Potential diagnostic biomarkers: 6 cuproptosis- and ferroptosis-related genes linking immune infiltration in acute myocardial infarction

Abstract

The current diagnostic biomarkers of acute myocardial infarction (AMI), troponins, lack specificity and exist as false positives in other non-cardiac diseases. Previous studies revealed that cuproptosis, ferroptosis, and immune infiltration are all involved in the development of AMI. We hypothesize that combining the analysis of cuproptosis, ferroptosis, and immune infiltration in AMI will help identify more precise diagnostic biomarkers. The results showed that a total of 19 cuproptosis- and ferroptosis-related genes (CFRGs) were differentially expressed between the healthy and AMI groups. Functional enrichment analysis showed that the differential CFRGs were mostly enriched in biological processes related to oxidative stress and the inflammatory response. The immune infiltration status analyzed by ssGSEA found elevated levels of macrophages, neutrophils, and CCR in AMI. Then, we screened 6 immune-related CFRGs (CXCL2, DDIT3, DUSP1, CDKN1A, TLR4, STAT3) to construct a nomogram for predicting AMI and validated it in the GSE109048 dataset. Moreover, we also identified 5 pivotal miRNAs and 10 candidate drugs that target the 6 feature genes. Finally, RT-qPCR analysis verified that all 6 feature genes were upregulated in both animals and patients. In conclusion, our study reveals the significance of immune-related CFRGs in AMI and provides new insights for AMI diagnosis and treatment.

Fig. 1. GO/KEGG analysis and the PPI network of differential CFRGs.

A Volcano plot of the differential CFRGs from the combined datasets. B Heatmap of the differential CFRGs from the combined datasets in AMI patients and healthy controls. C GO analysis of differential CFRGs. D KEGG analysis of differential CFRGs. E PPI network of the differential CFRGs. F Identification of 10 hub CFRGs.

Fig. 2. Differentially infiltrated immune cells and functions in AMI patients and healthy controls.

A Heatmap of differential immune cells and functions. B Correlation matrix of 16 immune cells. C The ssGSEA scores of 16 immune cells. D Correlation matrix of 13 immune functions. E The ssGSEA scores of 13 immune functions. CCR, cytokine-cytokine receptor. * P < 0.05, ** P < 0.01, *** P < 0.001.

Fig. 3. Correlation among hub CFRGs with immune cells and functions.

A Heatmap of correlation among 10 hub CFRGs with immune cells and functions. Scatter diagram of the correlation between CCR and CXCL2 (B), DDIT3 (C), DUSP1 (D), CDKN1A (E), TLR4 (F), STAT3 (G).

Fig. 4. Diagnostic model construction.

A Construction of a nomogram model with 6 feature genes. B, C ROC curve for evaluating and validating the diagnostic model’s performance.

Fig. 5. MiRNA-mRNA and drug-gene network construction.

A 5 Pivotal miRNAs targeting 6 feature genes. B–G 10 candidate drug molecules targeting 6 feature genes.

Fig. 6. AMI mouse model validation.

A, B Representative ECG images of AMI (n = 3) and sham animals (n = 3) were obtained after the cardiac surgery. The arrow noted that ST segment was elevated in AMI animals. C Representative echocardiography images of two groups at 24 h after the cardiac surgery. D, E Comparison of LVFS and LVEF in the two groups. ECG: electrocardiogram, LVFS: Left ventricular fractional shortening, LVEF: Left ventricular ejection fraction.

Fig. 7. Genes expression in AMI and sham mouse cardiac tissues and blood evaluated by RT-qPCR at 24 h post-MI.

mRNA levels of (A) STAT3, (B) TLR4, (C) CDKN1A, (D) DUSP1, (E) DDIT3, (F) CXCL2 in cardiac tissues. mRNA levels of (G) STAT3, (H) TLR4, (I) CDKN1A, (J) DUSP1, (K) DDIT3, (L) CXCL2 in blood. Significant differences between groups were determined using Student’s t test, data shown as mean ± SD, (*p < 0.05, **p < 0.01, ***p < 0.001, ****p < 0.0001).

Fig. 8. Genes expression in AMI patients and controls blood evaluated by RT-qPCR within 24 h post-MI.

A STAT3, (B) TLR4, (C) CDKN1A, (D) DUSP1, (E) DDIT3, (F) CXCL2. Significant differences between groups were determined using Mann–Whitney test, data shown as median±95%CI, (*p < 0.05, **p < 0.01, ***p < 0.001, ****p < 0.0001).