Accurate diagnosis of endometriosis using serum microRNAs

BackgroundEndometriosis, 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 noninvasive 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.ObjectiveThe objective of the study was to validate the use of a microRNA panel as a noninvasive diagnostic method for detecting endometriosis.Study DesignThis was a prospective study evaluating subjects with a clinical indication for gynecological 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 quantitative real-time polymerase chain reaction in a blinded fashion without knowledge of disease status. Receiver-operating characteristic analysis was performed on individual microRNAs as well as combinations of microRNAs. An algorithm combining the expression values of these microRNAs, 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 data set of 48 previously identified subjects not included in the training set (24 endometriosis and 24 controls) to validate its diagnostic performance.ResultsThe mean age of women in the study population was 34.1 and 36.9 years 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 4 serum microRNAs: miR-125b-5p, miR-150-5p, miR-342-3p, and miR-451a. Two serum microRNAs showed significantly lower levels in the endometriosis group: miR-3613-5p and let-7b. Individual microRNAs had receiver-operating characteristic areas under the curve ranging from 0.68 to 0.92. A classifier combining these microRNAs yielded an area under the curve of 0.94 when validated in the independent set of subjects not included in the training set. Analysis of the expression levels of each microRNA based on revised American Society of Reproductive Medicine staging revealed that all microRNAs 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 the menstrual cycle or use of hormonal medication had a significant impact on the expression levels in the microRNAs used in our algorithm.ConclusionThis is the first report showing that microRNA biomarkers can reliably differentiate between endometriosis and other gynecological pathologies with an area under the curve >0.9 across 2 independent studies. We validated the performance of an algorithm based on previously identified microRNA biomarkers, demonstrating their potential to detect endometriosis in a clinical setting, allowing earlier identification and treatment. The ability to diagnose endometriosis noninvasively could reduce the time to diagnosis, surgical risk, years of discomfort, disease progression, associated comorbidities, and health care costs. 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 noninvasive 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. The objective of the study was to validate the use of a microRNA panel as a noninvasive diagnostic method for detecting endometriosis. This was a prospective study evaluating subjects with a clinical indication for gynecological 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 quantitative real-time polymerase chain reaction in a blinded fashion without knowledge of disease status. Receiver-operating characteristic analysis was performed on individual microRNAs as well as combinations of microRNAs. An algorithm combining the expression values of these microRNAs, 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 data set of 48 previously identified subjects not included in the training set (24 endometriosis and 24 controls) to validate its diagnostic performance. The mean age of women in the study population was 34.1 and 36.9 years 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 4 serum microRNAs: miR-125b-5p, miR-150-5p, miR-342-3p, and miR-451a. Two serum microRNAs showed significantly lower levels in the endometriosis group: miR-3613-5p and let-7b. Individual microRNAs had receiver-operating characteristic areas under the curve ranging from 0.68 to 0.92. A classifier combining these microRNAs yielded an area under the curve of 0.94 when validated in the independent set of subjects not included in the training set. Analysis of the expression levels of each microRNA based on revised American Society of Reproductive Medicine staging revealed that all microRNAs 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 the menstrual cycle or use of hormonal medication had a significant impact on the expression levels in the microRNAs used in our algorithm. This is the first report showing that microRNA biomarkers can reliably differentiate between endometriosis and other gynecological pathologies with an area under the curve >0.9 across 2 independent studies. We validated the performance of an algorithm based on previously identified microRNA biomarkers, demonstrating their potential to detect endometriosis in a clinical setting, allowing earlier identification and treatment. The ability to diagnose endometriosis noninvasively could reduce the time to diagnosis, surgical risk, years of discomfort, disease progression, associated comorbidities, and health care costs.