Identification of oxidative stress-related diagnostic marker genes and immune landscape in interstitial cystitis by bioinformatics and machine learning

Background Interstitial cystitis (IC) is a chronic inflammatory disease with autoimmune associations that is challenging to diagnose and treat. Recent findings indicate that oxidative stress (OS) is a crucial pathophysiological mechanism in IC. Moreover, the interactions between OS, inflammation, and immune cell infiltration are highly complex. Therefore, this study aims to identify biomarkers linked to OS in the development of IC and to elucidate their relationship with immune cell infiltration. These findings could provide new research directions for the diagnosis and treatment of IC. Methods The GSE711783 dataset from the GEO database was utilized to identify differentially expressed genes in IC, while OS-related genes were obtained from the GeneCards database. Hub genes associated with OS were identified through integrated analysis using WGCNA and protein-protein interaction networks. Gene regulatory networks involving transcription factors, TF-miRNA interactions and gene-disease associations were analyzed using relevant databases. Diagnostic marker genes associated with OS were refined using machine learning algorithms. Subsequently, a nomogram diagnostic prediction model was developed and validated through in vitro experiments. Potential drug candidates were identified using the DSigDB database, and the immune landscape in IC was explored using the CIBERSORT algorithm. Results We identified a total of 68 differentially expressed genes related to OS, alongside 15 hub genes. Among these, four genes—BMP2, MMP9, CCK and NOS3—were further selected as diagnostic markers. Using the ANN model, ROC curve analysis, and nomogram diagnostic prediction model, all four genes demonstrated excellent diagnostic efficacy. Additionally, these genes exhibited strong associations with T cells CD4 memory resting, T cells CD4 memory activated, and Eosinophils. Finally, decitabine emerged as the most promising drug molecule for IC treatment. Conclusion We identified four diagnostic marker genes related to OS that are pivotal in the pathogenesis of IC, influencing both OS and immune responses. These findings highlight new avenues for research in the diagnosis and treatment of IC.