Integrative bioinformatics and machine learning combined with experimental validation in a doxorubicin-induced model identify BACH2, NXPH4, CD1E, and LIF as sodium overload-related molecular signatures in dilated cardiomyopathy.

Abstract

INTRODUCTION: Dilated cardiomyopathy (DCM) is a leading cause of heart failure and remains a major clinical challenge due to its complex etiology and lack of effective targeted therapies. Sodium overload-induced necrosis, a recently described form of regulated cell death, has emerged as a novel contributor to cardiovascular injury, but its role in DCM remains poorly defined. AIMS: This study aimed to elucidate the molecular signatures of sodium overload-associated cell death and explore their diagnostic and therapeutic relevance in DCM. MATERIALS AND METHODS: We systematically integrated four publicly available transcriptomic datasets (GSE226801, GSE116250, and GSE141910) from human myocardial tissues and in vitro sodium-overload models to identify sodium overload-associated death-related genes (DRGs). Machine learning algorithms were used to screen and validate key hub genes. Experimental validation was performed in a doxorubicin-induced DCM mouse model using Western blotting, quantitative PCR, and immunohistochemistry. Drug-gene interaction analysis was conducted using the Comparative Toxicogenomics Database (CTD). KEY FINDINGS: Four hub genes-BACH2, NXPH4, CD1E, and LIF-were identified as central regulators linking sodium overload to DCM pathogenesis. A diagnostic model incorporating these genes showed robust discrimination between DCM patients and healthy controls across multiple datasets. Furthermore, abrine was identified through CTD analysis as a potential therapeutic candidate capable of simultaneously targeting all four hub genes. SIGNIFICANCE: This study uncovers a novel mechanistic link between sodium overload-induced regulated necrosis and DCM progression. The findings provide new molecular insights into cardiomyocyte death and inflammation in DCM and propose candidate biomarkers and drug targets for precision therapy.