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LRpath analysis reveals common pathways dysregulated via DNA methylation across cancer types
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...
Autores principales: | , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
BioMed Central
2012
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3505188/ https://www.ncbi.nlm.nih.gov/pubmed/23033966 http://dx.doi.org/10.1186/1471-2164-13-526 |
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author | Kim, Jung H Karnovsky, Alla Mahavisno, Vasudeva Weymouth, Terry Pande, Manjusha Dolinoy, Dana C Rozek, Laura S Sartor, Maureen A |
author_facet | Kim, Jung H Karnovsky, Alla Mahavisno, Vasudeva Weymouth, Terry Pande, Manjusha Dolinoy, Dana C Rozek, Laura S Sartor, Maureen A |
author_sort | Kim, Jung H |
collection | PubMed |
description | 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. |
format | Online Article Text |
id | pubmed-3505188 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2012 |
publisher | BioMed Central |
record_format | MEDLINE/PubMed |
spelling | pubmed-35051882012-11-24 LRpath analysis reveals common pathways dysregulated via DNA methylation across cancer types Kim, Jung H Karnovsky, Alla Mahavisno, Vasudeva Weymouth, Terry Pande, Manjusha Dolinoy, Dana C Rozek, Laura S Sartor, Maureen A BMC Genomics Research Article 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. BioMed Central 2012-10-04 /pmc/articles/PMC3505188/ /pubmed/23033966 http://dx.doi.org/10.1186/1471-2164-13-526 Text en Copyright ©2012 Kim et al.; licensee BioMed Central Ltd. http://creativecommons.org/licenses/by/2.0 This is an Open Access article distributed under the terms of the Creative Commons Attribution License ( http://creativecommons.org/licenses/by/2.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. |
spellingShingle | Research Article Kim, Jung H Karnovsky, Alla Mahavisno, Vasudeva Weymouth, Terry Pande, Manjusha Dolinoy, Dana C Rozek, Laura S Sartor, Maureen A LRpath analysis reveals common pathways dysregulated via DNA methylation across cancer types |
title | LRpath analysis reveals common pathways dysregulated via DNA methylation across cancer types |
title_full | LRpath analysis reveals common pathways dysregulated via DNA methylation across cancer types |
title_fullStr | LRpath analysis reveals common pathways dysregulated via DNA methylation across cancer types |
title_full_unstemmed | LRpath analysis reveals common pathways dysregulated via DNA methylation across cancer types |
title_short | LRpath analysis reveals common pathways dysregulated via DNA methylation across cancer types |
title_sort | lrpath analysis reveals common pathways dysregulated via dna methylation across cancer types |
topic | Research Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3505188/ https://www.ncbi.nlm.nih.gov/pubmed/23033966 http://dx.doi.org/10.1186/1471-2164-13-526 |
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