Identification of immune-related genes shared between acute myocardial infarction and metabolic syndrome and construction of a diagnostic signature for acute myocardial infarction.

Figures

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  Figure 1.. The study flowchart.

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  Figure 2.. Identification of differentially expressed genes and key module genes in AMI. (a) The volcano shows total DEGs, of which the red triangles indicate significantly up-regulated genes and the green triangles indicate significantly down-regulated genes. (b) The heatmap depicts the top 50 DEGs screened out from AMI dataset. The red and blue represent up-regulated and down-regulated, respectively. (c and d) The corresponding scale-free topological model and mean connectivity values at different soft threshold powers. Here we selected β=22 as the soft threshold. (e) Cluster dendrogram of gene co-expression modules. (f) Eigengene adjacency heatmap between modules. (g) Correlations between different modules and AMI. Correlation coefficient and P value are shown at top and bottom, respectively. The red module is significantly associated with AMI. (h) Scatterplot of module membership vs. gene significance in the red module.

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  Figure 3.. Function enrichment analysis of intersection genes in AMI. (a-b) Venn diagram and KEGG analysis of 285 genes identified from the intersection of DEGs and the red module. (c–e) GO analysis of the 285 genes, including BP, CC and MF.

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  Figure 4.. Differential analysis between MetS and control samples. (a) The red and green triangles indicate DEGs with up-regulated and down-regulated, respectively. (b) Heatmap of expression levels of DEGs in MetS.

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  Figure 5.. GO and KEGG analysis and PPI network analysis. (a) The 40 common genes are screened out from intersection genes in AMI and MetS. (b) The 30 genes interacted with each other were identified through PPI network. (c) The 30 hub genes that are key nodes in the protein-protein interaction network, which play critical roles in the shared pathophysiology of AMI and MetS. (d) The column depicts the number of nodes about the 30 genes. (e-f) KEGG and GO analysis of the 40 common genes.

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  Figure 6.. Identification of candidate diagnostic hub genes for AMI with MetS via machine learning. (a and b) The Lasso analysis screens out the most 7 genes appropriate for AMI with MetS diagnosis. (c) The 8 candidate hub genes are identified depending on the significance score via the RF algorithm. (d) Venn diagram indicates that the 6 hub genes are identified from the intersection genes of LASSO and RF algorithms.

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  Figure 7.. The nomogram maps the normalized expression levels of each hub gene to a corresponding point score, with the total cumulative score (Overall point) correlated with the predicted probability of AMI (Positive Risk). The green dashed line marks the clinical risk threshold of 0.5, stratifying patients into high-risk (predicted probability ≥ 0.5) and low-risk (predicted probability < 0.5) groups.