Identification of novel biomarkers in Hunner’s interstitial cystitis using the CIBERSORT, an algorithm based on machine learning

Abstract

Abstract Background  Hunner’s interstitial cystitis (HIC) is a complex disorder characterized by pelvic pain, disrupted urine storage, and Hunner lesions seen on cystoscopy. There are few effective diagnostic biomarkers. In the present study, we used the novel machine learning tool CIBERSORT to measure immune cell subset infiltration and potential novel diagnostic biomarkers for HIC. Methods The GSE11783 and GSE57560 datasets were downloaded from the Gene Expression Omnibus for analysis. Ten HIC and six healthy samples from GSE11783 were analyzed using the CIBERSORT algorithm. Gene Set Enrichment Analysis (GSEA) was performed to identify biological processes that occur during HIC pathogenesis. Finally, expression levels of 11 T cell follicular helper cell (Tfh) markers were compared between three healthy individuals and four patients from GSE57560. Results Six types of immune cells in HIC from GSE11783 showed significant differences, including resting mast cells, CD4+ memory-activated T cells (CD3+ CD4+ HLA-DR+ cells), M0 and M2 macrophages, Tfh cells, and activated natural killer cells. Except for plasma cells, there were no significant differences between Hunner’s lesion and non-Hunner’s lesion areas in HIC. The GSEA revealed significantly altered biological processes, including antigen–antibody reactions, autoimmune diseases, and infections of viruses, bacteria, and parasites. There were 11 Tfh cell markers with elevated expression in patients from GSE57560. Conclusion This was the first demonstration of Tfh cells and CD3+ CD4+ HLA-DR+ cells with elevated expression in HIC. These cells might serve as novel diagnostic biomarkers.