Exploring the impact of endocrine-disrupting chemicals on erectile dysfunction through network toxicology and machine learning.

backgroundErectile dysfunction (ED) is a common male sexual disorder with a multifactorial etiology. The exposure to endocrine-disrupting chemicals (EDCs) has been increasingly linked to reproductive health disorders in both men and women. EDCs can interfere with hormonal signaling and physiological homeostasis, but their specific roles and mechanisms in contributing to ED remain inadequately elucidated.

methodsNetwork toxicology and enrichment analysis were used to identify potential targets and signaling pathways involved in ED induced by EDCs. Single-cell sequencing was conducted to analyze the expression profiles of these targets in corpus cavernosum tissue. Key regulatory molecules were identified through protein-protein interaction (PPI) network analysis. Core targets were selected using three machine learning algorithms to evaluate the association between EDCs and ED. Molecular docking simulations were further employed to verify the binding affinity between EDCs and target proteins, elucidating potential mechanisms of action.

resultsA total of 186 potential targets were identified. Single-cell sequencing revealed their expression characteristics. PPI analysis identified key regulatory molecules, and machine learning approaches pinpointed two core targets: CTNNB1 and HIF1A. Molecular docking confirmed that most EDCs exhibit stable binding to CTNNB1 and HIF1A, suggesting the involvement of associated signaling pathways in the development of ED.

conclusionsThis study systematically characterizes the molecular pathways through which EDCs contribute to ED, with CTNNB1 and HIF1A emerging as central players. The identification of these core targets provides a theoretical foundation for developing targeted interventions against environment-related ED and underscores the importance of mitigating EDC exposure in public health strategies.