Non-Coding RNAs in Endometriosis: Molecular Mechanisms, Clinical Applications, and Emerging Computational Approaches- A Systematic Review

Endometriosis is a chronic estrogen-dependent inflammatory disorder affecting approximately 10% of reproductive-age women and remains associated with substantial diagnostic delays, heterogeneous clinical presentation, infertility, chronic pelvic pain, and an increased risk of endometriosis-associated ovarian cancer. Despite advances in understanding disease biology, current diagnostic approaches rely heavily on imaging and surgical visualization, while reliable non-invasive biomarkers and accurate prognostic tools remain limited. Increasing evidence suggests that non-coding RNAs (ncRNAs), including microRNAs (miRNAs), long non-coding RNAs (lncRNAs), circular RNAs (circRNAs), and PIWI-interacting RNAs (piRNAs), play central roles in the molecular pathogenesis of endometriosis and may address existing gaps in diagnosis, disease stratification, and therapeutic targeting. However, findings are dispersed across diverse biological systems and analytical platforms, necessitating an integrated evaluation of their mechanistic and clinical significance. This systematic review provides a comprehensive synthesis of current evidence regarding the biological functions, molecular mechanisms, and translational applications of ncRNAs in endometriosis. We examine the involvement of ncRNAs in key pathogenic processes, including hormonal dysregulation, progesterone resistance, inflammation, immune dysfunction, epithelial–mesenchymal transition, angiogenesis, apoptosis resistance, fibrosis, metabolic reprogramming, infertility, and malignant transformation. In addition, we evaluate tissue-, blood-, saliva-, exosome-, follicular fluid-, menstrual blood, peritoneal fluid and cumulus cell-derived ncRNA signatures as diagnostic and prognostic biomarkers across disease stages and phenotypes, together with genetic variants at ncRNA loci identified through genome-wide and candidate-gene association studies. The review further explores the contribution of bioinformatics and machine learning to ncRNA network analysis, biomarker discovery, and predictive model development. Collectively, the available evidence positions ncRNAs as pivotal molecular regulators and promising translational tools that may facilitate early diagnosis, precision diagnosis, prognostic stratification, and the development of targeted therapeutic strategies for endometriosis.