Abstract
BackgroundThe relative contribution of epigenetic mechanisms to carcinogenesis is not well understood, including the extent to which epigenetic dysregulation and somatic mutations target similar genes and pathways. We hypothesize that during carcinogenesis, certain pathways or biological gene sets are commonly dysregulated via DNA methylation across cancer types. The ability of our logistic regression-based gene set enrichment method to implicate important biological pathways in high-throughput data is well established.ResultsWe developed a web-based gene set enrichment application called LRpath with clustering functionality that allows for identification and comparison of pathway signatures across multiple studies. Here, we employed LRpath analysis to unravel the commonly altered pathways and other gene sets across ten cancer studies employing DNA methylation data profiled with the Illumina HumanMethylation27 BeadChip. We observed a surprising level of concordance in differential methylation across multiple cancer types. For example, among commonly hypomethylated groups, we identified immune-related functions, peptidase activity, and epidermis/keratinocyte development and differentiation. Commonly hypermethylated groups included homeobox and other DNA-binding genes, nervous system and embryonic development, and voltage-gated potassium channels. For many gene sets, we observed significant overlap in the specific subset of differentially methylated genes. Interestingly, fewer DNA repair genes were differentially methylated than expected by chance.ConclusionsClustering analysis performed with LRpath revealed tightly clustered concepts enriched for differential methylation. Several well-known cancer-related pathways were significantly affected, while others were depleted in differential methylation. We conclude that DNA methylation changes in cancer tend to target a subset of the known cancer pathways affected by genetic aberrations.
Highlights
The relative contribution of epigenetic mechanisms to carcinogenesis is not well understood, including the extent to which epigenetic dysregulation and somatic mutations target similar genes and pathways
Performing an integrative analysis of biological concepts dysregulated via methylation across ten cancer types, we identified concepts affected in multiple cancer types that support biologically important findings
Our analysis validated KCNA3 as hypermethylated in breast cancer, plus identified it as hypermethylated in an additional 7 tumor types. Another example is human ether-a-go-go-related gene 1, which we found significantly differentially methylated in lung adenocarcinoma, myeloma and stomach cancers. hERG1 is often dysregulated in cancer and physically interacts with integrin to modulate adhesion dependent intracellular signalling cascades, including cell adhesion, invasion, and proliferation [39,40]
Summary
The relative contribution of epigenetic mechanisms to carcinogenesis is not well understood, including the extent to which epigenetic dysregulation and somatic mutations target similar genes and pathways. Since the introduction of the Illumina HumanMethylation BeadChip platform, which measures the methylation of over 27,000 CpG sites across the human genome, several studies have reported genomic sites with aberrant methylation in cancers These publicly available datasets, including several performed by The Cancer Genome Atlas (TCGA), allow for an integrative analysis of DNA methylation across multiple cancer. The most commonly used approach to identifying enriched sets of genes is based on counting the number of differentially expressed genes in a particular biological concept. A biological concept is a pre-defined, biologically-related set of genes, derived from any one of a number of different annotation sources [1] Such focus on biological concepts rather than individual genes has proven useful in cancer research. A number of tools that utilize this, or a very similar approach have been developed, such as David/EASE [4,5], Onto-Express [6,7], ConceptGen [1], the Gostats package of Bioconductor [8], GOMiner [9,10], and FuncAssociate [11]
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