Fig. 2

Unsupervised clustering to identify two catenin classifications. A Unsupervised Machine Learning algorithms was used to identify 2 molecular subtypes in TCGA-SKCM. A Left: The cumulative distribution function (CDF) curves in consensus cluster analysis. CDF curves of consensus scores by different subtype numbers (k = 2, 3, 4, 5, and 6) were displayed. Right: Relative change in area under the CDF curve for k = 2–6. B The consensus score matrix of melanoma cases in TCGA-SKCM when k = 2. The higher the consensus score was, the more likely they were assigned to the same group. C Three-dimensional principal component analysis (PCA) plot showing the distribution of melanoma examples based on catenin levels when k = 2. Each point represents a sample and different sample clusters are marked using different colors. D Heatmap showing differences in expression of 10 catenin molecules between two melanoma clusters. E The Sankey diagram demonstrated the association between clinicopathological parameters (Breslow_depth, Clark_level, and T/N/M stage) and subtypes attributes. F OS analysis to the catenin subtype was performed. Log rank test was conducted. G Volcano map of differentially expressed genes (DEGs) between C1 and C2 in TCGA-SKCM dataset. Data on the abscissa are differences in gene expression (log2 fold change); data on the ordinate represent the significance of these differences (− log10 padj). Red indicates upregulation in C1, and blue indicates downregulation in C1. H Gene Ontology (GO) and Kyoto encyclopedia of genes and genomes (KEGG) enrichment analysis was performed using the “clusterProfiler” R package. The size of the bubbles represents the number of genes enriched and the color of the bubbles represents the significance level [− log10 (P.adjust)] of the enrichment. I Representative pictures of pathologic Hematoxylin and eosin (HE) staining of the two catenin phenotypes