Endometriosis affects a large number of women of reproductive age, and its pathogenesis is still unclear. It causes severe chronic pelvic pain, which is often misdiagnosed as irritable bowel syndrome, or other disorders. Metabolomics and transcriptomic approaches enable the study of changes in various physiological or pathological pathways to identify new potential biomarkers. We employed gas chromatography–mass spectrometry (GC–MS) to investigate metabolic alterations, and quantitative real-time polymers-chain reaction (RT-qPCR) to assess changes in miR-451a and miR-125b in saliva in endometriosis. Serum and saliva samples of patients with symptomatic endometriosis and volunteers without it were collected and subjected to GC–MS and qPCR-RT analysis, respectively. Multivariate and univariate statistical analyses were performed. Orthogonal partial least squares discriminant analysis has shown the differences between the two groups. Eicosadienoic acid, arachidonic acid, and miR-451a increased significantly in endometriosis patients. Machine learning methods were used to build the predictive model, which can be used in early low-invasive diagnostics of endometriosis. Receiver operating characteristics analysis has tested the diagnostic power of metabolites. The combination of metabolic and microRNA profiling may improve our knowledge of the pathophysiological and signaling mechanisms in endometriosis and the discovery of new efficient biomarkers of endometriosis.
Shansky et al. (2026) studied this question.