Urine Peptide Patterns for Non-invasive Diagnosis of Endometriosis: a Preliminary Prospective Study

Objective: To detect endometriosis by urine peptide biomarkers using magnetic beads-based matrix-assisted laser desorption/ionization time-of-flight mass spectrometry (MALDI-TOF-MS) and to identify interesting peptides using liquid chromatography tandem mass spectrometry.

Study Design: Prospective case-control study in a university-based gynecological department and central laboratory. A total of 122 patients suffering from dysmenorrhea, pelvic pain and infertility were enrolled in the study. Urine samples were collected before laparoscopy. Urine samples were analyzed by the MALDI-TOF technique to generate peptide profiling and ClinProTools software was used to set up a diagnostic model for endometriosis. Liquid chromatography tandem mass spectrometry (LC-MS/MS) was used to identify interesting peptides.

Results: At laparoscopy 60 patients were diagnosed with endometriosis and 62 patients were disease-free. There were 36 different peptides expressed in endometriosis patients detected by MALDI-TOF compared with controls. We established a genetic algorithm as a diagnostic model with the combination of five peptides (m/z=1433.9, 1599.4, 2085.6, 6798.0 and 3217.2). The model showed a sensitivity of 90.9% and specificity of 92.9%. Urine from another 26 symptomatic patients before laparoscopy were randomly selected and analyzed accordingly. A genetic algorithm showed a sensitivity of 90.9% and specificity of 92.9% in predicting endometriosis before laparoscopy. We also identified two peptides not belonging to the diagnostic model as collagen precursors.

Conclusions: Patients with endometriosis have a unique cluster of peptides in urine. Peptide proteomic profiling provides a novel method for non-invasive diagnosis of endometriosis.