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

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.