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. 2025 Jun 6:18:7263-7286.
doi: 10.2147/JIR.S524653. eCollection 2025.

Identification and Validation of Oxidative Stress-Related Diagnostic Marker Genes and Immune Landscape in Ulcerative Interstitial Cystitis by Integrating Bioinformatics and Machine Learning

Chaowei Fu #  1 , Yuwei Zhang #  2 , Yu Zhao #  1 , Shiyu Wang #  1 , Yuhua Zhou  1 , Jing Lv  1 , Shengkai Jin  1 , Fengping Liu  1 , Ninghan Feng  1   2   3
Affiliations

Identification and Validation of Oxidative Stress-Related Diagnostic Marker Genes and Immune Landscape in Ulcerative Interstitial Cystitis by Integrating Bioinformatics and Machine Learning

Chaowei Fu et al. J Inflamm Res. .

Abstract

Purpose: Interstitial cystitis (IC) is a chronic inflammatory disease with autoimmune associations, particularly in ulcerative IC, a severe and refractory subtype. Oxidative stress plays a crucial role in IC pathogenesis, interacting with inflammation and immune cell infiltration. This study aimed to identify oxidative stress-linked biomarkers and explore their relationship with immune cell infiltration to enhance diagnosis and treatment strategies.

Patients and methods: The GSE711783 dataset from GEO was analyzed to identify differentially expressed genes in ulcerative IC. Oxidative stress-related genes were sourced from GeneCards, with hub genes identified via WGCNA and protein-protein interaction networks. Diagnostic markers were refined using machine learning, and a nomogram prediction model was developed. Diagnostic biomarkers were validated in vitro and in vivo, immune infiltration was assessed with CIBERSORT, and potential therapeutic drugs were identified through DSigDB.

Results: Four diagnostic biomarkers-BMP2, MMP9, CCK, and NOS3-were identified and found to be associated with immune cells, including CD4+ T cells and eosinophils. Decitabine was identified as a potential therapeutic candidate. Experimental validation confirmed the expression of these biomarkers.

Conclusion: This study identifies BMP2, MMP9, CCK, and NOS3 as key biomarkers, offering valuable insights into the diagnosis and treatment of IC.

Keywords: bioinformatics; diagnostic marker; immune landscape; interstitial cystitis; machine learning; oxidative stress.

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Conflict of interest statement

The author(s) report no conflicts of interest in this work. This paper has been uploaded to Research Square as a preprint: https://www.researchsquare.com/article/rs-4642942/v1.pdf.

Figures

Figure 1
Figure 1
Flow diagram of this research.
Figure 2
Figure 2
DEGs analysis and WGCNA analysis. (A) The volcano plot of genes; (B) The gene heatmap of DEGs; (C) Cluster tree of 16 samples; (D) Choosing the best soft-threshold power, the red line indicates the minimum soft threshold of 6 for constructing the scale-free network; (E) Gene dendrogram for all DEGs; (F) Heatmap illustrating the relationships between modules and traits; (G) The Venn diagram of DEGs and WGCNA genes; (H) The Venn diagram of OS-related genes and key DEGs.
Figure 3
Figure 3
Enrichment analysis of key DEGs. (A) The GO enrichment analysis plot; (B) The KEGG enrichment analysis plot.
Figure 4
Figure 4
Network of regulatory interactions and associations. (A), (B) The network of gene-TF and gene-miRNA regulatory interactions. (C) Expression of TFs in GSE11783 datasets. (D) The gene-disease association network. ****, **, * represent P<0.0001, P<0.01, P<0.05.
Figure 5
Figure 5
Screening diagnostic marker genes using machine learning. (A) LASSO logistic regression analysis. (B) RF analysis. (C) SVM-RFE analysis. (D) Venn diagrams for three analysis method results.
Figure 6
Figure 6
Nomogram model construction for IC diagnosis. (A) Expression of diagnostic marker genes in the GSE11783 dataset. (B) ANN model plot. Positive weights are connected by red lines, negative weights are connected by gray lines, and the thickness of the lines reflects the size of the weights. (C) ROC curves of the four genes and Nomogram model. (D) Nomogram to predict IC risk. (E) DCA curve to assess the practical efficacy of the nomogram. (F) Calibration curve evaluation for the diagnostic potential of the nomogram model. ****, ***, **, represent P<0.0001, P<0.001, P<0.01.
Figure 7
Figure 7
Infiltration pattern of immune cell subtypes. (A) A stacked plot of the expression of 22 types of immune cells in each sample; (B) The expression of 22 types of immune cells. (C), (D) The correlation analysis plot between 4 genes and 22 types of immune cells; ****, ***, **, * represent P<0.0001, P<0.001, P<0.01, P<0.05. The red numerical text indicates a p-value less than 0.05.
Figure 8
Figure 8
Validation of hub genes expression in cell experiments. (A) The mRNA expression levels of three genes in the SV-HUC-1 cell line. (B) The mRNA expression levels of three genes in the T24 cell line. (C) The mRNA and protein expression levels of three genes in the SV-HUC-1 cell line and T24 cell line. ****, ***, **, * represent P<0.0001, P<0.001, P<0.01, P<0.05. Experiments were performed with 3 independent trials and 3 repetitions per trial.
Figure 9
Figure 9
Validation of hub genes expression in v animal experiments. (A) The mRNA expression levels of four genes in the IC rat model. (B) The protein expression levels of four genes in the IC rat model. ****, ***, **, * represent P<0.0001, P<0.001, P<0.01, P<0.05.
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References

    1. van de Merwe JP, Nordling J, Bouchelouche P, et al. Diagnostic criteria, classification, and nomenclature for painful bladder syndrome/interstitial cystitis: an ESSIC proposal. Europ Urol. 2008;53:60–67. doi: 10.1016/j.eururo.2007.09.019 - DOI - PubMed
    1. Tripp DA, Dueck J, Holden RR, et al. Biopsychosocial predictors of suicide risk in patients with interstitial cystitis/bladder pain syndrome. Can Urol Assoc J. 2021;15(12):E630–E636. doi: 10.5489/cuaj.7337 - DOI - PMC - PubMed
    1. Humphrey L, Arbuckle R, Moldwin R, et al. The bladder pain/interstitial cystitis symptom score: development, validation, and identification of a cut score. Europ Urol. 2012;61(2):271–279. doi: 10.1016/j.eururo.2011.10.004 - DOI - PubMed
    1. Dasgupta J, Tincello DG. Interstitial cystitis/bladder pain syndrome: an update. Maturitas. 2009;64:212–217. doi: 10.1016/j.maturitas.2009.09.016 - DOI - PubMed
    1. Nasrin S, Masuda E, Kugaya H, et al. Improvement by phytotherapeutic agent of detrusor overactivity, down-regulation of pharmacological receptors and urinary cytokines in rats with cyclophosphamide induced cystitis. J Urol. 2013;189:1123–1129. doi: 10.1016/j.juro.2012.09.054 - DOI - PubMed

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