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Open Access Research article

LRpath analysis reveals common pathways dysregulated via DNA methylation across cancer types

Jung H Kim12, Alla Karnovsky1, Vasudeva Mahavisno1, Terry Weymouth1, Manjusha Pande3, Dana C Dolinoy2, Laura S Rozek24 and Maureen A Sartor1*

Author affiliations

1 Department of Computational Medicine and Bioinformatics, Medical School, University of Michigan, Ann Arbor, MI, USA

2 Department of Environmental Health Sciences, School of Public Health, University of Michigan, Ann Arbor, MI, USA

3 Neurology Department, Medical School, University of Michigan, Ann Arbor, MI, USA

4 Department of Otolaryngology, Medical School, University of Michigan, Ann Arbor, Mi, USA

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Citation and License

BMC Genomics 2012, 13:526  doi:10.1186/1471-2164-13-526

Published: 4 October 2012

Abstract

Background

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. 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.

Results

We 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.

Conclusions

Clustering 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.