ISLR as a Cuproptosis-Related Predictor and Therapeutic Target in Heart Failure: A Multi-Omics and Bioinformatics Approach

Figures

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  Figure 1. The flowchart of this study. * p < 0.05, ** p < 0.01, *** p < 0.001.

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  Figure 2. The CRGs expressional landscape in HF. (A) The boxplot showing the differentially expressed CRGs between healthy control and HF groups. (B) The heat map showing the expression characteristic of CRGs in healthy control and HF group.(C) Correlation analysis of differentially expressed CRGs; Red represents positive correlation and green represents negative correlation. (D) The PPI network of differentially expressed CRGs. (E) The predicted interactome of differentially expressed CRGs and their biological function. (for all figures: * represents p < 0.05, ** represents p < 0.01, and *** represents p < 0.001).

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  Figure 3. Identification and enrichment analysis of Cuproptosis subtypes. (A) Consensus clustering matrix when k = 2. (B) PCA diagram separated subtype1 (blue) and subtype 2 samples (red). (C) Barplot showing percent of immune cells in subtype 1 and subtype samples. (D) Box plot showing the differences of infiltrated immune cells in subtype 1 and subtype 2 samples. *p < 0.05, **p < 0.01. (E) Barplot showing GO enrichment results of subtype 1 and subtype 2. (F) Barplot showing KEGG enrichment results of subtype 1 and subtype 2.

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  Figure 4. The WGCNA for HF and Cuproptosis subtypes. (A) The selection of the soft-thresholding power β for disease WGCNA. (B) The heatmap showing module-disease correlations. (C) The blue module had the highest positive correlation with HF. (D) The selection of the soft-thresholding power β for cluster WGCNA. (E) The heatmap showing module-cluster correlations. (F) The turquoise module had the highest positive correlation with cluster.

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  Figure 5. Machine learning models for predictive genes selection. (A) The intersection of hub genes in turquoise module from cluster WGCNA and hub genes in blue module from disease WGCNA. (B) Comprehensive estimate the performance of the four machine learning models including Recall, Precision, F1, Accuracy and AUC. (C) The AUC for four machine learning models. (D) The residual boxplot for four machine learning models. (E) The top 6 features for four machine learning models.

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  Figure 6. The validation of 6 characteristic features. (A) ROC for 6 characteristic features in training dataset. (B) ROC for 6 characteristic features in test dataset. (C) ROC for 6 characteristic features in GSE120895. (D) ROC for 6 characteristic features in GSE203160. (E) Boxplot showing the expression level of 6 characteristic features in GSE120895. (F) Boxplot showing the expression level of 6 characteristic features in GSE203160. * p < 0.05, ** p < 0.01.

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  Figure 7. The diagnostic value of characteristic genes for HF. (A) The nomogram for the model based on six characteristic genes. (B) The calibration curve of model based on six characteristic genes. (C) The decision curve analysis for the model based on six characteristic genes.

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  Figure 8. Molecular docking and in vivo experiment. (A) The interaction between ISLR and pirinixic acid (B) Representative M-mode echocardiographic images of the left ventricle from sham or TAC mice received PBS or Pirinixic acid (n=8 for each group). (C) Representative cardiac Masson trichrome. Scale bar, 50 μm. (D) LV ejection fraction (LVEF) was assessed by echocardiography. (E) Fractional shortening (FS) was assessed by echocardiography. (F) Heart weight-to-tibia length ratio (HW/TL) of PBS and Pirinixic Acid mice 4 weeks after TAC or sham surgery (n=8 for each group). (G) Quantification of the LV collagen volume in each group.