Pyroptosis Patterns Are Involved in Immune Microenvironment Regulation of Dilated Cardiomyopathy

Figures

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  Figure 1. Expression landscape of PRGs in DCM. (a) The protein-protein interactions among PRGs. (b) Interrelationships of the expression of PRGs for all samples (left) and DCM samples (right). The two most interrelated PRGs shown in the two scatter plots: GSDMD and GPX4. (c) The box plot shows the expression of 19 PRGs in DCM compared to healthy samples. (d) The heat map shows the expression status of 19 PRGs in DCM compared to healthy samples. (e) The volcano plot visually shows the expression of PRGs between healthy and DCM samples.

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  Figure 2. PRGs can be used to classify normal and DCM samples. (a) Univariate logistic regression was used to study the correlation between PRGs and DCM (P < 0.05) and uncovered 21 DCM-related PRGs. (b) LASSO coefficient distribution of 21 DCM-related RGs. (c) In LASSO regression, 10-fold cross-validation was used to fine-tune parameter selection. λ is the adjustment parameter, and the partial likelihood deviance is plotted according to log (λ). (d) Through multivariate logistic regression, a distinguishing feature with 12 PRGs was developed, and the risk scores of DCM and healthy samples were estimated. (e) The risk profile between DCM and healthy samples illustrates that the risk score of DCM was higher than that of healthy samples. (f) ROC curves were used to analyse the ability of 12 PRGs to distinguish between healthy and DCM samples, and the AUC value was used to evaluate the distinction ability.

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  Figure 3. Association of PRGs with immune cells and immune response pathways. (a) The dot plot shows the correlation between each infiltrating cell and each hub PRG in the dysregulated immune microenvironment. The most positive interrelated immune cell-hub PRG pair was GSDMD-pDCs, and the violin plot on the right shows the expression or fraction status. The most negatively correlated immune cell-hub PRG pair was GSDMD-Tregs, and the violin plot on the left shows the expression or fraction status. (b) The dot plot shows the association between each gene set of the immune dysregulation response and the 12 hub PRGs. The most positively correlated pair was CASP1-parainflammation, and the violin plot in the right shows the expression or activity. The most negative interrelated pair was CASP9-type_II_IFN_response, and the violin plot on the right shows the expression or activity.

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  Figure 4. Identification two different subtypes of pyroptosis patterns in DCM. (a) A consensus clustering cumulative distribution function (CDF) with k = 2 − 7 is shown. (b) The relative change in the area under the CDF curve for k = 2 − 7 is shown. (c) Heat map of the matrix of cooccurrence proportions for DCM samples. (d) Principal component analysis (PCA) of the transcriptome profiles of the two pyroptosis patterns, indicating that there were significant differences in the transcriptome between the two pyroptosis patterns. (e) Comparison of age and sex between the two pyroptosis patterns. The correlation of different clinical features between the 2 patterns is shown by the heat map. (f) Box plot of the expression status of PRGs in two pyroptosis patterns. (g) Heat map of the expression status of PRGs in the two pyroptosis patterns.

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  Figure 5. Differences in immune microenvironmental features in different pyroptosis patterns. (a) The abundance difference of infiltrating immunocytes in each immune microenvironment in pyroptosis patterns. (b) Activity differences in each immune response gene set in pyroptosis patterns. (c) Expression differences in each HLA gene in the pyroptosis patterns.

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  Figure 6. Diversity of potential biological function characteristics between the two pyroptosis patterns. (a) Differences in KEGG pathway enrichment scores between the two pyroptosis patterns. (b) Differences in GO pathway enrichment scores between the two pyroptosis patterns.

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  Figure 7. Identification and functional analysis of genes involved in the pyroptosis phenotype in DCM. (a) 2142 genes were related to the pyroptosis phenotype. (b) GO-BP functional enrichment analysis showed the biological features of genes involved in the pyroptosis phenotype. (c) GO enrichment analysis of genes related to the pyroptosis phenotype revealed the correlation between PRGs and immune mechanisms. (d) Sample clustering was performed on the strength of the expression information of all samples. All differentially expressed genes with P < 0.05 were used for WGCNA. (e) Analysis of the scale-free ft index and analysis of the mean connectivity for various soft-thresholding powers. (f) Gene tree diagram. The coloured line below the tree diagram shows the modules determined using dynamic tree cutting. (g) Heat map of the relevance between the characteristic genes of the module and the pyrolysis pattern. (h) Gene significance (GS) scatter plot of pyrolysis pattern A and module member (MM) in the magenta module.

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  Figure 8. Validation of the expression levels of NLRP1, TNFα, CASP1, CASP9, and PRKACA in healthy and Dox-induced DCM mouse myocardial tissues. (a) Representative echocardiographic (echo), HE, and Masson's trichrome staining images. (b) Cardiac function index (n = 6). (c) Expression levels of NLRP1, TNFα,CASP1, CASP9, and PRKACA were quantified using qRT–PCR analysis in myocardial tissue. ∗P < 0.05, ∗∗P < 0.01, ∗∗∗P < 0.001, and ∗∗∗∗P < 0.0001.