New Evidence About Malignant Transformation of Endometriosis-A Systematic Review

Background: Endometriosis is a benign gynecologic condition that has the risk of malignant transformation in approximately 0.5-1% of cases, most of which develop into endometriosis-associated ovarian cancers (EAOCs), such as clear cell and endometrioid adenocarcinomas. The current systematic review aims to condense recent information on the genetic and molecular mechanisms, clinical risk factors, and possible therapeutic targets of the malignant transformation of endometriosis. Methods: A systematic literature search of PubMed, Europe PMC, and Google Scholar was carried out according to PRISMA guidelines for articles published until December 2024. Following a screening of 44,629 titles, 43 full articles were included after meeting inclusion criteria. No case reports or reviews were included, and articles had to mention a malignant transformation of endometriosis and not just a diagnosis of cancer. The quality and risk of bias of studies were evaluated using ROBINS-I. Results: Malignant transformation of endometriosis is associated with genetic alterations, including ARID1A mutations, microsatellite instability, and abnormal PI3K/Akt and mTOR pathway activation. Increased oxidative stress, inflammation-driven mismatch repair deficiency, and epigenetic alterations like RUNX3 and RASSF2 hypermethylation are implicated in carcinogenesis. Clinical risk factors are advanced age (40-60 years), large ovarian endometriomas (>9 cm), postmenopausal status, and prolonged estrogen exposure. Imaging techniques like MR relaxometry and risk models based on machine learning are highly predictive for early detection. Conclusions: Endometriosis carcinogenesis is a multifactorial process driven by genetic changes, oxidative stress, and inflammatory mechanisms. Identification of high-risk individuals through molecular and imaging biomarkers may result in early detection and personalized therapy. Further research should aim at the development of more precise predictive models and exploration of precision medicine approaches to inhibit the emergence of EAOC.