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

SIMPLE SUMMARY: Due to their central role in many biological processes, studies on protein kinases are an area of active research. In the last few decades, several inhibitors that target kinases have been approved by U.S. Food and Drug Administration (FDA). However, a large proportion of kinases remain uncharacterized, with very little information on their functionality. Variations in genome-wide DNA methylation and gene expression pattern have been extensively studied in many cancers. However, an extensive kinome-centered pan-cancer methylation and expression analysis are still lacking. This study aims to identify prognostic and diagnostic biomarkers, focusing on uncharacterized (dark) kinases to further encourage their research as therapeutic targets. 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.