Fig. 5: Proteomic clusters and the impacts of AKAP9 mutation in ESCC progression.
From: Integrative proteogenomic characterization of early esophageal cancer
a Consensus clustering analysis of 786 samples (two-sided Fisher’s exact test). Left: the percentages of the two clusters in 22 substages; Right: 786 samples were classified into two clusters based on proteomic patterns. *p = 0.029 (Age), ****p < 2.2E–16 (Phases), ****p < 2.2E–16 (Substages). b Volcano analysis of DEPs (left) in the two clusters and their associated biological pathways (right) in the two clusters (two-sided Student’s t-test). Biological pathways were analyzed from the Reactome database. C1: the Cluster 1. C2: Cluster 2. c Venn diagram depicting the number of the genes both detected in the genome and proteome in C2. The right shows the significant C2 mutations with mutation frequency over 10%. d Heatmap showing the impacts of AKAP9 mutation on the protein level of AKAP9 (two-sided Student’s t-test, BH-adjusted **p = 8.4E–3). e Scatterplot showing the relationship between log10 PRKACA and log10 AKAP9 expression at the protein level (two-sided Pearson’s correlation test, mean ± SD). f GSEA plot (KEGG gene sets) for glycolysis in AKAP9 mutation and WT comparison. g Heatmap depicting the impacts of AKAP9 mutation on glycolysis in ESCC progression (two-sided Student’s t-test, BH-adjusted *p < 0.05). The square directs to a subset of patient samples used for WES (n = 102). h Scatterplots showing the relationship between log10 G6PD (left)/HK1 (right) and log10 GPI expression at the protein level (two-sided Pearson’s correlation test, mean ± SD). i A brief summary of the impacts of AKAP9 mutation. ****p < 1.0E–4, ***p < 1.0E–3, **p < 0.01, *p < 0.05, ns. > 0.05. Source data are provided as a Source data file.
