Skip to main content

ORIGINAL RESEARCH article

Front. Immunol.
Sec. Systems Immunology
Volume 16 - 2025 | doi: 10.3389/fimmu.2025.1511529

Identification and validation of immune and diagnostic biomarkers for interstitial cystitis/painful bladder syndrome by integrating bioinformatics and machine-learning

Provisionally accepted
Ci Zou Ci Zou 1Tao Zhou Tao Zhou 1Can Zhu Can Zhu 1Qiongfang Wu Qiongfang Wu 2Mingqiang Deng Mingqiang Deng 2Zhiwei Jiang Zhiwei Jiang 1Longfei Peng Longfei Peng 1Hao Geng Hao Geng 1Wei Zhang Wei Zhang 1Zhouting Tuo Zhouting Tuo 1,3*
  • 1 Second Hospital of Anhui Medical University, Hefei, Anhui Province, China
  • 2 Guangzhou Institutes of Biomedicine and Health, Chinese Academy of Sciences (CAS), Guangzhou, Guangdong Province, China
  • 3 Anhui Medical University, Hefei, China

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

    The etiology of interstitial cystitis/painful bladder syndrome (IC/BPS) remains elusive, presenting significant challenges in both diagnosis and treatment. To address these challenges, we employed a comprehensive approach aimed at identifying diagnostic biomarkers that could facilitate the assessment of immune status in individuals with IC/BPS.Transcriptome data from IC/BPS patients were sourced from the Gene Expression Omnibus (GEO) database. We identified differentially expressed genes (DEGs) crucial for gene set enrichment analysis. Key genes within the module were revealed using weighted gene co-expression network analysis (WGCNA). Hub genes in IC/BPS patients were identified through the application of three distinct machine-learning algorithms. Additionally, the inflammatory status and immune landscape of IC/BPS patients were evaluated using the ssGSEA algorithm. The expression and biological functions of key genes in IC/BPS were further validated through in vitro experiments.A total of 87 DEGs were identified, comprising 43 up-regulated and 44 down-regulated genes. The integration of predictions from the three machine-learning algorithms highlighted three pivotal genes: PLAC8 (AUC: 0.887), S100A8 (AUC: 0.818), and PPBP (AUC: 0.871). Analysis of IC/BPS tissue samples confirmed elevated PLAC8 expression and the presence of immune cell markers in the validation cohorts. Moreover, PLAC8 overexpression was found to promote the proliferation of urothelial cells without affecting their migratory ability by inhibiting the Akt/mTOR/PI3K signaling pathway.Our study identifies potential diagnostic candidate genes and reveals the complex immune landscape associated with IC/BPS. Among them, PLAC8 is a promising diagnostic biomarker that modulates the immune response in patients with IC/BPS, which provides new insights into the future diagnosis of IC/BPS.

    Keywords: IC/BPS, bioinformatics, machine-learning, PLAC8, immune cell landscape

    Received: 07 Nov 2024; Accepted: 03 Jan 2025.

    Copyright: © 2025 Zou, Zhou, Zhu, Wu, Deng, Jiang, Peng, Geng, Zhang and Tuo. 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: Zhouting Tuo, Anhui Medical University, Hefei, China

    Disclaimer: All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article or claim that may be made by its manufacturer is not guaranteed or endorsed by the publisher.