Fig. 3

Screening of key module genes in the integrated CAVD dataset via WGCNA and identification of CAVD key genes through the intersection of key module genes and DEGs. A The scale-free topology model was utilized to identify the best β value, and β = 5 was chosen as the soft threshold based on the average connectivity and scale independence. B The network heatmap showing the gene dendrogram and module eigengenes. C The cluster dendrogram presenting module eigengenes. D The heatmap revealing the relationship between module eigengenes and status of CAVD. The correlation (upper) and p-value (bottom) of module eigengenes and status of CAVD were presented. The pink and yellow modules correlated with CAVD exhibited the highest and lowest correlation coefficients, respectively, which were identified as the key modules in CAVD. E The correlation plot between the pink module membership and the gene significance of genes in the pink module. F The correlation plot between the yellow module membership and the gene significance of genes in the yellow module. G A total of 124 key genes in CAVD were identified by taking the intersection between key modules genes and DEGs via the venn diagram. WGCNA weighted gene co-expression network analysis, CAVD calcific aortic valve disease, DEG differentially expressed genes