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Significant associations between driver gene mutations and DNA methylation alterations across many cancer types.
Chen, Yun-Ching; Gotea, Valer; Margolin, Gennady; Elnitski, Laura.
Afiliación
  • Chen YC; Genomic Functional Analysis Section, National Human Genome Research Institute, National Institutes of Health, Bethesda, Maryland, United States.
  • Gotea V; Genomic Functional Analysis Section, National Human Genome Research Institute, National Institutes of Health, Bethesda, Maryland, United States.
  • Margolin G; Genomic Functional Analysis Section, National Human Genome Research Institute, National Institutes of Health, Bethesda, Maryland, United States.
  • Elnitski L; Genomic Functional Analysis Section, National Human Genome Research Institute, National Institutes of Health, Bethesda, Maryland, United States.
PLoS Comput Biol ; 13(11): e1005840, 2017 Nov.
Article en En | MEDLINE | ID: mdl-29125844
ABSTRACT
Recent evidence shows that mutations in several driver genes can cause aberrant methylation patterns, a hallmark of cancer. In light of these findings, we hypothesized that the landscapes of tumor genomes and epigenomes are tightly interconnected. We measured this relationship using principal component analyses and methylation-mutation associations applied at the nucleotide level and with respect to genome-wide trends. We found that a few mutated driver genes were associated with genome-wide patterns of aberrant hypomethylation or CpG island hypermethylation in specific cancer types. In addition, we identified associations between 737 mutated driver genes and site-specific methylation changes. Moreover, using these mutation-methylation associations, we were able to distinguish between two uterine and two thyroid cancer subtypes. The driver gene mutation-associated methylation differences between the thyroid cancer subtypes were linked to differential gene expression in JAK-STAT signaling, NADPH oxidation, and other cancer-related pathways. These results establish that driver gene mutations are associated with methylation alterations capable of shaping regulatory network functions. In addition, the methodology presented here can be used to subdivide tumors into more homogeneous subsets corresponding to underlying molecular characteristics, which could improve treatment efficacy.
Asunto(s)

Texto completo: 1 Bases de datos: MEDLINE Asunto principal: Transducción de Señal / Metilación de ADN / Mutación / Neoplasias Tipo de estudio: Prognostic_studies / Risk_factors_studies Límite: Humans Idioma: En Revista: PLoS Comput Biol Asunto de la revista: BIOLOGIA / INFORMATICA MEDICA Año: 2017 Tipo del documento: Article País de afiliación: Estados Unidos

Texto completo: 1 Bases de datos: MEDLINE Asunto principal: Transducción de Señal / Metilación de ADN / Mutación / Neoplasias Tipo de estudio: Prognostic_studies / Risk_factors_studies Límite: Humans Idioma: En Revista: PLoS Comput Biol Asunto de la revista: BIOLOGIA / INFORMATICA MEDICA Año: 2017 Tipo del documento: Article País de afiliación: Estados Unidos