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 Feng1– 3 

1Wuxi School of Medicine, Jiangnan University, Wuxi, Jiangsu, People’s Republic of China; 2Nantong University Medical School, Nantong University, Nantong, Jiangsu, People’s Republic of China; 3Department of Urology, Jiangnan University Medical Center, Wuxi, Jiangsu, People’s Republic of China

*These authors contributed equally to this work

Correspondence: Ninghan Feng, Jiangnan University Medical Center, 68 Zhongshan Road, Wuxi, 214002, People’s Republic of China, Tel +8613861892528, Email n.feng@jiangnan.edu.cn

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: interstitial cystitis, oxidative stress, diagnostic marker, bioinformatics, machine learning, immune landscape