Fig 1.
Differentially expressed genes in different types of AMI.
(A)Volcano plot for differential expression analysis of GSE66360. (B)Heat map of GSE66360 differential expression analysis.
Fig 2.
WGCNA and significant module recognition.
(A)Dendrogram of all genes in the GSE66360 dataset clustered based on the topological overlap matrix(1-TOM). (B)Module-trait heat map of the correlation between clustered gene modules and AMI in the GSE66360 dataset. Each module contains the corresponding correlation coefficient and p-value. (C)In the GSE66360 data set, the yellow scatter plot of the module has the most significant positive correlation with AMI.
Fig 3.
Screening and validation of candidate hub genes.
(A)Venn diagram showing 250 overlapping intersection genes. (B)Pathway analysis of KEGG candidate hub genes. (C) GO enrichment analysis of candidate hub genes.
Fig 4.
(A)PPI network of intersection hub genes. (B)Protein interaction network core gene map (the darker the color, the higher the score).
Fig 5.
(A)Hub gene nomogram model. (B)Calibration curve assessing the predictive ability of the nomogram. (C) ROC curve to evaluate the diagnostic efficacy of the nomogram model and each hub gene.
Fig 6.
Immune relevance of TNF in AMI.
(A) Relative distribution of 22 immune cell types in all AMI samples. (B) Difference in immune cell infiltration between AMI and controls. (C)Correlation between TNF and infiltration immune cells in AMI.
Fig 7.
Results of mendelian randomization studies.
(A)Scatter plot showing the causal effect of TNF on AMI risk. (B)Forest plot showing the causal effect of each SNP on AMI risk. (C) Funnel plot showing overall heterogeneity in MR estimates of the effect of TNF on AMI. (D)Leave-one-out plot visualizing the causal effect of’ TNF on AMI risk when one SNP is omitted.