Integrated multi-omics analysis reveals key hub genes and mechanisms in calcific aortic stenosis

Figures

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  Figure 1. Transcriptional dysregulation in aortic calcification. Sample normalization of the GSE51472 dataset (A), DEGs in aortic sclerosis patients compared with controls (B), and gene set enrichment analysis (C); DEGs in aortic calcification patients (D), GSEA (E), PPI network (F), networks of the top two key gene modules (G), and enrichment analysis of key modules (H,I). Sample size: Control group (n = 5); AS group (n = 5).

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  Figure 2. Transcriptional dysregulation in aortic calcification. Sample normalization of the GSE12644 dataset (A), DEGs in aortic calcification patients compared with control group (B), GSEA (C), PPI network (D), and GO/KEGG enrichment analysis (E); identification of intersection genes, PPI network (G,H), and GO/KEGG enrichment analysis (I). D: Red for upregulated genes, green for downregulated genes; H: Ranked by Degree, with darker colors indicating higher ranks. Sample size: Control group (n = 10); AS group (n = 10).

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  Figure 3. He and alizarin red staining. HE and alizarin red staining of tissues from non-stenotic (A,C) and stenotic (B,D) aortic valves.

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  Figure 4. Proteomic sequencing analysis of aortic valves from non-stenotic and stenotic patients. Quality control (A), volcano plot (B) and heatmap (C) of DEGs, GSEA (D), GO (E) and KEGG analysis (F), PPI network (G), and enrichment analysis of upregulated genes (H) Sample size: Control group (n = 8); AS group (n = 8).

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  Figure 5. Identification and expression analysis of hub genes. Hub gene identification by multi-algorithms (A), enrichment analysis of hub genes (B), expression analysis of hub genes in proteomic sequencing (C), and their expression in GSE12644 and GSE51472 datasets (D,E). differences in mean values between the control group and AS group were analyzed using an independent samples t-test. Statistical significance was set at P < 0.05.

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  Figure 6. Diagnostic efficacy, transcriptional regulation, RNA modification, and expression validation of hub genes. ROC curves of hub genes in proteomics (A), transcriptional regulatory network (B) and expression analysis (C) of hub genes, m6A modification (D) and expression analysis (E) of hub genes, intersection analysis of transcriptomic DEGs and hub genes (F), and mRNA expression validation of overlapping genes (G,H). Sample size: Control group (n = 8); AS group (n = 8). differences in mean values between the control group and AS group were analyzed using an independent samples t-test. Statistical significance was set at P < 0.05.

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  Figure 7. Single-cell transcriptome data analysis of valve calcification patients. Data quality control (A,B), UMAP of cell annotation and calcification scores (C,D), expression of PLAU and MMP9 in smooth muscle cells, T cells, endothelial cells, macrophages, and monocytes across different FC scores and positions (E–I). FC score: fibro-calcification score.

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  Figure 8. Impact of fibro-calcification score on cell functions. Effects of FC score on smooth muscle cell and T cell functions in the fibrosa layer (A,B); effects on endothelial cell, macrophage, and monocyte functions in the fibrosa layer, mixed remaining layer, and ventricular layer (C–E).

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  Figure 9. Effects of genes on cell pseudotime and functions. Impact of PLAU and MMP9 on differentiation trajectories of monocytes (A,B) and macrophages (C,D); effects on functions of endothelial cells, monocytes, and macrophages in the fibrosa layer (E).