ArticleJournal of inflammation research2024
Consensus Clustering Analysis Identifies Ferroptosis-Related Patient Clusters and Predictive Signature Construction Based on Ferroptosis-Related Genes in Ischemic Cardiomyopathy.
Article in Journal of inflammation research, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.
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Who cites it
4 citing papers in PubMed.
- Molecular mechanism by which perfluorooctane sulfonate regulates the initiation and progression of prostate cancer via the ENTPD5-adenine axis.Naunyn-Schmiedeberg's archives of pharmacology · 2026Article
- Targeting the ARRDC3-DRP1 Axis via hUMSC-Derived Exosomal CRYAB for Neuroprotection in Cerebral Ischemia/Reperfusion Injury.Advanced healthcare materials · 2026Article
- Exploring the common ferroptosis-related genes and molecular mechanisms in periodontitis and systemic sclerosis via integrated bioinformatics and experimental analysis.Frontiers in cell and developmental biology · 2026Article
- Profiling Shared Cytotoxic Immune Signatures in SLE-Associated Coronary Injury Through Transcriptomics and Machine Learning.ImmunoTargets and therapy · 2025Article
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Authors and funding
9 authors.
Funding
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Abstract
Background: 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.
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