Programmed cell death-related transcriptomic features are associated with aortic valve calcification progression: an integrated analysis of public datasets and clinical samples

Figures

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  Figure 1. Differentially expressed genes and progressive activation of PCD pathways in aortic valve calcification. (A,B) Volcano plot of differentially expressed genes between control and BAV (A) or SAV (B) samples. Red indicates up-regulated genes, while blue indicates down-regulated genes. (C) Violin plot showing increasing PCD GSVA scores among the three groups. (D) Line plot illustrating the upward trend of PCD pathway scores across disease stages. BAV, bicuspid aortic valve; FC, fold change; GSVA, gene set variation analysis; PCD, programmed cell death; SAV, stenotic aortic valve.

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  Figure 2. Co-expression network analysis identifies key modules linked to calcification severity and PCD activity. (A) Determination of the optimal soft-thresholding power based on scale-free topology and mean connectivity. (B) Gene dendrogram and module assignment obtained through dynamic tree cutting. (C) Correlations between module eigengenes and clinical traits. PCD, programmed cell death.

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  Figure 3. Dynamic clustering reveals six distinct temporal expression patterns and identifies 56 core candidate genes. (A) Six dynamic expression clusters identified by Mfuzz. Each panel represents one Mfuzz cluster (Clusters 1–6). Different colored lines indicate individual genes, and the color intensity reflects the membership degree of each gene within the corresponding cluster (higher membership is shown in warmer colors). (B) Venn diagram integrating DEG, WGCNA modules, PCD genes, and Mfuzz clusters. BAV, bicuspid aortic valve; DEG, differentially expressed gene; PCD, programmed cell death; SAV, stenotic aortic valve; WGCNA, weighted gene co-expression network analysis.

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  Figure 4. LASSO-based feature selection identifies four key genes and establishes a robust risk scoring model. (A) LASSO coefficient profiles of 56 candidate genes across increasing penalty parameters. (B) Cross-validation plot identifying the optimal λ for LASSO feature selection. (C) Distribution of risk scores across disease stages. (D) ROC curve showing strong discriminatory ability of the risk model. (E) Calibration curve demonstrating good agreement between predicted and observed probabilities. (F) Nomogram integrating gene-based risk score for predicting calcification probability. (G) Expression levels of four LASSO-selected genes across control, BAV, and SAV groups. **, P<0.01; ***, P<0.001 vs. control group. AUC, area under the curve; AV, aortic valve; BAV, bicuspid aortic valve; LASSO, least absolute shrinkage and selection operator; ROC, receiver operating characteristic; SAV, stenotic aortic valve.

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  Figure 5. External validation confirms the discriminative power and calibration performance of the risk model. (A) Risk score distribution across control, sclerotic, and calcified valve samples in the external validation dataset. (B) ROC curve showing the discriminative performance of the model in the validation cohort. (C) Calibration curve evaluating agreement between predicted and observed probabilities. *, P<0.05; **, P<0.01. AUC, area under the curve; AV, aortic valve; ROC, receiver operating characteristic.

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  Figure 6. Immune infiltration analysis reveals microenvironmental remodeling and its association with the four-gene signature. (A) Immune cell composition across control, BAV, and SAV samples. (B) Correlation heatmap showing coordinated relationships among immune cell subsets. (C) Differential abundance of immune cells across groups. (D) Positive correlations between hub genes and immune cell subsets. *, P<0.05; **, P<0.01 vs. control and SAV groups. BAV, bicuspid aortic valve; SAV, stenotic aortic valve.

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  Figure 7. qRT-PCR validation evaluates the expression patterns of the four-gene signature in peripheral blood leukocyte samples. qRT-PCR validation of CAV1 (A), IGF1 (B), IL7R (C), and JAK1 (D) expression in peripheral blood leukocytes from healthy controls, patients with mild aortic valve sclerosis, and patients with severe aortic valve stenosis. **, P<0.01; ***, P<0.001 vs. control group. qRT-PCR, quantitative real-time polymerase chain reaction.