Diagnostic value of MiRNAs in endometriosis: a systematic review and meta-analysis

BACKGROUND: In recent years, microRNAs have attracted increasing attention for their potential diagnostic and prognostic value across various diseases. This systematic review and meta-analysis aimed to evaluate the diagnostic accuracy of miRNAs as a novel class of non-invasive biomarkers for endometriosis. METHODS: A comprehensive literature search was conducted in PubMed, EMBASE, Web of Science, and the Cochrane Library for studies investigating the diagnostic value of miRNAs in EMs. Eligible studies were selected based on predefined inclusion criteria. A bivariate random-effects model was used to pool key diagnostic parameters, including summary sensitivity (SSEN), summary specificity (SSPE), summary positive likelihood ratio (SPLR), summary negative likelihood ratio (SNLR), diagnostic odds ratio (DOR), and area under the summary receiver operating characteristic curve (AUC), with corresponding 95% confidence intervals (CIs). Subgroup and sensitivity analyses were performed to explore sources of heterogeneity. RESULTS: A total of 118 diagnostic datasets from 30 studies were included, involving 93 distinct miRNAs and 3,274 participants. Pooled estimates indicated moderate–good diagnostic accuracy: sensitivity ≈ 0.82 (95% CI: 0.79–0.84), specificity ≈ 0.79 (95% CI: 0.76–0.82), and AUC ≈ 0.87 (95% CI: 0.84–0.90). Additionally, subgroup analysis revealed comparable diagnostic performance between upregulated and downregulated miRNAs. miRNAs demonstrated moderate–good diagnostic accuracy in multi-miRNA panels compared to those in single miRNA. CONCLUSION: miRNAs show promise as non-invasive diagnostic biomarkers for endometriosis, with robust sensitivity and specificity demonstrated across multiple studies. miRNAs demonstrated moderate–good diagnostic accuracy (AUC ≈ 0.87), with multi-miRNA panels performing best. However, due to considerable heterogeneity among existing studies, further high-quality research is warranted to identify optimal miRNA panels for clinical application.