Exploring Epithelial-Mesenchymal Transition Signals in Endometriosis Diagnosis and In Vitro Fertilization Outcomes.
Vito CelaElisa MalacarneMaria Elena Rosa ObinoIlaria MarziFrancesca PapiniFrancesca VergineElena PisacretaElisa ZappelliDeborah PietrobonoGiorgia ScarfòSimona DanieleFerdinando FranzoniClaudia MartiniPaolo Giovanni ArtiniPublished in: Biomedicines (2021)
Endometriosis (EMS) pathogenesis has been related to the release of inflammatory mediators in peritoneal fluid, creating an altered microenvironment that leads to low-grade oocyte/embryos and to the reduction of implantation rates. The Epithelial-Mesenchymal Transition (EMT), an inflammation-related process, can be a further contributing factor to EMS. This study aimed to investigate, among various cytokines and EMT markers (Cadherins, TGF-β, HIF-1α), diagnostic markers of EMS and prognostic factors of in vitro fertilization (IVF) outcomes. Herein, EMS patients manifested higher serum levels of the inflammatory molecules IL-6, IL-8, and IL-12 and a decrease in the concentrations of the anti-inflammatory IL-10. Moreover, biochemical markers associated with the EMT process were more elevated in serum and follicular fluid (FF) of EMS patients than in controls. At the end, the number of good-quality embryos was inversely related to serum IL-6 and EMT markers. Interestingly, serum IL-6 and FF IL-10 concentrations differentiated EMS patients from controls. Finally, serum IL-8 and E-Cadherin levels, as well as FF IL-10, predicted positive IVF outcome with great accuracy. Our data confirm the pivotal role of inflammatory mediators (i.e., IL-6 and IL-10) in EMS pathogenesis and suggest that EMT-related markers are elevated in EMS patients and can be predictive of IVF outcome.
Keyphrases
- epithelial mesenchymal transition
- prognostic factors
- end stage renal disease
- ejection fraction
- newly diagnosed
- chronic kidney disease
- peritoneal dialysis
- low grade
- oxidative stress
- type diabetes
- adipose tissue
- signaling pathway
- anti inflammatory
- machine learning
- emergency medical
- deep learning
- insulin resistance
- artificial intelligence
- drug induced