Computational Investigation of Smooth Muscle Cell Plasticity in Atherosclerosis and Vascular Calcification: Insights from Differential Gene Expression Analysis of Microarray Data

Figures

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  Figure 1. The study design and analysis workflow for gene expression profiling of SMCs across multiple datasets.

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  Figure 2. The top 50 DEGs in two pairwise comparisons: SMC vs. non-SMC and SMC-calc vs. SMC. (A). The log2-fold change (logFC) of the top 50 DEGs was analyzed by limma. Genes with positive logFCs are upregulated in SMCs, whereas those with negative logFCs are downregulated. The size of each point represents the statistical significance (−log10 adjusted p value). (B). The difference in the mean expression of the top 50 genes was analyzed via STAMP. The size of each point corresponds to the -log10 adjusted p value.

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  Figure 3. The PPI network of the top 50 DEGs. (A). from limma and (B). from STAMP in two pairwise comparisons: SMC vs. non-SMC and SMC-calc vs. SMC. The PPI network was analyzed via STRING and visualized via Cytoscape. In the network, the size of each shape corresponds to the gene expression level: larger shapes denote upregulated gene expression (+), whereas smaller shapes indicate downregulated gene expression (−). The circles represent proteins associated with muscle tissue with a score >2.0, and the squares represent proteins with a score <2.0. Additionally, red indicates proteins located in the extracellular compartment with a score >2.0, whereas blue represents those with a score <2.0.

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  Figure 4. Reactome pathway annotation in two pairwise comparisons: SMC vs. non-SMC and SMC-calc vs. SMC. (A). Reactome pathway enrichment analysis of DEGs. The colors represent the statistical significance (adjusted p value). (B). The reactome pathway was first mapped to the gene expression table via Entrez id and then pairwise compared via STAMP with Welch’s t-test, followed by BH FDR correction (q < 0.05, the false-positive rate). The size of each point corresponds to the -log10 adjusted p value.

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  Figure 5. Boxplot of the expression of the 12 marker genes across different groups (non-SMC, SMC, SMC-ath, SMC-calc, and SMC-t). The genes included PCSK1, TGFBI, PMM1, CLDN7, NPPB, and CRYAB in SMC, as well as C1S, CALCOCO2, SAC3D1, MAOA, RGCC, and ADH1B in SMC-calc. PCSK1 is a marker gene found in both SMC and SMC-calc but is shown only once. The y-axis represents gene expression levels, whereas the x-axis categorizes samples into their respective groups. The different colors indicate different groups, as shown in the legend.

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  Figure 6. The PPI network of the 12 candidate genes was constructed using the STRING database. The central network represents the result of simultaneously inputting all 12 genes, with the number of visible interactions adjusted and clustering performed using the MCL algorithm. Nodes are colored according to their cluster, with each color representing an independent functional module. The six functional clusters are separated by dashed lines; the thickness of the lines reflects the confidence level of the interactions. Due to the lack of direct STRING interactions with other genes, five genes that could not form clear PPI subnetworks within the main network are displayed separately as peripheral modules. Each subnetwork was constructed based on the top 10 interacting proteins identified from individual STRING analyses. These independent subnetworks are indicated by blue arrows.