To define a set of urinary proteins that correlate with symptom severity in urologic chronic pelvic pain syndrome (UCPPS) and validate a putative molecular test for symptom severity based on quantitatively varying urinary proteins.

Quantitative urinary proteomic analysis of 81 UCPPS subjects was performed. Patients were stratified into “severe” (n = 37) and “non-severe” (n = 44) subpopulations based on summing pain and urinary severity scores which were derived from the GenitoUrinary Pain Index (GUPI) questionnaire.

Utilizing quantitative urinary proteomics, a total of 118 proteins were significantly altered ( P < .05) between the severe and non-severe UCPPS severity subpopulations. We developed a logistic regression model using 4-fold cross-validation to identify a panel of 5 proteins with a ROC AUC of 0.928 to distinguish severe and non-severe UCPPS subpopulations. A parallel analysis of the proteomics data using a decision tree analysis performed similarly. Further validation is needed in future studies.

UCPPS patients with severe pain and urinary symptoms have quantitatively distinct urinary proteins compared to UCPPS patients with less severe symptoms. These protein markers require validation but could potentially be used as an objective symptom severity metric. A subset of these differentially regulated proteins can differentiate severe and non-severe UCPPS patients with high sensitivity and specificity. Informatic analysis of the quantitatively altered urinary proteome implicates putative therapeutic targets.