Understanding the Molecular Landscape of Endometriosis: A Bioinformatics Approach to Uncover Signaling Pathways and Hub Genes

Abstract

Background: Endometriosis is a chronic gynecological disorder characterized by the ectopic growth of endometrial tissue outside the uterus, leading to debilitating pain and infertility in affected women. Despite its prevalence and clinical significance, the molecular mechanisms underlying the progression of endometriosis remain poorly understood. This study employs bioinformatics tools and molecular docking simulations to unravel the intricate genetic and molecular networks associated with endometriosis progression. Objectives: The primary objectives of this research are to identify differentially expressed genes (DEGs) linked to endometriosis, elucidate associated biological pathways using the Database for Annotation, Visualization, and Integrated Discovery (DAVID), construct a Protein-Protein Interaction (PPI) network to identify hub genes, and perform molecular docking simulations to explore potential ligand-protein interactions associated with endometriosis. Methods: Microarray data from Homo sapiens, specifically Accession: GDS3092 Series = GSE5108 (Platform: GPL2895), were retrieved from the NCBI Gene Expression Omnibus (GEO). The data underwent rigorous preprocessing and DEG analysis using NCBI GEO2. Database for Annotation, Visualization, and Integrated Discovery analysis was employed for functional annotation, and a PPI network was constructed using the STITCH database and Cytoscape 3.8.2. Molecular docking simulations against target proteins associated with endometriosis were conducted using MVD 7.0. Results: A total of 1 911 unique elements were identified as DEGs associated with endometriosis from the microarray data. Database for Annotation, Visualization, and Integrated Discovery analysis revealed pathways and biological characteristics positively and negatively correlated with endometriosis. Hub genes, including BCL2, CCNA2, CDK7, EGF, GAS6, MAP3K7, and TAB2, were identified through PPI network analysis. Molecular docking simulations highlighted potential ligands, such as Quercetin-3-o-galactopyranoside and Kushenol E, exhibiting favorable interactions with target proteins associated with endometriosis. Conclusions: This study provides insights into the molecular signatures, pathways, and hub genes associated with endometriosis. Utilizing DAVID in this study clarifies biological pathways associated with endometriosis, revealing insights into intricate genetic networks. Molecular docking simulations identified ligands for further exploration in therapeutic interventions. The consistent efficacy of these ligands across diverse targets suggests broad-spectrum effectiveness, encouraging further exploration for potential therapeutic interventions. The study contributes to a deeper understanding of endometriosis pathogenesis, paving the way for targeted therapies and precision medicine approaches to improve patient outcomes. These findings advance our understanding of the molecular mechanisms in endometriosis (EMS), offering promising avenues for future research and therapeutic development in addressing this complex condition.