Pan-Cancer Analysis of Human Kinome Gene Expression and Promoter DNA Methylation Identifies Dark Kinase Biomarkers in Multiple Cancers

Abstract

Kinases are a group of intracellular signaling molecules that play critical roles in various biological processes. Even though kinases comprise one of the most well-known therapeutic targets, many have been understudied and therefore warrant further investigation. DNA methylation is one of the key epigenetic regulators that modulate gene expression. In this study, the human kinome's DNA methylation and gene expression patterns were analyzed using the level-3 TCGA data for 32 cancers. Unsupervised clustering based on kinome data revealed the grouping of cancers based on their organ level and tissue type. We further observed significant differences in overall kinase methylation levels (hyper- and hypomethylation) between the tumor and adjacent normal samples from the same tissue. Methylation expression quantitative trait loci (meQTL) analysis using kinase gene expression with the corresponding methylated probes revealed a highly significant and mostly negative association (~92%) within 1.5 kb from the transcription start site (TSS). Several understudied (dark) kinases (PKMYT1, PNCK, BRSK2, ERN2, STK31, STK32A, and MAPK4) were also identified with a significant role in patient survival. This study leverages results from multi-omics data to identify potential kinase markers of prognostic and diagnostic importance and further our understanding of kinases in cancer.

Keywords: CpG methylation; TCGA; correlation analysis; dark kinase; kinome; pan-cancer; promoter; survival analysis; understudied kinase.

Figure 1

Distribution of the TCGA cancer samples in 2D t-SNE plot. (a) x vs. y (b) x vs. z (c) y vs. z coordinates for 6270 (combined kinase gene expression and promoter CpGs β values) features and 7783 TCGA tumor samples belonging to 30 cancer types. The mapped data points are colored as per the cancer types. (d–f) 2D t-SNE plot showing separation of TCGA Esophageal, Lung Carcinoma and Cervical Cancer into Adeno and Squamous histological tissue types based on combined kinase gene expression and methylation data of 930 samples which includes 71 esophageal adenocarcinoma, 80 esophageal squamous cell carcinoma, 414 lung adenocarcinomas, 365 lung squamous cell carcinoma, 246 cervical squamous cell carcinoma and 30 endocervical adenocarcinoma.

Figure 2

Distribution of the differentially expressed genes. (a) Sunburst plot showing the top DE kinase genes in ≥ 4 TCGA cancer types. Dark kinases are highlighted in maroon (b) Number of Upregulated (red) and downregulated (blue) kinase genes observed in analyzed cancers. (c) Table showing the list of common DE genes obtained in ≥ 10 cancers, the direction (upregulated—red, downregulated—blue) and in number of cancers observed. Dark kinases are marked with * symbol.

Figure 3

Distribution of DM CpGs across cancers. (a) Box plots showing distribution of hyper (Red) and hypomethylated (blue) probes and the corresponding average gene expression (light red and light blue) in different cancers. The gene expression values were normalized between 0 and 1. T-test was used to show the significance level between the methylation levels of the hyper and hypo methylated probes and between the corresponding gene expression level (ns: p > 0.05, *: p ≤ 0.05, **: p ≤ 0.01, ***: p ≤ 0.001, ****: p ≤ 0.0001). CpG probes with mean β value difference of at least 0.2 (Δβ ≥ 0.2) at BH adjusted p-value < 0.05 were considered differentially methylated. (b) Distribution of hypermethylated (red) and hypomethylated (blue) probes obtained for each cancer. (c) List of commonly observed probes DM in ≥ 10 cancers, their direction of methylation (hypermethylation—red and hypomethylation—blue) and the number of cancers observed. Dark kinase genes are marked with * symbol.

Figure 4

Correlation between kinase gene expression and methylation (a) Distribution of pan-cancer positive and negative correlation between DNA methylation β value and gene expression plotted against distance between CpG sites within 50Kb and transcription start site (TSS) (b) Bubble plot showing the status of most significant correlations belonging to Dark Kinases group +/− 1500 bp from TSS obtained at Bonferroni corrected p-value < 0.05. Negative correlations are shown in red and positive correlations are shown in blue. Genes with significant correlation values obtained in > 30% of analyzed cancers are plotted.

Figure 5

Dark kinase genes and methylation in survival (a) List of dark kinases whose expression, and methylation of CpG probes found to be significant in survival (p-value < 0.05) in various cancers. (b) Top upregulated dark kinases from DGE analysis whose high and low expression groups also have a significant difference in the overall survival (p-value < 0.05) in several cancers.

Figure 6

Role of PKMYT1 in prognosis and diagnosis (a,b) Survival plots of PKMYT1 high vs. low gene expression and promoter DNA methylation sites (cg02510853) which are associated with KIRC patient survival with p-value for KM plot (log-rank test) and Cox proportional hazard model. (c,d) Corresponding ROC plot of gene expression and promoter methylation for the generalized linear model.