Xiaohong Bo,1,2,* Jili Fan,1,2,* Shuai Xu,3,4 Yiyao Zeng,3,4 Anchen Xu,3,4 Xiangyu Wang,3,4 Hongju Wang,5 Pinfang Kang,5 Yangjun Fu,6 Huimin Fan,3,4,7 Xin Tan,3,4 Yafeng Zhou3,4 

1Department of Cardiovascular Disease, Taihe County People’s Hospital, Fuyang, 236600, People’s Republic of China; 2Department of Cardiovascular Disease, Taihe Hospital of Wannan Medical College, Fuyang, 236600, People’s Republic of China; 3Department of Cardiology, The Fourth Affiliated Hospital of Soochow University, Suzhou Dushu Lake Hospital, Medical Center of Soochow University, Suzhou, 215000, People’s Republic of China; 4Institute for Hypertension, Soochow University, Suzhou, 215000, People’s Republic of China; 5Department of Cardiovascular Disease, the First Affiliated Hospital of Bengbu Medical University, Bengbu, 233000, People’s Republic of China; 6Department of Neurology, The Third People’s Hospital of Hefei, Hefei City, Anhui Province, 230041, People’s Republic of China; 7Center of Translational Medicine and Clinical Laboratory, The Fourth Affiliated Hospital to Soochow University, Suzhou Dushu Lake Hospital, Suzhou, 215028, People’s Republic of China

*These authors contributed equally to this work

Correspondence: Yafeng Zhou; Xin Tan, Email Dryafengzhou@163.com; txth1106@163.com

Background: Dilated cardiomyopathy (DCM) is a severe cardiac condition characterized by ventricular dilation and impaired systolic function, leading to heart failure and sudden death. Current treatments have limited efficacy in reversing disease progression. Recent research suggests that disulfidptosis, a novel cell death mechanism, may play a role in DCM pathogenesis, though its specific involvement remains unclear.
Methods: This study utilized multiple GEO datasets to analyze the expression of 24 disulfidptosis-related genes (DiGs) in DCM patients and healthy controls. Differential expression analysis and consensus clustering were used to classify DCM patients into subgroups based on DiGs expression profiles. A predictive model was constructed using four machine learning methods, and its performance was validated with independent datasets. A ceRNA regulatory network was established, and in vivo experiments were conducted using a DCM mouse model to validate key findings.
Results: Nineteen DiGs were differentially expressed between DCM and controls. Consensus clustering divided DCM patients into two subtypes with distinct immune profiles. The support vector machine (SVM) model demonstrated superior diagnostic performance (AUC = 0.983), identifying ACTN4, MYH10, TLN1, DSTN, and NCKAP1 as core predictors. In vivo validation confirmed their expression changes in myocardial tissue. Single-cell RNA-seq further revealed increased fibroblast and macrophage infiltration in DCM, along with enrichment of fibrotic and immune pathways. Quercetin and resveratrol were predicted as potential drugs targeting these core genes.
Conclusion: This study demonstrates that ACTN4, MYH10, TLN1, DSTN, and NCKAP1 play significant roles in the pathogenesis of DCM, influencing immune cell infiltration and metabolic homeostasis. Quercetin and resveratrol were identified as potential therapeutic agents, offering new directions for DCM treatment.

Keywords: dilated cardiomyopathy, diagnostic model, disulfidptosis-related genes, immune cell infiltration