Global RNA expression analysis of patient samples identified potential diagnostic biomarkers specific for peritoneal, ovarian and deep endometriosis

Endometriosis is a chronic and debilitating gynecological disorder affecting approximately 10% of women of reproductive age worldwide (190 million), often leading to chronic pain, infertility, and considerable economic burden. Despite being recognized for over a century, endometriosis remains challenging to diagnose, thus there is a need for simpler diagnostic methods, and blood biomarker-based approaches-successful in other diseases-are a promising option. While some biomarkers show elevated serum levels in endometriosis, study outcomes vary, and their specificity and sensitivity remain suboptimal. Many of these biomarkers are also linked to other inflammatory conditions, limiting their diagnostic value for endometriosis. To expand the current biomarker landscape, we performed unbiased RNA sequencing analysis of patient-derived endometriosis tissue samples, representing all major subtypes (peritoneal, ovarian, and deep infiltrating), with the aim of identifying potential subtype-specific biomarkers. Our analysis revealed significant differences in gene expression profiles between normal eutopic endometrium and various types of endometriosis. We also observed significant differences in immune cell composition, with notable alterations in the abundance of natural killer (NK) cells and M2 macrophages across subtypes. Importantly, we validated the increased expression of PLA2G2A, ANGPTL7, and PLA2G5 using ELISA in individual endometriosis subtypes, supporting their potential as non-invasive, subtype-specific diagnostic biomarkers pending further validation. This study represents a meaningful advancement in the understanding of subtype-specific molecular and immunological pathways in endometriosis. Our findings are well aligned with current research and provide novel insights into the pathophysiology of distinct endometriosis subtypes. The identified biomarkers could contribute to the development of an improved, subtype-informed diagnostic algorithm for endometriosis.