Identification of Potential Biomarkers for Progression and Prognosis of Bladder Cancer by Comprehensive Bioinformatics Analysis

Figure 2

WGCNA was used to identify BLCA-related DEGs. (a) In the TCGA-BLCA dataset, analysis of the scale-free fit index (left) and mean connectivity (right) for various soft-thresholding powers. was selected as the optimal soft-thresholding parameter. (b) In the TCGA-BLCA dataset, a dendrogram of all DEGs is clustered based on a dissimilarity score (different colors indicate different modules). (c) A heatmap showing the relationship between module eigengenes and BLCA clinical characteristics. (d) In the brown module, a scatter plot of module eigengenes is shown. (e) In the GSE133624 dataset, analysis of the scale-free fit index (left) and mean connectivity (right) for various soft-thresholding powers. was selected as the optimal soft-thresholding parameter. (f) In the GSE133624 dataset, a dendrogram showing all DEGs grouped using a dissimilarity measure (different colors indicate distinct modules). (g) A heatmap showing the relationship between module eigengenes and BLCA clinical characteristics. (h) In the pink module, a scatter plot of module eigengenes is shown.

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