ArticleJournal of clinical medicine2022
Salivary MicroRNA Signature for Diagnosis of Endometriosis.
Article in Journal of clinical medicine, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. It is linked to 2 registered trials, which are not on this map. Cited by 70 papers, 2 of them syntheses that pooled it.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Evaluation of Genetic Signature in Endometriosis Disease by Non Invasive Sampling
Salivary MicroRNA in Endometriosis: Correlation With Progestin Treatment Response: A Prospective Observational Study
Who cites it
70 citing papers in PubMed, 2 syntheses or guidelines pooled it, 130 citations in OpenAlex.
- Diagnostic value of MiRNAs in endometriosis: a systematic review and meta-analysis.BMC women's health · 2026Pooled it
- Diagnostic accuracy of machine learning for endometriosis: a systematic review and meta-analysis.Frontiers in endocrinology · 2025Pooled it
- Assessment of salivary microRNA by RT-qPCR: Facing challenges in data interpretation for clinical diagnosis.PloS one · 2024Trial
- Fascin-Centred Invasive Competence in Eutopic Endometrium: A Hypothesis-Driven Narrative Review of Endometriosis Pathogenesis and Non-Surgical Biomarker Potential.International journal of molecular sciences · 2026Review
- Superficial Peritoneal Endometriosis Beyond Surgical Diagnosis: A Narrative Review of Emerging Functional and Molecular Perspectives.Medicina (Kaunas, Lithuania) · 2026Review
- N-Acetylcysteine in Endometriosis: A Systematic Review of Biological Rationale and Clinical Evidence.Antioxidants (Basel, Switzerland) · 2026Review
- Emerging Pathways to Non-Invasive Diagnosis in Endometriosis: Integrating Machine Learning, Deep Learning and Multi-Omics Biomarkers.Diagnostics (Basel, Switzerland) · 2026Review
- Translational Assessment of Omics Approaches in Endometriosis: Bridging Molecular Discovery with Clinical Utility.International journal of molecular sciences · 2026Review
- Endometriosis and Chronic Endometritis: Shared Mechanisms, Diagnostic Challenges, and Clinical Implications in Infertility.Diagnostics (Basel, Switzerland) · 2026Review
- Clinical Value of Circulating Endometrial Cells in the Diagnosis and Stratified Diagnosis of Endometriosis.Journal of clinical medicine · 2026Article
- Integrating Metabolic and MicroRNA Profiling to the Diagnostics of Endometriosis: A Pilot Study.International journal of molecular sciences · 2026Article
- MicroRNA Expression Profile in Endometriosis and Endometriosis-Associated Ovarian Cancer-Systematic Review.Cells · 2026Review
- Identification of candidate microRNA biomarkers of endometriosis in different bodily fluids.Scientific reports · 2026Article
- Review
- Urinary microRNAs for the non-invasive diagnosis of endometriosis identified by next-generation sequencing and machine learning.Reproductive biology and endocrinology : RB&E · 2026Article
- Current Insights into Endometriosis: Hormonal Management, Clinical Outcomes, and Opportunities for Progress.Current pharmaceutical design · 2026Review
- Profiling miRNA in salivary samples from subjects with endometriosis: a pilot study.Frontiers in molecular biosciences · 2026Article
- Urine and Serum miRNA Signatures for the Non-Invasive Diagnosis of Adenomyosis: A Machine Learning-Based Pilot Study.Diagnostics (Basel, Switzerland) · 2025Article
- Endometriosis During Peri-Menopause and Post-Menopause: A Review of the Literature.Journal of clinical medicine · 2025Review
- Endometriosis During Adolescence: A Narrative Review.Journal of clinical medicine · 2025Review
10 more citing papers are in PubMed but not listed here.
Corrections and comments
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Authors and funding
12 authors at 4 institutions in 1 country.
Funding
Abstract
backgroundEndometriosis 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.
methodsWe 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.
resultsAmong 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%.
conclusionsThe 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.
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What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.