Analysis of the stability of mirnas candidates for endogenous controls in mesenchymal stem cells derived from menstrual flow in women with endometriosis

• Grant number: 22/01948-7
• Support Opportunities: Scholarships in Brazil - Scientific Initiation
• Start date: May 01, 2022
• End date: December 31, 2022
• Field of knowledge: Health Sciences - Medicine - Maternal and Child Health
• Principal Investigator: Juliana Meola Lovato
• Grantee: Sabrina Yukari Santos Hacimoto
• Host Institution: Faculdade de Medicina de Ribeirão Preto (FMRP). Universidade de São Paulo (USP). Ribeirão Preto , SP, Brazil

Abstract

The etiopathogenesis of endometriosis is complex and multifactorial. Sampson's theory is one of the most consensual for the disease. In addition to this, it was hypothesized that endometrial mesenchymal stem cells present in menstrual flow (MenSCs) would be involved in the development of this disease. Furthermore, it is common that there is a participation of miRNAs acting at the post-transcriptional level in the deregulation of gene expression characteristic for the establishment of endometriosis, which makes them of great interest since RT-qPCR can determine them. However, there is still no consensus on the normalization of miRNAs in MenSCs. Therefore, this unprecedented study seeks to investigate the expression profiles of miRNAs candidates for potential normalizers to obtain endogenous controls for evaluating the differential expression of miRNAs in MenSCs, both in cases of endometriosis and for the wide use made possible by regenerative medicine that exploits these cells. Objective: To evaluate the expression and stability of miRNAs: hsa-miR-30e-5p; hsa-miR-16-5p; hsa-miR-103a-3p; hsa-miR-24-3p; hsa-miR-17-5p; hsa-miR-25-3p; hsa-miR-23a-3p; hsa-miR-101-3p; hsa-mir-22-5p; hsa-mir-191-5p in endometriosis. Study Design: Exploratory experimental study. Methods: Using MenSCs from women with (n=10) and without endometriosis (n=10) previously isolated and characterized. Evaluation of miRNA expression by RT-qPCR. Then, the Ct values will be analyzed using NormFinder, BestKeeper, GeNorm, and DeltaCt algorithms and then compiled for interpretation via RefFinder. (AU)