Gene Expression and Demographic Factors Associated With Endometriosis Incidence: A Landscape Genetic Approach Publisher



Mahmoudi H ; Pam P ; Javidmehr K ; Moini A ; Noormohammadi Z
Source: Middle East Fertility Society Journal Published:2025

Abstract

Background: Endometriosis is a chronic inflammatory disease that results in female infertility. It is considered a complex disorder that plays a role in the impacts of endometriosis on infertility. The present study was performed in the Iranian women population to provide data on the genetic basis of endometriosis and the role played by different demographic variables like lifestyle factors, locality, ethnicity, etc. Methods: The individuals were divided into two groups: 50 samples, including 25 women with endometriosis and 25 controls. Endometrial tissue and whole blood samples were used for gene expression of MFN2, PINK1, PRKN, and their nine SNPs genotyping, respectively. The multivariate computational methods used for analyzing data on the abovementioned tasks included factor multiple logistic regression, factor analysis of mixed data (FAMD), and redundancy analysis (RDA). STRING was used for protein–protein interaction and K-means clustering. Results: The findings revealed a significant difference (P < 0.05) in the magnitude of gene expression in the target genes studied. PPI interaction (P < 0.0001) with FDR < 0.001 showed the interaction between three genes and clustered together. The FAMD analysis showed that the target genes’ SNP variability is the most contributing variable in differentiating the cases and controls studied. A significant association between the genes and the SNPs studied, as well as with demographic variables, was observed. The RDA analysis revealed a significant association between geographical variables, the gene’ expression magnitude, and the SNPs’ genotypes. In addition, sPCA analyses showed a significant positive and negative eigenvalue (global and local structuring, respectively) of the genetic content of the studied samples by geographical variables. Conclusion: The present study, based on gene expressions and their related SNPs, showed the contribution of these data to geographical and demographical variables. © 2025 Elsevier B.V., All rights reserved.