Exosomal MicroRNAs as Epigenetic Biomarkers for Endometriosis: A Systematic Review and Bioinformatics Analysis
Bibliographic record
Abstract
The clinical application of exosomal microRNAs as diagnostic biomarkers presents a promising approach for identifying potential markers of endometriosis. We conducted a systematic review of case-control studies to investigate exosomal microRNAs as epigenetic biomarkers potentially involved in the pathogenesis of endometriosis. A comprehensive literature search was performed across PubMed, Embase, Web of Science, and Scopus databases, yielding 702 studies, with 12 meeting the inclusion criteria after screening and full-text review. These studies included 191 women with confirmed endometriosis and 169 healthy controls. Quality assessment using the Newcastle-Ottawa Scale indicated a moderate quality across studies, with a common score of 5/9. In total, 668 exosomal microRNAs were found to be significantly differentially expressed between endometriosis patients and controls. In serum samples, 119 exosomal microRNAs were differentially expressed, with miR-22-3p, miR-320a, miR-320b, and miR-1273g-3p reported in more than one study. In endometrial tissue samples, miR-200c-3p and miR-425-5p were identified in more than one study, with miR-200c-3p consistently upregulated. Bioinformatic analysis indicated that these exosomal microRNAs are involved in key signaling pathways such as PI3K/Akt, MAPK, and TGF-β, which are associated with cell proliferation, migration, and inflammation. Despite these promising findings, variability in exosomal microRNA expression patterns across studies underscores the need for standardized methods and validation in large-scale, ethnically diverse cohorts. Future research should focus on rigorous validation studies to establish clinically relevant exosomal microRNAs for early diagnosis and improved patient outcomes.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.009 | 0.026 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.007 | 0.007 |
| Bibliometrics | 0.019 | 0.016 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".