Machine Learning Analysis of ARVC Informed by Sodium Channel Protein-Based Interactome Networks

Yanan Zhu¹Hui Zhang¹Xuan Zhao¹Xin Wang²Lina Xing³Sijie Yao¹Xiao Jin¹Tingting Li¹Ting Dai¹Xinyue Ding¹Zhen Qi⁴Zongjun Liu^1*

• ¹Department of Cardiology, Putuo Hospital Affiliated to Shanghai University of Traditional Chinese Medicine, Shanghai, China
• ²Shanghai Key Laboratory of Regulatory Biology, Institute of Biomedical Sciences, East China Normal University, Shanghai, China
• ³Department of GCP Office, Shanghai Ninth People’s Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China
• ⁴Neocellmed Co., Ltd. Shanghai, China, Shanghai, China

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. In vitro experiments further confirmed that Kaempferol exerted therapeutic effects in ARVC. Conclusions: 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.