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Consensus Clustering Analysis Identifies Ferroptosis-Related Patient Clusters and Predictive Signature Construction
Shuai Guo1, Zhaoting Gong1, Xiaona Sun2
1Center for Coronary Artery Disease, Division of Cardiology, Beijing Anzhen Hospital, Capital Medical University, Beijing, People's Republic of China.
Insights
Ferroptosis, a cell death process, plays a key role in ischemic cardiomyopathy (ICM). Researchers identified ferroptosis-related genes to create a predictive signature for diagnosing and treating ICM patients.
Area of Science:
- Biomedical research
- Cardiovascular science
- Molecular biology
Background:
- Ischemic cardiomyopathy (ICM) is a major global health concern.
- The specific role of ferroptosis in ICM pathogenesis is not well understood.
Purpose of the Study:
- To investigate the role of ferroptosis in ICM.
- To identify ferroptosis-related genes and develop a predictive signature for ICM.
Main Methods:
- Differential gene expression analysis of ferroptosis-related genes (DEFRGs) using the GSE57338 dataset.
- Consensus clustering to identify ferroptosis-associated clusters in ICM.
- Development and validation of an 11-gene predictive signature using LASSO regression and the GSE5406 dataset.
- Validation of key genes in a rat ICM model using qRT-PCR.
Main Results:
- Identified 15 DEFRGs, distinguishing two ICM patient clusters with different ferroptosis profiles.
- Developed a robust 11-gene predictive signature for ICM.
- Observed distinct immune cell infiltration and pathway enrichment patterns between high-risk and low-risk patient clusters.
- Validated gene expression changes in a rat ICM model.
Conclusions:
- Two distinct ferroptosis-related clusters were identified in ICM patients.
- A novel ferroptosis-based predictive signature for ICM was developed.
- These findings offer new perspectives for ICM diagnosis and therapeutic strategies.
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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