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Background: m6A-RNA modification mediated by the N6-methyladenosine RNA methylation-related molecule methyltransferase-like 3 has been implicated in the progression of endometriosis. However, the functions of other m6A regulators, especially in ovarian endometriosis, remain unknown.
Methods: Three datasets (GSE7305, GSE7307, and GSE37837) with diagnosed ovarian endometriosis were extracted from the Gene Expression Omnibus database. Using bioinformatics methods such as Weighted Gene Co-expression Network Analysis, Gene Ontology analysis, protein-protein interaction, and correlation, hub genes were identified. Using dot blot and N6-methyladenosine-IP-qPCR, the total and individual N6-methyladenosine gene levels were quantified. On clinical ovarian ectopic and eutopic endometrium tissues, N6-methyladenosine RNA methylation sequencing was performed. To authenticate protein localization and expression level, immunohistochemical staining and western blot were conducted, respectively. The database Connectivity Map was used to predict small molecules with potential therapeutic effects.
Results: In ovarian endometriosis, the N6-methyladenosine "reader" molecule IGF2BP2 and related target genes MEIS2 and GATA6 were highly expressed. IGF2BP2 promoted the proliferation, migration, and invasion of ectopic endometrial stromal cells by stabilizing the mRNA of MEIS2 and GATA6. Synergistically, METTL3 and IGF2BP2 increased the N6-methyladenosine methylation of MEIS2 and GATA6. We developed five molecules (Mercaptopurine, MK-886, CP-863187, Canadine, and Securinine) that could be used to treat ovarian endometriosis based on IGF2BP2.
Conclusion: Our findings provided additional support for a systematized understanding of the role of N6-methyladenosine RNA methylation in endometriosis and confirmed for the first time the mechanism of IGF2BP2 in promoting ovarian endometriosis. This provides the molecular foundation for potential future therapies for ovarian endometriosis.
Data Availability: The data used to support the findings of this study are available from the corresponding author upon request.
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http://dx.doi.org/10.1016/j.biocel.2022.106296 | DOI Listing |
Am J Reprod Immunol
September 2025
Department of Obstetrics and Gynecology, Gazi University Faculty of Medicine, Ankara, Turkey.
Problem: Endometriosis is a chronic inflammatory disease that leads to pelvic pain and infertility. Recent studies have indicated that immunological, endocrine, biochemical, and genetic irregularities, along with suboptimal quality of oocytes, embryos, and the endometrial environment, significantly impact infertility associated with endometriosis. Ectopic endometrial cells in endometriosis have the capacity to avoid apoptosis.
View Article and Find Full Text PDFArch Gynecol Obstet
September 2025
Department of Obstetrics and Gynecology, National Clinical Research Center for Obstetric & Gynecologic Diseases, State Key Laboratory of Complex Severe and Rare Diseases, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences & Peking Union Medical College, No.1 Shuaifuyuan, Beij
Purpose: The aim of this study was to summarize and analyze the incidence, underlying causes and related risk factors of misdiagnosis in patients with Obstructed Hemivagina and Ipsilateral Renal Anomaly (OHVIRA) syndrome.
Methods: This is a single center, retrospective study conducted in a tertiary hospital, enrolling patients diagnosed with OHVIRA syndrome in our center between January 2000 and December 2023, with intact charts retrieved. We collected information related to misdiagnosis.
Ann Med
December 2025
Department of Gynecology, The Third Affiliated Hospital of Sun Yat-sen University, Guangzhou, Guangdong Province, China.
Objective: To evaluate preoperative serum calcium levels and their association with deep infiltrating endometriosis (DIE) in ovarian endometrioma.
Design: A retrospective, observational cohort study.
Participants: A total of 2,557 women who underwent surgery for benign ovarian tumors were initially enrolled.
J Minim Invasive Gynecol
September 2025
Department of Gynecology, Obstetrics and Reproductive Medicine, AP-HM, Pôle femmes parents enfants, Marseille, France.
Objective: To develop a machine learning method for the automatic recognition of endometriosis lesions during laparoscopic surgery and evaluate its feasibility and performance.
Design: Collecting and annotating surgical videos and training, validating, and testing a deep neural network.
Setting: Multicenter proof-of-concept study using surgical videos from expert centers in France, Hungary, Brazil, and Denmark.
Oncol Res Treat
September 2025
Background: Ovarian cancer is a prevalent and highly lethal gynaecological cancer. Among its various subtypes, epithelial ovarian cancer predominates, comprising of ten distinct subtypes and contributing significantly to the overall burden of ovarian malignancies. Concurrently, endometriosis, characterised by the ectopic growth of endometrial tissue within the pelvis, affects a substantial number of women of reproductive age.
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