Unveiling shared therapeutic targets and pathological pathways between coronary artery disease and major depressive disorder through bioinformatics analysis

Figures

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  Fig. 1. The workflow chart of the study.

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  Fig. 2. Identification of DEGs. (a, b) The PCA analysis of GSE71226 and GSE76826 datasets. (c, d) Volcan plots displaying the DEGs in the GSE71226 and GSE76826 datasets. (e, f) Venn diagrams displaying the number of up- and down-regulated DEGs in the GSE71226 and GSE76826 datasets.

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  Fig. 3. Functional enrichment analysis of DEGs. (a, b) Functional enrichment analysis of DEGs in the GSE71226 dataset. (c, d) Functional Enrichment analysis of DEGs in the GSE76826 dataset. (e, f) Functional enrichment analysis of overlapping DEGs between the CAD and MDD datasets.

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  Fig. 4. Identification of hub genes. (a) Venn diagrams showing the overlapping DEGs based on Degree, Stress, DMNC, EcCentricity, and Radiality algorithms. (b) PPI network of 4 hub genes (TXN, CDC42, COX7A2, and NDUFB3) with other proteins. (c) Co-expressed gene network of 4 identified hub genes (TXN, CDC42, COX7A2, and NDUFB3).

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  Fig. 5. Validation and prognostic capability of hub genes. (a, b) Bar plots of hub genes in the GSE20680 (a) and GSE38206 (b) datasets. (c, d) ROC curve analysis of hub genes in the GSE20680 (c) and GSE38206 (d) datasets. P* < 0.05, P** < 0.01, P*** < 0.001.

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  Fig. 6. Immune infiltration analysis in the GSE71226 dataset. P* < 0.05, P** < 0.01, P**** < 0.0001.

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  Fig. 7. Immune infiltration analysis in the GSE76826 dataset. P* < 0.05, P** < 0.01, P**** < 0.0001.

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  Fig. 8. The correlation between hub genes and infiltrating immune cells in the GSE71226 dataset. Correlation of NDUFB3 (a), CDC42 (b), and TXN (c), with infiltrating immune cells in the GSE71226 dataset.

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  Fig. 9. The correlation between hub genes and infiltrating immune cells in the GSE76826 dataset. Correlation of NDUFB3 (a), TXN (b), and CDC42 (c) with infiltrating immune cells in the GSE76826 dataset.

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  Fig. 10. Evaluation of hub genes in the GSE12288 dataset using WGCNA. (a) Hierarchical clustering and heatmap of samples. (b) Histogram of connectivity distribution. (c) Gene dendrogram of hierarchical clustering (d) Heatmap of adjacency matrix between different modules. (e) Heatmap of Pearson correlation analysis. (f) Heatmap displaying the relationship between various modules and CAD functionality. (g) Scatter plot between Module Membership (MM) and Gene Significance (GS) in the blue module. (h) PPI network of hub genes with other molecules.

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  Fig. 11. Evaluation of hub genes using RT-PCR. The mRNA levels of CDC42 (a), NDUFB3 (b), and TXN (c) in myocardial tissues of MI mice compared to those of sham-operated mice. P*** < 0.001.

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  Fig. 12. Identification of candidate drugs interacted with hub genes using molecular docking analysis. (a) The top 10 potential candidate drugs were identified based on their binding energy. (b–d) The molecular docking interactions between CDC42 (b), NDUFB3 (c), TXN (d), and GS-9620.