Saliva-based microRNA diagnostic signature for the superficial peritoneal endometriosis phenotype

Sofiane Bendifallah; Yohann Dabi; Stéphane Suisse; Johanna Ilic; Léa Delbos; Mathieu Poilblanc; Philippe Descamps; Francois Golfier; Ludmila Jornea; Delphine Bouteiller; Cyril Touboul; Anne Puchar; Emile Daraï

doi:10.1016/j.ejogrb.2024.04.020

Saliva-based microRNA diagnostic signature for the superficial peritoneal endometriosis phenotype

Eur J Obstet Gynecol Reprod Biol. 2024 Apr 17:297:187-196. doi: 10.1016/j.ejogrb.2024.04.020. Online ahead of print.

Authors

Affiliations

¹ Sorbonne University - Department of Obstetrics and Reproductive Medicine, Hôpital Tenon, 4 rue de la Chine, 75020 Paris, France; Clinical Research Group (GRC) Paris 6: Centre Expert Endométriose (C3E), Sorbonne University (GRC6 C3E SU), France. Electronic address: sofiane.bendifallah@aphp.fr.
² Sorbonne University - Department of Obstetrics and Reproductive Medicine, Hôpital Tenon, 4 rue de la Chine, 75020 Paris, France; Cancer Biology and Therapeutics, Centre de Recherche Saint-Antoine (CRSA), Sorbonne University, INSERM UMR_S_938 75020 Paris, France.
³ Ziwig, 19 rue Reboud, Lyon, France.
⁴ Sorbonne University - Department of Obstetrics and Reproductive Medicine, Hôpital Tenon, 4 rue de la Chine, 75020 Paris, France.
⁵ Department of Obstetrics and Reproductive Medicine - CHU d'Angers, Endometriosis Expert Center - Pays de la Loire, France. Electronic address: lea.delbos@chu-angers.fr.
⁶ Department of Obstetrics and Gynecology, Lyon Sud University Hospital, Hospices Civils de Lyon, Lyon1 University, Lyon, France; Endometriosis Expert Center, President of the EndAURA Network.
⁷ Department of Obstetrics and Reproductive Medicine - CHU d'Angers, Endometriosis Expert Center - Pays de la Loire, France.
⁸ Sorbonne Université, Institut du Cerveau - Paris Brain Institute - ICM, Inserm, CNRS, APHP, Hôpital de la Pitié Salpêtrière, Paris, France.
⁹ Genotyping and Sequencing Core Facility, iGenSeq, Institut du Cerveau et de la Moelle Epinière, ICM, Hôpital Pitié-Salpêtrière, 47-83 Boulevard de l'Hôpital, 75013 Paris, France.
¹⁰ Sorbonne University - Department of Obstetrics and Reproductive Medicine, Hôpital Tenon, 4 rue de la Chine, 75020 Paris, France; Clinical Research Group (GRC) Paris 6: Centre Expert Endométriose (C3E), Sorbonne University (GRC6 C3E SU), France.

PMID: 38677096
DOI: 10.1016/j.ejogrb.2024.04.020

Abstract

Objective: 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.

Keywords: Diagnostic laparoscopy; Endometriosis phenotype; Machine Learning; Next-generation Sequencing; Superficial peritoneal endometriosis; microRNA.