Fig. 5: Characterization of immune-based clusters in DC subtypes.

a Heatmap illustrating cell type compositions and activities of the functional genes/proteins across the four immune clusters (Kruskal–Wallis test, BH-adjusted p < 0.05). The square directs to a subset of patient samples used for proteomic profiling (n = 438). b Boxplot showing the HLA-I score (left) and HLA-II score (right) in the four immune clusters (two-sided Wilcoxon signed-ranked test). n (Hot immune cluster) = 105, n (Cold tumor) = 75, n (Epithelial cluster) = 129, n (B cells cluster) = 129 biologically independent samples examined. Boxplot shows median (central line), upper and lower quartiles (box limits), 1.5× interquartile range (whiskers). c Proteome-based deconvolution of the immune-related cell signatures/proteins and dominant pathways in the four immune clusters (Kruskal–Wallis test, BH-adjusted p < 0.05). The GSVA score based on the global proteomic data for biological pathways overrepresented in different immune clusters. The square directs to a subset of patient samples used for proteomic profiling (n = 438), as well as in Fig. 5a. GSVA gene set variation analysis. d Density contours of immune and microenvironment scores of four immune clusters. e Heatmap showing the correlation between AARS1 and TMB, immune infiltration, and HLA-I molecules (two-sided Pearson’s correlation test). The square directs to a subset of patient samples used for WES (n = 120). TMB tumor mutation burden. f The (Pearson’s) correlation between AARS1 and the predominant pathways in the Hot tumor cluster (two-sided Pearson’s correlation test). g The correlation between AARS1 and makers of the predominant pathways in the Hot tumor cluster (two-sided Pearson’s correlation test). h A brief summary of the impacts of AARS1 in the DC. ****p < 1.0E−4, ***p < 1.0E−3, **p < 1.0E−2, *p < 0.05, ns. >0.05. Source data are provided as a Source data file.