Consensus Clustering Analysis Identifies Ferroptosis-Related Patient Clusters and Predictive Signature Construction Based on Ferroptosis-Related Genes in Ischemic Cardiomyopathy

Abstract

Shuai Guo,1,* Zhaoting Gong,1,* Xiaona Sun,2 Fei Gao,1 Xiang Li,1 Xiaolin Zu,1 Chao Qu,1 Hongliang Zhang,3 Hai Gao1 1Center for Coronary Artery Disease, Division of Cardiology, Beijing Anzhen Hospital, Capital Medical University, Beijing, People’s Republic of China; 2Department of Cardiology, Laizhou City People’s Hospital, Laizhou, People’s Republic of China; 3Department of Cardiology, Fuwai Hospital, National Center for Cardiovascular Disease, Chinese Academy of Medical Science and Peking Union Medical College, Beijing, People’s Republic of China*These authors contributed equally to this workCorrespondence: Hai Gao, Center for Coronary Artery Disease, Division of Cardiology, Beijing Anzhen Hospital, Capital Medical University, Beijing, People’s Republic of China, Email [email protected] Hongliang Zhang, Department of Cardiology, Fuwai Hospital, National Center for Cardiovascular Disease, Chinese Academy of Medical Science and Peking Union Medical College, Beijing, 100037, People’s Republic of China, Email [email protected]: Ischemic cardiomyopathy (ICM) significantly contributes to global disease burden, while the role of ferroptosis in ICM remains underexplored.Methods: We identified differentially expressed ferroptosis-related genes (DEFRGs) by analyzing the GSE57338 dataset and cross-referencing with FerrDb. Consensus clustering was then used to identify ferroptosis-associated clusters within the ICM samples. A ferroptosis-specific predictive signature was developed using the least absolute shrinkage and selection operator (LASSO) method and validated with the GSE5406 dataset. Additionally, quantitative real-time PCR (qRT-PCR) experiments were performed to validate the 11 feature genes in a rat ICM model.Results: We identified 15 DEFRGs in GSE57338, which distinguished two patient clusters with distinct ferroptosis gene expression, pathway enrichment profiles, and metabolic characteristics. All DEFRGs were upregulated in cluster 2. Potential therapeutic targets were also identified for different ICM patient clusters. The 11-gene predictive signature (TXNRD1, STEAP3, STAT3, SCL2A1, PLIN2, NQO1, NNMT, IL33, ENPP2, ARRDC3, ALOX5) showed robust predictive power in both training and validation sets. High-risk patients exhibited increased infiltration of CD8+ T cells, CD4+ naïve T cells, M0/M1 macrophages, and resting mast cells, along with significant enrichment in epithelial mesenchymal transition and interferon responses. Low-risk patients had higher infiltration of regulatory T cells and monocytes. Results of qPCR analysis confirmed the bioinformatic analysis, validating the expression of the 11 feature genes in the rat ICM model.Conclusion: We identified two ferroptosis-related clusters in ICM patients and developed a predictive signature based on ferroptosis-related genes. Our findings highlight the importance of ferroptosis in ICM and offer new insights for its diagnosis and treatment.Keywords: ischemic cardiomyopathy, ferroptosis, bioinformatics analysis, predictive signature

Keywords

• ischemic cardiomyopathy
• ferroptosis
• bioinformatics analysis
• predictive signature