Accurate Diagnosis of Endometriosis Using Serum MicroRNAs

Abstract Background Endometriosis, a chronic disease that afflicts millions of women worldwide, has traditionally been diagnosed by laparoscopic surgery. This diagnostic barrier delays identification and treatment by years, resulting in prolonged pain and disease progression. Development of a non-invasive diagnostic test could significantly improve timely disease detection. We tested the feasibility of serum microRNAs as diagnostic biomarkers of endometriosis in women with gynecologic disease symptoms. Objective To validate the use of a microRNA panel as a non-invasive diagnostic method for detecting endometriosis. Study Design This was a prospective study evaluating subjects with a clinical indication for gynecologic surgery in an academic medical center. Serum samples were collected prior to surgery from 100 subjects. Women were selected based on the presence of symptoms and laparoscopy was performed to determine the presence or absence of endometriosis. The control group was categorized based on absence of visual disease at the time of surgery. Circulating miRNAs miR-125b-5p, miR-150-5p, miR-342-3p, miR-451a, miR-3613-5p and let-7b were measured in serum by qRT-PCR in a blinded fashion, without knowledge of disease status. Receiver operating characteristic (ROC) analysis was performed on individual miRNAs, as well as combinations of miRNAs. An algorithm combining the expression values of these miRNAs, built using machine learning with a Random Forest classifier, was generated to predict the presence or absence of endometriosis on operative findings. This algorithm was then tested in an independent dataset of 48 previously identified subjects not included in the training set (24 endometriosis and 24 controls) to validate its diagnostic performance. Results The mean age of women in the study population was 34.1 and 36.9 for the endometriosis and control groups, respectively. Control group subjects displayed varying pathologies, with leiomyoma occurring the most often (n=39). Subjects with endometriosis had significantly higher expression levels of four serum miRNAs: miR-125b-5p, miR-150-5p, miR-342-3p, and miR-451a. Two serum miRNAs showed significantly lower levels in the endometriosis group: miR-3613-5p and let-7b. Individual miRNAs had ROC areas under the curve (AUC) ranging from 0.68 to 0.92. A classifier combining these miRNAs yielded an AUC of 0.94 when validated in the independent set of subjects not included in the training set. Analysis of the expression levels of each miRNA based on rASRM staging revealed that all miRNAs could distinguish Stage I/II from control, and Stage III/IV from control, but that the difference between Stage I/II and Stage III/IV was not significant. Subgroup analysis revealed that neither phase of menstrual cycle or use of hormonal medication significantly impacted expression levels in the miRNAs used in our algorithm. Conclusions This is the first report showing that miRNA biomarkers can reliably differentiate between endometriosis and other gynecologic pathologies with an AUC > 0.9 across two independent studies. We validated the performance of an algorithm based on previously identified miRNA biomarkers, demonstrating their potential to detect endometriosis in a clinical setting, allowing earlier identification and treatment. The ability to diagnose endometriosis non-invasively could reduce the time to diagnosis, surgical risk, years of discomfort, disease progression, associated co-morbidities and healthcare costs.