The Different Gene Expression Profile in the Eutopic and Ectopic Endometrium Sheds New Light on the Endometrial Seed in Endometriosis.
Muhammad Assad RiazEzekiel Onyonka MechaCharles O A OmwandhoFelix ZeppernickIvo Meinhold-HeerleinLutz KonradPublished in: Biomedicines (2024)
The changes in endometrial cells, both in the eutopic endometrium of patients with and without endometriosis and in lesions at ectopic sites, are frequently described and often compared to tumorigenesis. In tumorigenesis, the concept of "seed and soil" is well established. The seed refers to tumor cells with metastatic potential, and the soil is any organ or tissue that provides a suitable environment for the seed to grow. In this systematic review (PRISMA-S), we specifically compared the development of endometriosis with the "seed and soil" hypothesis. To determine changes in the endometrial seed, we re-analyzed the mRNA expression data of the eutopic and ectopic endometrium, paying special attention to the epithelial-mesenchymal transition (EMT). We found that the similarity between eutopic endometrium without and with endometriosis is extremely high (~99.1%). In contrast, the eutopic endometrium of patients with endometriosis has a similarity of only 95.3% with the ectopic endometrium. An analysis of EMT-associated genes revealed only minor differences in the mRNA expression levels of claudin family members without the loss of other cell-cell junctions that are critical for the epithelial phenotype. The array data suggest that the changes in the eutopic endometrium (=seed) are quite subtle at the beginning of the disease and that most of the differences occur after implantation into ectopic locations (=soil).
Keyphrases
- epithelial mesenchymal transition
- systematic review
- gene expression
- single cell
- small cell lung cancer
- magnetic resonance
- cell therapy
- signaling pathway
- induced apoptosis
- stem cells
- endometrial cancer
- transforming growth factor
- computed tomography
- randomized controlled trial
- magnetic resonance imaging
- big data
- high resolution
- high throughput
- oxidative stress
- deep learning
- endoplasmic reticulum stress
- transcription factor
- contrast enhanced