Diagnostic and predictive values of ferroptosis-related genes in heart failure

Abstract

Background: Heart failure is a complex clinical syndrome, and there is growing evidence that ferroptosis is related to heart failure. This study sought to identify a new diagnostic model for ferroptosis-related genes in heart failure patients and analyze the signature genes associated with ferroptosis in heart failure. Methods: The ferroptosis-related genes were found on the FerrDbwebsite, and the heart failure microarray datasets (GSE5406, GSE57338, GSE1145) were screened from the GEO database. The "limma" package in R software was then used to analyze the ferroptosis-related differentially expressed genes (DEGs), and functional enrichment analysis was carried out for ferroptosis-related DEGs. The differentially expressed ferroptosis-related genes were then screened using LASSO regression and SVM-RFE algorithms. The intersection was then used to get the signature genes. The signature genes served as the foundation for the diagnostic model. The diagnostic model was created using a nomogram and receiver operating characteristic curve (ROC), and the model's precision was assessed. The expression of the signature genes' signaling pathways was examined using GSEA. The CIBERSORT algorithm was then used to analyze immune cell infiltration and correlation analysis in the immune systems of heart failure patients. Finally, the testing set was used to evaluate the diagnostic and predictive value of signature genes in heart failure. Results: The training set (GSE5406) was used to screen 127 ferroptosis-related differentially expressed genes, including 44 up-regulated and 83 down-regulated genes. Ferroptosis was significantly enriched for genes that were differentially expressed according to KEGG analysis, and oxidative stress was significantly enriched in genes according to GO-BP analysis. A diagnostic model and nomogram were successfully constructed based on the five differential genes with an area under the curve (AUC):0.952 (95% CI: 0.894-0.993), using the diagnostic model to differentiate between the normal control group and the heart failure group. Five ferroptosis-related differential genes (BECN1, SLC39A14, QSOX1, DAZAP1, TMSB4X) were screened and identified. Additionally, CD4-naive T cells were discovered to be related to heart failure patients. Finally, the diagnostic performance in the testing set (GSE57338, GSE1145) was confirmed, further demonstrating the accuracy and reliability of the study's findings. Conclusion: A novel diagnostic model with significant value for heart failure was successfully established after five ferroptosis-related genes were screened and identified. Additionally, it might be beneficial for treating patients with heart failure and aid in understanding the part ferroptosis plays in the pathogenesis of the condition.