Endometriosis diagnosis constitutes a considerable economic burden for the healthcare system with diagnostic tools often inconclusive with insufficient accuracy. We sought to analyze the human miRNAome to define a saliva-based diagnostic miRNA signature for endometriosis.
Methods
We performed a prospective ENDO-miRNA study involving 200 saliva samples obtained from 200 women with chronic pelvic pain suggestive of endometriosis collected between January and June 2021. The study consisted of two parts: (i) identification of a biomarker based on genome-wide miRNA expression profiling by small RNA sequencing using next-generation sequencing (NGS) and (ii) development of a saliva-based miRNA diagnostic signature according to expression and accuracy profiling using a Random Forest algorithm.
Results
Among the 200 patients, 76.5% (n = 153) were diagnosed with endometriosis and 23.5% (n = 47) without (controls). Small RNA-seq of 200 saliva samples yielded ~4642 M raw sequencing reads (from ~13.7 M to ~39.3 M reads/sample). Quantification of the filtered reads and identification of known miRNAs yielded ~190 M sequences that were mapped to 2561 known miRNAs. Of the 2561 known miRNAs, the feature selection with Random Forest algorithm generated after internally cross validation a saliva signature of endometriosis composed of 109 miRNAs. The respective sensitivity, specificity, and AUC for the diagnostic miRNA signature were 96.7%, 100%, and 98.3%.
Conclusions
The ENDO-miRNA study is the first prospective study to report a saliva-based diagnostic miRNA signature for endometriosis. This could contribute to improving early diagnosis by means of a non-invasive tool easily available in any healthcare system.
Bendifallah S et al., 2024·European journal of obstetrics, gynecology, and reproductive biology
Patients with superficial peritoneal endometriosis (SPE) present with symptoms suggestive of endometriosis but clinical and imaging exams are inconclusive. Consequently, laparoscopy is usually necessary to confirm diagnosis. The present study aimed to evaluate the accuracy of microRNAs (miRNAs) to diagnose patients with SPE from the ENDOmiARN cohort This prospective study (NCT04728152) included 200 saliva samples obtained between January and June 2021 from women with pelvic pain suggestive of endometriosis. All patients underwent either laparoscopy and/or MRI to confirm the presence of endometriosis. Among the patients with endometriosis, two groups were defined: an SPE phenotype group of patients with peritoneal lesions only, and a non-SPE control group of patients with other endometriosis phenotypes (endometrioma and/or deep endometriosis). Data analysis consisted of two parts: (i) identification of a set of miRNA biomarkers using next-generation sequencing (NGS), and (ii) development of a saliva-based miRNA signature for the SPE phenotype in patients with endometriosis based on a Random Forest (RF) model.
Among the 153 patients with confirmed endometriosis, 10.5 % (n = 16) had an SPE phenotype. Of the 2633 known miRNAs, the feature selection method generated a signature of 89 miRNAs of the SPE phenotype. After validation, the best model, representing the most accurate signature had a 100 % sensitivity, specificity, and AUC.
This signature could constitute a new diagnostic strategy to detect the SPE phenotype based on a simple biological test and render diagnostic laparoscopy obsolete. PRéCIS: We generated a saliva-based signature to identify patients with superficial peritoneal endometriosis which is the most challenging form of endometriosis to diagnose and which is often either misdiagnosed or requires invasive laparoscopy.
endometriosis/diagnosis/biomarkersgynecology/pelvic-pain/chronic-pelvic-paindiagnostics/endometrial-assessment/luteal-phase-biopsy
Open Access
Background/Objectives: Endometriosis represents substantial direct and indirect healthcare costs impacted by an absence of uniformly accurate, non-invasive diagnostic tools. We endeavored to demonstrate gastrointestinal myoelectrical activity (GIMA) biomarkers, unique to endometriosis, will allow non-invasive, uniformly accurate diagnosis or exclusion of endometriosis. Prospective open-label comparative study of 154 patients, age ≥ 18, with or without diagnosed endometriosis. Population included 62 non-endometriosis controls (Cohort 1), 43 subjects with surgically/histologically confirmed endometriosis (Cohort 2), and 49 subjects with abdominal pain and negative imaging (Cohort 3). Non-invasive electroviscerography (EVG) recorded GIMA biomarkers from three abdominal electrodes before and 30 min post water load protocol. Cohort 2 had postoperative EVG and Cohort 3 had preoperative EVG. Calculated specificity, sensitivity, negative predictive value (NPV), positive predictive value (PPV), and predictive probability or C-statistic used univariate, multivariate, linear, and logistical regression analyses of the area under the curve (AUC) at all frequency and time points, including age and pain covariants. The non-endometriosis cohort differed significantly from the endometriosis cohorts (p < 0.001) for median (IQR) and AUC percent frequency distribution of power at baseline, 10 min, 20 min, and 30 min post water load at all frequency ranges: 15-20 cpm, 30-40 cpm, and 40-50 cpm. The endometriosis cohorts were statistically similar (p > 0.05). GIMA biomarker threshold scoring demonstrated 95%/91% sensitivity and PPV, 96%/95% specificity and NPV, and a C-statistic of >99%/98%, respectively, for age subsets. GIMA biomarkers in Cohort 3 predicted 47/49 subjects positive and 2/49 negative for endometriosis, confirmed surgically. Hormonal therapy, surgical stage, nor pain score affected diagnostic accuracy. EVG with GIMA biomarker detection distinguished participants with and without endometriosis based upon endometriosis-specific GIMA biomarkers threshold scoring.
National Institute for Health and Care Excellence, 2026·National Institute for Health and Care Excellence
Second draft guidance (consultation 15 September to 5 October 2026; expected publication 21 January 2027). Recommendation 1.1: Endotest (Ziwig, saliva 109-microRNA signature, CE IVDR class C, ages 18 to 43, GBP 1,381 per test) can be used in the NHS during a 4-year evidence generation period as an option to diagnose endometriosis in primary care, only when clinical examination is normal and ultrasound is negative, inconclusive, declined or not suitable. Recommendation 1.5: more research is needed on DotEndo (DotLab blood microRNA, GBP 400), Endomkit (Camlab/apDia serum BDNF plus CA-125, GBP 28 to 199) and EndoSure (gastrointestinal myoelectrical activity, GBP 350) before NHS funding. Committee noted all peer-reviewed accuracy evidence came from secondary or tertiary care where prevalence is higher than in primary care; the developmental Endotest cohort was judged at high risk of bias and the external validation studies at unclear risk; DotEndo, Endomkit and EndoSure had no external validation studies; no study reported clinical outcomes; the technologies cannot distinguish types of endometriosis or determine severity; clinical experts stated a negative result should not be a reason to deny referral if endometriosis is still suspected; imaging does not identify all types, particularly superficial peritoneal endometriosis. Average time to diagnosis in the UK cited as 9 years 4 months.
endometriosis/diagnosis/biomarkersdiagnostics/genetic-and-immune-testing/karyotype-and-genetic-panels
Open Access