A Novel Disulfidptosis‐Related Diagnostic Gene Signature and Differential Expression Validation in Ischaemic Cardiomyopathy

Figures

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  FIGURE 1. The study design.

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  FIGURE 2. DiGs and immune infiltration analysis in IC. (A and B) Comparison of the dataset before and after removing batch effects. (C) The spatial distribution of CNV in DiGs across 23 chromosomes. (D and E) Differential expressions of 17 DiGs. (F) The relative abundance of immune cells between IC and Control. (G) Difference in immune infiltration. (H) The correlation analysis between 17 DiGs and immune cells. (*p ≤ 0.05, **p ≤ 0.01, ***p ≤ 0.001.)

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  FIGURE 3. Identification of DiG molecular clusters in IC. (A) Consensus clustering matrix when k = 2. (B‐D) Representative cumulative distribution function (CDF) curves (B), CDF delta area curves (C) and the score of consensus clustering (D). (E) T‐SNE visualises the distribution of two clusters. (F) Boxplots showed the expression of 17 DiGs between two disulfidptosis clusters. (G) The expression patterns of 17 DiGs were presented in the heatmap. (*p ≤ 0.05, **p ≤ 0.01, ***p ≤ 0.001.)

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  FIGURE 4. Construction and evaluation of SVM, RF, GLM and XGB machine models. (A) Cumulative residual distribution of each machine‐learning model. (B) The residuals of each machine‐learning model. (C) The important features in SVM, RF, GLM and XGB machine models. (D) ROC analysis of four machine‐learning models based on fivefold cross‐validation in the testing cohort.

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  FIGURE 5. Validation of the five‐gene–based SVW model. (A) Construction of a nomogram for predicting the risk of IC clusters based on the five‐gene–based SVW model. (B, C) Construction of calibration curve (B) and DCA (C) for assessing the predictive efficiency of the nomogram model. (D, E) ROC analysis of the five‐gene–based SVW model based on fivefold cross‐validation in GSE48166. (F, G) ROC analysis of the five‐gene–based SVW model based on fivefold cross‐validation in GSE57345.

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  FIGURE 6. lncRNA–miRNA–mRNA regulatory network.

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  FIGURE 7. Single‐cell gene expression analysis in individuals with IC and normal controls. (A) t‐SNE dimensionality reduction plot of single cells from normal and IC samples. (B) t‐SNE dimensionality reduction plot of single cells from the normal and IC groups. (C) t‐SNE dimensionality reduction plot of eight cell types. (D) Heatmap displaying the marker genes for each cell type. (E) Expression density plots of five core genes (MYH9, NUBPL, MYL6, MYH10 and NCKAP1) across different cell types.

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  FIGURE 8. Male mouse data for myocardial injury in the IC model. (A) Changes in body weight between the Control and IC groups of mice (n = 8). (B) Changes in serum LDH levels between the control and IC groups (n = 8). (C) Representative haematoxylin and eosin (HE) staining of myocardial tissue showing histological changes in the control and IC groups. Scale bar = 100 μm. (D) Masson's trichrome staining depicting the myocardial infarction area. Scale bar = 500 μm. (E) Echocardiography results showing heart rate, left ventricular ejection fraction (LVEF), left ventricular fractional shortening (LVFS), left ventricular end‐diastolic diameter (LVEDD) and left ventricular end‐systolic diameter (LVESD) (n = 8). (E) Relative mRNA expression levels of NUBPL, NCKAP1, MYL6, MYH9 and MYH10 in male mice myocardial tissue (n = 6). Data are presented as mean ± SEM. (*p < 0.05, **p < 0.01.)

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  FIGURE 9. (A) IHC expression of MYH9, NUBPL, MYL6, MYH10 and NCKAP1 in myocardial cells of male mice in the control and IC groups (20× and 40×). (B) Western blot bands showing protein expression of MYH9, NUBPL, MYL6, MYH10 and NCKAP1 in male mouse myocardial tissue. (mean ± SD, n = 4). (*p ≤ 0.05, **p ≤ 0.01.)