ArticlePloS one2025
Characteristic genes and immune landscape of interstitial cystitis.
Article in PloS one, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.
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Who cites it
4 citing papers in PubMed.
- Repeated intravesical platelet-rich plasma injections alleviate symptoms via T-cell modulation and mitochondrial dysfunction in non-ulcer interstitial cystitis/bladder pain syndrome.Scientific reports · 2026Article
- Re: "Regulation of Autophagy by the Cdk1/p53/p21 Feedback Loop in an Interstitial Cystitis/Bladder Pain Syndrome Cell Model: Implications for Inflammatory Response".International urogynecology journal · 2026Article
- Enhanced CHI3L1 promotes macrophage activation in persistent inflammatory events of ulcerative interstitial cystitis.Frontiers in immunology · 2026Article
- Shaping the future of abdominal and pelvic pain research with novel scientific and technological advances.Frontiers in pain research (Lausanne, Switzerland) · 2026Article
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9 authors.
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Abstract
backgroundInterstitial cystitis (IC) was still a disease with the exclusive diagnosis and lacked an effective gold standard. It was of great significance to find diagnostic markers for IC. Our study was aimed to screen characteristic genes via machine learning algorithms, characterize the immune landscape of IC, and show correlations between characteristic genes and immune cell subtypes.
methodsRNA sequencing data sets on IC were downloaded from Gene Expression Omnibus (GEO) database, including GSE57560, GSE11783 and GSE621, whose corresponding platforms were GPL16699, GPL570 and GPL262 respectively. Three machine learning algorithms were applied for identification of characteristic gene for IC. Single sample Gene Set Enrichment Analysis (ssGSEA) was applied to figure out the immune cell infiltration (ICI) of IC and normal tissue samples. Correlation analysis was performed via Spearman test. Receiver operator characteristic curve (ROC) was used to evaluate diagnostic efficacy of key genes.
resultsCCL18, MMP10 and WIF1 were identified as characteristic gene via machine learning algorithms. MMP10 and CCL18 were with higher expression in IC tissues compared with normal bladder tissues, while WIF1 had lower expressionin IC tissues (P < 0.05). These three genes had good diagnostic efficacy for IC. Compared with normal bladder tissues, 18 immune cell subtypes were up-regulated in interstitial cystitis tissues (P < 0.05). MMP10 and CCL18 were positively correlated to immune scores in IC, while WIF1 was negatively correlated to immune scores (P > 0.05).
conclusionWe screened the feature genes, CCL18, MMP10 and WIF1, among the differentially expressed genes (DEGs) by three different machine learning algorithms. They showed good diagnostic performance in both training and testing cohorts and were potential diagnostic markers for IC. We paint the immune landscape of IC. In IC tissue, immune cell subtypes infiltrated extensively. Most immune cell subtypes were up-regulated in IC tissue, including mast cells, activated CD4 T cells, and regulatory T cells that suppress immune responses. MMP10 and CCL18 had positive correlation to ICI, while WIF was negatively correlated with ICI. MMP10 and CCL18 may be the driving factors of immune response or their expression levels may be increased by immune response. The effect of characteristic genes of IC on immune cell subtypes still needed to be further explored.
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