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
Study Design
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.
Results
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.
Conclusion
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.
Bendifallah S et al., 2022·Journal of clinical medicine
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.
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.
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%.
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.
endometriosis/diagnosis/biomarkersdiagnostics/genetic-and-immune-testing/karyotype-and-genetic-panels
Open Access
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.
Diagnosis of endometriosis is a challenge. The recent development of a saliva-based micro-ribonucleic acid (miRNA) signature for the diagnosis of endometriosis may enable a timelier and less invasive approach, but this requires external validation.
The prospective, multicenter validation of the salivary miRNA signature of endometriosis (ENDOmiRNA) study aimed to assess the diagnostic accuracy, validate the biological reproducibility, and evaluate the clinical utility of a saliva miRNA signature of endometriosis. The study population comprised patients 18 to 43 years of age with signs and symptoms suggestive of endometriosis, who were recruited from diverse medical settings. Patients received a diagnosis of endometriosis by imaging, laparoscopic procedure, or both. All patients who were determined to not have endometriosis were classified as controls (and all underwent laparoscopy). Assessment of endometriosis status based on the saliva miRNA signature was established blinded to patients' endometriosis status, as determined by imaging and/or laparoscopy and/or histology.
The external validation population was composed of 971 patients, including patients from a prior interim analysis, with an overall endometriosis prevalence of 77%. The saliva miRNA signature had an accuracy (defined as the probability of correct classification for both positive and negative results) of 96.6% (95% confidence interval [CI], 95.2 to 97.6%), a sensitivity of 97.3% (95% CI, 96.4 to 98.0%), a specificity of 94.1% (95% CI, 91.0 to 96.4%), a positive predictive value of 98.2% (95% CI, 97.3 to 98.9%), a negative predictive value of 91.3% (95% CI, 88.3 to 93.4%), a positive likelihood ratio of 16.6 (95% CI, 10.8 to 26.9), and a negative likelihood ratio of 0.03 (95% CI, 0.02 to 0.04). Among patients with surgical confirmation of the diagnosis, misclassification, underestimation, and overestimation rates were 4.6%, 2.4%, and 2.2%, respectively, for the saliva miRNA signature and 27.2%, 15.1%, and 12.2%, respectively, for imaging (either transvaginal ultrasound, magnetic resonance imaging, or both).
This prospective, multicenter external validation study demonstrated the accurate performance of a saliva-based miRNA signature for the diagnosis of endometriosis in this cohort. (Funded by Ziwig; ClinicalTrials.gov number, NCT05244668.).