Machine learning analysis of ARVC informed by sodium channel protein-based interactome networks.

Background: Arrhythmogenic right ventricular cardiomyopathy (ARVC) is an inherited cardiac disorder characterized by sodium channel dysfunction. However, the clinical management of ARVC remains challenging. Identifying novel compounds for the treatment of ARVC is crucial for advancing drug development. Purpose: In this study, we aim to identify novel compounds for treating ARVC. Methods: Machine learning (ML) models were constructed using proteins analyzed from the scRNA-seq data of ARVC rats and their corresponding protein-protein interaction (PPI) network to predict binding affinity (BA). To validate these predictions, a series of experiments in cardiac organoids were conducted, including Western blotting, ELISA, MEA, and Masson staining to assess the effects of these compounds. Results: We first discovered and identified SCN5A as the most significantly affected sodium channel protein in ARVC. ML models predicted that Kaempferol binds to SCN5A with high affinity. Conclusion: This study presents a novel approach for identifying potential compounds to treat ARVC. By integrating ML modeling with organoid validation, our platform provides valuable support in addressing the public health challenges posed by ARVC, with broad application prospects. Kaempferol shows promise as a lead compound for ARVC treatment.