An exploratory study of high-throughput transcriptomic analysis reveals novel mRNA biomarkers for acute myocardial infarction using integrated methods

Figures

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  Fig. 1. Presented the schematic diagram of the study.

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  Fig. 2. Transcriptomic analysis was conducted on peripheral blood samples from AMI and treatment groups. (A) Depicted the principal component analysis (PCA) plot based on transcriptomics data. (B) The sPLS-DA demonstrated moderate clustering between the three groups. (C) The volcano plots displayed differentially expressed genes (DEGs) between the AMI group, the post-treatment group and the healthy control group. The black line represented the cutoff line with indicated significance criteria. Points with absolute log fold-change ≥ 2 and P < 0.05 were shown in blue, points with absolute log fold-change ≤ -2 and P ≤ 0.05 were in red, and the rest were in green: (C) AMI versus healthy controls, (D) Post-treatment of AMI versus AMI, and (E) Post-treatment of AMI versus healthy controls. AMI Acute Myocardial Infarction, FC fold change, Treat Post-treatment of AMI.

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  Fig. 3. GO functional enrichment and pathway enrichment analyses were conducted on DEGs. (A–C) The top 5 GO terms associated with the Molecular Functions (MF), Biological Processes (BP), and Cellular Compartments (CC) in both upregulated and downregulated DEGs were identified. (A) In the comparison between AMI and healthy controls, (B) Post-treatment of AMI versus AMI, and (C) Post-treatment of AMI versus healthy controls. (D–F) KEGG enrichment analyses were performed on the upregulated and downregulated DEGs. The top 10 were shown here. (D) For the comparison between AMI and healthy controls, (E) Post-treatment versus AMI, and (F) Post-treatment of AMI versus healthy controls.

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  Fig. 4. Sparse partial least-squares discriminant analysis. (A, B) Selected features were shown in a pyramid bar plot. The loading plot represented the top 25 mRNAs contributing to group separation (Left). The area under the ROC curve (AUC) values, compared to the grouping level, were shown for the three-group classification (Right). (A) The bar plot showed the contribution value of each mRNA. (B) sPLS-DA contributions to component 2 were depicted. The color in the bar plot represented the highest mRNA expression level in the corresponding group. (C) Displayed the background prediction plot for the sample’s prediction results.

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  Fig. 5. The results of weighted gene co-expression network analysis (WGCNA). (A) Clustering dendrogram of 81 samples. (B) Analysis of the scale-free index for various soft-threshold powers (left). Analysis of the mean connectivity for various soft-threshold powers(right). (C) WGCNA was performed to identify 28 modules by unsupervised clustering. (D) Heatmap of the correlation between the module eigengenes and clinical traits of AMI, healthy control, and Post-treatment of AMI. The tan module was identified as the positively correlated module with chest pain duration (r = 0.73, P < 0.01). (E) The gene significance and module membership of the genes in the tan module exhibited a positive correlation. (F) KEGG enrichment analyses in tan module.

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  Fig. 6. Through various data analysis methods, significant gene sets were obtained in AMI, post-treatment of AMI, and healthy control group. (A) The UpSet plot illustrated the intersection and union relationships among DEGs (top 200), sPLS-DA component 1, component 2, tan module of WGCNA, and random forest (RF) top 200 genes. The ribbons between the dots represent the intersections between sets. Genes with an intersection count of two or more groups were selected for further analysis. (B) The extracted features were reduced via the LASSO regression. (C) LASSO coefficients of the variables. (D) The expression level of six mRNA in the AMI, Post-treatment of AMI and healthy controls. (E) Validation of 6 mRNA expression by fluorescence quantitative PCR.