Discovery of non-invasive biomarkers for the diagnosis of endometriosis

• Main Authors: Stella Irungu, Dimitrios Mavrelos, Jenny Worthington, Oleg Blyuss, Ertan Saridogan, John F. Timms
• Format: Article
• Language: English
• Published: BMC 2019-04-01
• Series: Clinical Proteomics
• Subjects: Endometriosis; Biomarkers; Eutopic tissue; Ectopic tissue; Serum
• Online Access: http://link.springer.com/article/10.1186/s12014-019-9235-3

Abstract Background Endometriosis is a common gynaecological disorder affecting 5–10% of women of reproductive age who often experience chronic pelvic pain and infertility. Definitive diagnosis is through laparoscopy, exposing patients to potentially serious complications, and is often delayed. Non-invasive biomarkers are urgently required to accelerate diagnosis and for triaging potential patients for surgery. Methods This retrospective case control biomarker discovery and validation study used quantitative 2D-difference gel electrophoresis and tandem mass tagging–liquid chromatography–tandem mass spectrometry for protein expression profiling of eutopic and ectopic endometrial tissue samples collected from 28 cases of endometriosis and 18 control patients undergoing surgery for investigation of chronic pelvic pain without endometriosis or prophylactic surgery. Samples were further sub-grouped by menstrual cycle phase. Selected differentially expressed candidate markers (LUM, CPM, TNC, TPM2 and PAEP) were verified by ELISA in a set of 87 serum samples collected from the same and additional women. Previously reported biomarkers (CA125, sICAM1, FST, VEGF, MCP1, MIF and IL1R2) were also validated and diagnostic performance of markers and combinations established. Results Cycle phase and endometriosis-associated proteomic changes were identified in eutopic tissue from over 1400 identified gene products, yielding potential biomarker candidates. Bioinformatics analysis revealed enrichment of adhesion/extracellular matrix proteins and progesterone signalling. The best single marker for discriminating endometriosis from controls remained CA125 (AUC = 0.63), with the best cross-validated multimarker models improving the AUC to 0.71–0.81, depending upon menstrual cycle phase and control group. Conclusions We have identified menstrual cycle- and endometriosis-associated protein changes linked to various cellular processes that are potential biomarkers and that provide insight into the biology of endometriosis. Our data indicate that the markers tested, whilst not useful alone, have improved diagnostic accuracy when used in combination and demonstrate menstrual cycle specificity. Tissue heterogeneity and blood contamination is likely to have hindered biomarker discovery, whilst a small sample size precludes accurate determination of performance by cycle phase. Independent validation of these biomarker panels in a larger cohort is however warranted, and if successful, they may have clinical utility in triaging patients for surgery.