Comprehensive analysis of disulfidptosis-related genes and the immune microenvironment in heart failure

Figures

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  FIGURE 1. Expression landscape of DRGs in HF. (A, B) Boxplot of DRG expression levels in healthy samples and HF samples in both the GSE141910 and GSE57338 datasets. (C, D) Western blot analysis was performed to measure SLC7A11, SLC3A2, RPN1, and NCKAP1 expression levels in rats (n = 3). (E–H) Western blot analysis was performed to measure ANP, BNP, β-MHC, α-SMA, Collagen I, and Collagen III expression levels in rats (n = 3). (I) Correlations among the expression of 4 DRGs in HF samples. (J) Boxplots illustrating the differences in immune infiltration between HF and normal samples. (K) Correlation analysis of 4 differentially expressed DRGs and infiltrated immune cells. Western blotting data are shown as the mean ± SEM. Significance: *p < 0.05.

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  FIGURE 2. Construction and verification of a nomogram for HF. (A) Construction of a nomogram based on DRGs for prognostic signature. (B, C) Construction of the calibration curve for assessing the predictive efficiency of the nomogram in both the GSE141910 and GSE57338 datasets. (D, E) Decision curve analysis of risk prediction of the nomogram for HF in both the GSE141910 and GSE57338 datasets. (F, G) ROC curve validation of risk prediction of the nomogram for HF in both the GSE141910 and GSE57338 datasets. (H, I) The risk distribution between healthy and HF samples, where HF samples have a much higher risk score than healthy samples in both the GSE141910 and GSE57338 datasets.

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  FIGURE 3. Consensus clustering analysis of DRGs. (A) Consensus clustering matrix when k = 2. (B) Consensus clustering cumulative distribution function (CDF) for k = 2–10. (C) Relative change in area under CDF curve for k = 2–10. (D) Tracking plot for validation of the clustering results. (E) PCA analysis showing significant differences between the two subtypes. (F) The heatmap illustrates the association of different clinical characters with the 2 subtypes.

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  FIGURE 4. Diversity of immune characteristics among distinct disulfidptosis-related patterns. (A) The abundance differences of each immune infiltrating immunocyte in 2 disulfidptosis-related patterns. (B) The boxplot of 28 infiltrated immune cell types was calculated by GSEA. (C, D) The activity differences of each immune checkpoint in 2 disulfidptosis-related patterns. (E) The expression differences of each HLA gene in two disulfidptosis-related patterns.

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  FIGURE 5. Enrichment analysis. (A, B) GO/KEGG functional enrichment analysis by Metascape. (C, D) GSEA functional and pathway enrichment analysis. (E) GSVA functional enrichment analysis.

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  FIGURE 6. Single-cell sequencing data analysis. (A) Dimensionality reduction identified 15 principal components using the UMAP algorithm, and classification of 26 cell clusters. (B) Annotation of 26 cell clusters in HF by singleR. (C, D) Analysis of the number of interactions (C) and interaction strength (D) among different cell types in HF samples. (E–H) The expression of 4 DRGs in different types of cells. (I) The relationship between the expression level of DRGs and cell populations. (J) The disulfidptosis enrichment score (activity) in each cell. (K) The distribution of disulfidptosis score in different cell types.