Validation of a Saliva Micro-RNA Signature for Endometriosis

Bendifallah S, Sofiane Bendifallah, Roman H, Horace Roman, Stéphane Suisse, Suisse S, Andrew Spiers, Spiers A, Petit E, Erick Petit, E. Petit, Delbos L, Léa Delbos, Yohann Dabi, Dabi Y, Cyril Touboul, Touboul C, Thomas Dennis, Dennis T, T. Dennis Thomas, Merlot B, Benjamin Merlot, Sauvanet E, Eric Sauvanet, Éric Sauvanet, Fauvet R, Raffaèle Fauvet, R. Fauvet, Jamard E, Estelle Jamard, Clotilde H, Hennetier Clotilde, Morgane P, Perrin Morgane, Fedida B, Benjamin Fedida, Krystel Nyangoh, Nyangoh K, Vincent Lavoué, Claire-Marie Roger, Roger CM, Claire‐Marie Roger, Lucas N, Nicolas Lucas, Darnaud T, Thomas Darnaud, Boudy AS, Anne‐Sophie Boudy, Anne-Sophie Boudy, Ludivine Genre, Genre L, Pierre Lèguevaque, Leguevaque P, Pierre Leguevaque, Akldios C, Cherif Akldios, Benjoar M, Mikhael Benjoar, Élodie Chantalat, Chantalat E, Elodie Chantalat, Yann Tanguy Le Gac, Tanguy Le Gac Y, Poilblanc M, Mathieu Poilblanc, Pascal Rousset, Rousset P, H. Fernandez, Fernandez H, Hervé Fernandez, Francois Golfier, François Golfier, Golfier F, Descamps P, Philippe Descamps
NEJM evidence · 2025 · vol. 4(11) , pp. EVIDoa2400195 · doi:10.1056/evidoa2400195 · PMID:41147827 · W4415619543
article OA: closed CC0 ⤵ 2 in-corpus citations
Full text JSON View on OpenAlex View on PubMed View at publisher
⚙ AI-generated summary by claude@2026-06, 2026-06-08 ⓘ

This study validated a saliva micro-RNA signature for endometriosis, showing high diagnostic accuracy, sensitivity, and specificity in a large, multicenter cohort.

One-sentence paraphrase of the abstract; not a substitute for reading it. No clinical advice. How this works

⚙ AI-generated deep summary by claude@2026-07, 2026-07-16 · read from full text ⓘ

This prospective, multicenter external validation study assessed the diagnostic accuracy, biological reproducibility, and clinical utility of a previously developed saliva micro-RNA signature for endometriosis (ENDOmiRNA) in patients aged 18 to 43 years with symptoms suggestive of endometriosis, with endometriosis status determined by imaging and/or laparoscopy and/or histology and controls selected as patients without endometriosis who all underwent laparoscopy. In 971 participants (77% endometriosis prevalence), the saliva miRNA signature achieved 96.6% accuracy with a sensitivity of 97.3% and specificity of 94.1%, and among surgically confirmed cases showed lower misclassification/underestimation/overestimation rates than imaging alone. The study explicitly frames its main limitation as the need for external validation of a prior diagnostic signature. This paper is centrally about endometriosis — validation of a saliva miRNA signature (ENDOmiRNA) for diagnosing endometriosis.

Read from the paper's body, not the abstract. Not a substitute for reading the paper. No clinical advice. How this works

Abstract

BACKGROUND: 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. METHODS: 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. RESULTS: 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). CONCLUSIONS: 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.).
Full text 5,119 characters · extracted from oa-doi-fallback · 4 sections · click to expand

Abstract

Background 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.

Methods

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.

Results

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).

Conclusions

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.) Notes A data sharing statement provided by the authors is available with the full text of this article. Supported by unrestricted grants from Ziwig. Disclosure forms provided by the authors are available with the full text of this article. We would like to extend our sincere thanks to Sophie Beranger, Lucie Bonin, Delphine Bouteiller, Patrice Crochet, Pierre Descargues, Nathalie Hoen, Zoe Husson, Ludmila Jornea, Alexandra Madar, Frédérique Perotte, Delphine Raffin, Salma Touleimat, Mélusine Turck, Eric Verspyck, and Emna Younsi for their contributions to this work. Supplementary Material Information & Authors Information Published In NEJM Evidence Copyright Copyright © 2025 Massachusetts Medical Society. For personal use only. Any commercial reuse of NEJM Group content requires permission. History Published online: October 28, 2025 Published in issue: October 28, 2025 Topics Authors Metrics & Citations Metrics Altmetrics Citations Export citation Select the format you want to export the citation of this publication. Cited by - Changing the paradigm of endometriosis – from diagnosis to integrated long-term management: a joint society opinion paper, Reproductive BioMedicine Online, 53, 1, (105642), (2026).https://doi.org/10.1016/j.rbmo.2026.105642 - Letter to the Editor: Clues to revising the conventional diagnostic algorithm for endometriosis, International Journal of Gynecology & Obstetrics, 173, 3, (1657-1658), (2026).https://doi.org/10.1002/ijgo.71051 - Response: Clues to revising the conventional diagnostic algorithm for endometriosis, International Journal of Gynecology & Obstetrics, 173, 3, (1654-1656), (2026).https://doi.org/10.1002/ijgo.71050 - Clinical Value of Circulating Endometrial Cells in the Diagnosis and Stratified Diagnosis of Endometriosis, Journal of Clinical Medicine, 15, 8, (3021), (2026).https://doi.org/10.3390/jcm15083021 - SGLT2 Inhibitors and Mortality in Endometriosis With Type 2 Diabetes, JACC: Advances, (102681), (2026).https://doi.org/10.1016/j.jacadv.2026.102681 - Menstrual Effluent in the Pathogenesis and Diagnosis of Endometriosis—A Systematic Review, Diagnostics, 16, 5, (677), (2026).https://doi.org/10.3390/diagnostics16050677 - Non-Invasive Methods for Early Diagnosis of Endometriosis—A Comprehensive Narrative Literature Review, Healthcare, 13, 24, (3276), (2025).https://doi.org/10.3390/healthcare13243276 - Urine and Serum miRNA Signatures for the Non-Invasive Diagnosis of Adenomyosis: A Machine Learning-Based Pilot Study, Diagnostics, 15, 23, (3012), (2025).https://doi.org/10.3390/diagnostics15233012 Loading...

Text is read by the "Ask this paper" AI Q&A widget below. Extraction quality varies by source — PMC NXML preserves structure cleanly, OA-HTML may include some navigation residue, and OA-PDF can have broken hyphenation. The publisher copy (via DOI) is the canonical version.

My notes (saved in your browser only)

⚙ Ask this paper AI returns verbatim quotes from the full text · source: oa-doi-fallback ⓘ

Answers must be backed by verbatim quotes from this paper's full text. Hallucinated quotes are dropped automatically; if no verbatim passage answers the question, we say so. How this works

Condition tags

endometriosis

MeSH descriptors

Endometriosis Endometriosis Endometriosis Endometriosis Endometriosis Endometriosis Endometriosis Endometriosis Endometriosis Endometriosis Endometriosis Endometriosis Endometriosis Endometriosis Endometriosis Endometriosis Endometriosis Endometriosis Endometriosis Endometriosis

Citation neighborhood

Papers in the corpus that this work cites (lower rings, blue) and that cite this one (upper rings, green). Dot size scales with the paper's in-corpus citation count — bigger dot = more influential within the endo/adeno field. Click a dot to open that paper. [ expand to 2 hops ] — adds papers reached through this work's immediate citers/citees. Heavier; up to 60 extra dots.

References (36)

Cited by (8)

Source provenance

europepmc
last seen: 2026-07-26T06:08:39.051465+00:00
openalex
last seen: 2026-06-10T17:14:06.276822+00:00
pubmed
last seen: 2026-07-26T06:04:21.402177+00:00
unpaywall
last seen: 2026-06-13T06:42:57.164913+00:00
License: CC0 · commercial use OK