New Insights into the Role of Inflammatory Pathways and Immune Cell Infiltration in Sleep Deprivation-Induced Atrial Fibrillation: An Integrated Bioinformatics and Experimental Study

Figures

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  Figure 1. Flowchart of this study.

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  Figure 2. Identification of DEGs associated with AF and SD. (A) Volcano plot illustrating DEGs from the GSE79768 dataset. (B) Corresponding heat map of the DEGs from the GSE79768 dataset. (C) Volcano plot of DEGs identified in the GSE98582 dataset. (D) Heat map of the DEGs from the GSE98582 dataset. (E) Venn diagram showing the overlap of differentially expressed genes between the conditions of SD and AF.

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  Figure 3. Enrichment analysis of common DEGs. (A) Circular plot of enriched Gene Ontology (GO) terms for DEGs, with color indicating logFC. (B) Bar charts of the top GO terms across BP, CC, and MF, ranked by -log10(P-adjusted). (C) Circular heatmap of KEGG pathway enrichment, showing pathways by logFC and Z-score. (D) Bar chart of the top KEGG pathways, ranked by -log10(P-adjusted).

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  Figure 4. Weighted Gene Co-expression Network Analysis (WGCNA) of the GSE79768 dataset and intersecting genes. (A) Gene dendrogram and module color assignment, generated by hierarchical clustering and dynamic tree cutting, showing modules of co-expressed genes. (B) Correlation heatmap of module eigengenes with AF and control traits; colors indicate the strength and direction of correlations. (C) Heatmap of eigengene adjacency, representing the correlations among gene modules. (D) Venn diagram showing the overlap between DEGs and WGCNA-identified modules, highlighting 147 shared genes.

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  Figure 5. Feature selection and network analysis of key genes. (A) Protein-protein interaction (PPI) network of key genes identified by feature selection methods. (B) Centrality analyses of the PPI network using degree, EPC, MCC, and MNC metrics. (C) Venn diagram showing the overlap of top genes identified by different centrality measures. (D) Lasso regression coefficient profiles for gene selection across varying log(λ) values. (E) Binomial deviance plot for Lasso regression, with vertical dashed lines indicating the optimal λ. (F) Top genes ranked by the mean decrease in Gini index from random forest analysis. (G and H) Plots of 10-fold CV accuracy and error versus the number of selected features. (I) Venn diagram depicting the overlap of key genes identified by Lasso, SVM, and RF models.

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  Figure 6. Validation of key gene expression in GSE79768 and GSE98582 dataset. (A–D) Violin plots showing the expression levels of CDC5L, MAPK14, RAB5A, and YBX1 in AF versus control groups in the GSE79768 dataset. (E–H) Violin plots of the same genes comparing SD versus control groups in the GSE98582 dataset. Asterisks denote levels of statistical significance: P < 0.01 (**), P < 0.001 (***).

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  Figure 7. ROC curves for key genes across multiple datasets. (A–D) ROC curves illustrating the diagnostic performance of RAB5A and YBX1 in distinguishing cases from controls in datasets GSE79768 (A), GSE98582 (B), GSE56931 (C), and GSE41177 (D). The x-axis represents the false positive rate (1-Specificity), and the y-axis represents the true positive rate (Sensitivity). Blue lines represent RAB5A, red lines represent YBX1, and the dashed diagonal line indicates random performance (AUC=0.5).

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  Figure 8. Immune infiltration analysis and correlation of YBX1 expression with immune cell populations in the GSE79768 dataset. (A) Box plots showing the relative proportions of various immune cell types in AF versus control groups. (B) Stacked bar chart representing the composition of immune cells in individual samples. (C) Correlation analysis of YBX1 expression with different immune cell populations, with size and color of dots indicating correlation coefficients and p-values, respectively. (D and E) Scatter plots illustrating the correlations between YBX1 expression and the abundance of CD8+ T cells (D) and M1 macrophages (E), with Pearson correlation coefficients and p-values displayed. Asterisks denote statistically significant differences: P < 0.05(*),P < 0.01(**).

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  Figure 9. Increased susceptibility to AF in rats following SD. (A) Representative ECG recordings showing sinus rhythm and AF induction in control and SD groups. (B) Electrophysiological mapping displaying activation maps, conduction velocity maps, and merged maps for both groups. (C) Bar graph showing AERP in control and SD groups. (D) Duration of AF episodes in both groups. (E) AF incidence rates in control and SD groups. (F) CV comparison between groups. (G) CV heterogeneity index in control and SD groups. Asterisks denote statistical significance: P < 0.05(*).

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  Figure 10. Echocardiographic, histopathological, and biochemical assessment in control and SD groups.(A) Representative cardiac ultrasound images showing LAD in control and SD rats. (B) Quantification of LAD in both groups. (C) HE staining of cardiac tissue showing histopathological differences between control and SD groups. Scale bars: 50 µM (D) LVFS comparison between groups. (E–G) ELISA quantification of inflammatory markers, including IL-1β (E), IL-18 (F), and NF-κB (G) levels in cardiac tissue. (H) LVEF comparison between groups. Asterisks indicate statistical significance: P < 0.05(*).

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  Figure 11. Expression analysis of YBX1 and key molecules in the NOD-like receptor signaling pathway.(A) Representative immunofluorescence images showing YBX1 (red) localization in myocardial tissue, counterstained with DAPI (blue) in control and SD groups; merged images demonstrate co-localization. Scale bars: 100 µM (B) Western blot analysis of NLRP3, YBX1, pro-caspase-1, ASC, and caspase-1 in cardiac tissue from control and SD groups, with GAPDH as the loading control. (C) Quantification of YBX1 fluorescence intensity, showing increased expression in the SD group compared to control. (D–H) Quantification of protein expression levels of NLRP3 (D), pro-caspase-1 (F), ASC (G), and active caspase-1 (H) relative to GAPDH, indicating upregulation in the SD group. Asterisks denote statistically significant differences: P < 0.05(*).

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  Figure 12. Schematic diagram of the study. Sleep deprivation increases susceptibility to atrial fibrillation by upregulating YBX1 and activating the NLRP3 inflammasome.