Identification of Endometriosis Pathophysiologic-Related Genes Based on Meta-Analysis and Bayesian Approach.

Abstract

Endometriosis is a complex disease with diverse etiologies, including hormonal, immunological, and environmental factors; however, its exact pathogenesis remains unknown. While surgical approaches are the diagnostic and therapeutic gold standard, identifying endometriosis-associated genes is a crucial first step. Five endometriosis-related gene expression studies were selected from the available datasets. Approximately, 14,167 genes common to these 5 datasets were analyzed for differential expression. Meta-analyses utilized fold-change values and standard errors obtained from each analysis, with the binomial and continuous datasets contributing to endometriosis presence and endometriosis severity meta-analysis, respectively. Approximately 160 genes showed significant results in both meta-analyses. For Bayesian analysis, endometriosis-related single nucleotide polymorphisms (SNPs), the human transcription factor catalog, uterine SNP-related gene expression, disease-gene databases, and interactome databases were utilized. Twenty-four genes, present in at least three or more databases, were identified. Network analysis based on Pearson's correlation coefficients revealed the HLA-DQB1 gene with both a high score in the Bayesian analysis and a central position in the network. Although ZNF24 had a lower score, it occupied a central position in the network, followed by other ZNF family members. Bayesian analysis identified genes with high confidence that could support discovering key diagnostic biomarkers and therapeutic targets for endometriosis.