Elucidating ferroptosis mechanisms in heart failure through transcriptomics, single-cell sequencing, and experimental validation

Abstract

Background

The mechanisms underlying ferroptosis in heart failure (HF) remain incompletely understood.

Methods

This study analyzed the heart failure dataset from the Gene Expression Omnibus to identify differentially expressed ferroptosis-related genes (DFRGs). Key DFRGs were selected using LASSO regression and SVM-RFE machine learning techniques. Their diagnostic accuracy was evaluated via ROC curve analysis. Single-cell sequencing data, heart failure cell, and mouse models were utilized to validate these key DFRGs. Additionally, potential non-coding RNAs targeting these genes were predicted, and analyses for gene set enrichment, immune cell infiltration, and drug targeting were conducted.

Results

A total of 127 DFRGs were identified, with 83 downregulated and 44 upregulated compared to controls. Seven key DFRGs (PTGS2, BECN1, SLC39A14, QSOX1, MLST8, TMSB4X, KDM4A) were identified, showing high diagnostic accuracy (AUC 0.988) in the GSE5406 dataset. GO and KEGG analyses linked these genes to ferroptosis, FoxO signaling, and autophagy pathways. A ceRNA network identified 217 miRNAs and 243 lncRNAs potentially targeting these genes, and 64 drugs were predicted as potential targets. Single-cell sequencing and in vitro experiments revealed differential expression of SLC39A14 and QSOX1, which was further confirmed in vivo.

Conclusion

This study provides novel insights into the role of ferroptosis in heart failure by identifying and validating DFRGs that exhibit differential expression across various cell types. The differential expression patterns of these genes, particularly SLC39A14 and QSOX1, indicate their potential involvement in the pathophysiological mechanisms contributing to HF. These findings offer new insights for the development of targeted therapies for HF.