Identification of anoikis-related biomarkers linking immune infiltration to acute myocardial infarction

Figures

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  Figure 1. Identification of differentially expressed genes (DEGs) and differentially expressed anoikis-related genes (DEARGs). A, Volcano plot of DEGs, where blue dots indicate down-regulated genes, gray dots indicate stable genes, and red dots indicate up-regulated genes. B, Heat map of DEGs, where each column corresponds to the expression profile of an acute myocardial infarction (AMI) sample, and each row corresponds to a gene. C, Venn diagram of DEGs and ARGs. D, Expression of 21 DEARGs in normal and AMI samples. The top 5 genes with the most significant differential expression are marked in red text (sorted in descending order by |log2FC|). The blue group represents normal samples, while the red group represents AMI samples. Data are reported as median and interquartile range. ***P<0.001, ****P<0.0001 (Kruskal-Wallis test). E, Relative positions of the 21 DEARGs on the chromosomes. F, Correlation heat map of the 21 DEARGs, where red represents positive correlation, while blue represents negative correlation.

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  Figure 2. Functional enrichment analysis of differentially expressed anoikis-related genes (DEARGs). A, Biological process of GO enrichment analysis. B, Cell components of GO enrichment analysis. C, Molecular function of GO enrichment analysis. D, KEGG pathway of DEGs. GO: Gene Ontology; KEGG: Kyoto Encyclopedia of Genes and Genomes.

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  Figure 3. Immune infiltration analysis. A, Percentage of immune cells. B, Immune cell difference between normal and acute myocardial infarction (AMI) samples. C, Correlation between 21 differentially expressed anoikis-related genes (DEARGs) and 14 differential immune cells. Data are reported as median and interquartile range. *P<0.05, **P<0.01, ***P<0.001, ****P<0.0001 (Kruskal-Wallis test). ns: non-significant.

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  Figure 4. Identification of hub feature genes by LASSO and SVM. A, Feature genes identified by LASSO. B, Random Forest sorting results of hub feature genes. C and D, 10-fold cross-validation was used to calculate AUC, and the optimal parameter was determined to be 9. LASSO: least absolute shrinkage and selection operator; SVM: support vector machine; AUC: area under the ROC curve.

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  Figure 5. Receiver operating characteristic (ROC) curves of the 9 risk feature genes in the training dataset. AUC: area under the ROC curve.

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  Figure 6. Receiver operating characteristic (ROC) curves of the 9 risk feature genes in the validation dataset. AUC: area under the ROC curve.

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  Figure 7. Consensus matrix analysis based on the expression of 9 risk feature genes in each acute myocardial infarction sample of the GSE66360 dataset. A, Consensus clustering matrix at k=2. B, Consensus index of CDF curves. C, CDF delta area curve. D, PCA of three clusters. E, Incidence of heart failure in three clusters based on the GSE59867 dataset. F, Heat map of the expression profiles of 9 risk feature genes in three clusters. G, Box plots of the expression of 9 risk feature genes in three clusters. Data are reported as median and interquartile range. **P<0.01, ***P<0.001, ****P<0.0001 (Kruskal-Wallis test). ns: non-significant. CDF: cumulative distribution function; PCA: principal component analysis.

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  Figure 8. Establishment and verification of the nomogram. A, Gene expression was evaluated in blood samples from acute myocardial infarction (AMI) patients and healthy controls by RT-qPCR. Data are reported as mean and SD. *P<0.05 (t-test). B, Nomogram for the diagnosis of AMI. C, Calibration curve of the nomogram. D, Decision curve analysis of the nomogram. E, ROC curve: AUC of 0.962. F, ROC curves assessing the prognostic accuracy of nomogram and individual genes. ROC: receiver operating characteristic. AUC: area under the ROC curve.