experimental validation |
| title_sort | integrated identification of immune related therapeutic targets for interstitial cystitis via multi algorithm machine learning transcriptomic profiling and in vivo experimental validation |
| topic | interstitial cystitis machine learning single-cell analysis molecular dynamics simulation in vivo experiment |
| url | https://www.frontiersin.org/articles/10.3389/fimmu.2025.1636855/full |
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