The onset and progression of breast cancer are linked to genetic and epigenetic changes that alter the normal programming of cells. Epigenetic modifications of DNA and histones contribute to chromatin structure that result in the activation or repression of gene expression. Several epigenetic pathways have been shown to be highly deregulated in cancer cells. Targeting specific histone modifications represents a viable strategy to prevent oncogenic transformation, tumor growth or metastasis. Methylation of histone H3 lysine 4 has been extensively studied and shown to mark genes for expression; however this residue can also be acetylated and the specific function of this alteration is less well known. To define the relative roles of histone H3 methylation (H3K4me3) and acetylation (H3K4ac) in breast cancer, we determined genomic regions enriched for both marks in normal-like (MCF10A), transformed (MCF7) and metastatic (MDA-MB-231) cells using a genome-wide ChIP-Seq approach. Our data revealed a genome-wide gain of H3K4ac associated with both early and late breast cancer cell phenotypes, while gain of H3K4me3 was predominantly associated with late stage cancer cells. Enrichment of H3K4ac was over-represented at promoters of genes associated with cancer-related phenotypic traits, such as estrogen response and epithelial-to-mesenchymal transition pathways. Our findings highlight an important role for H3K4ac in predicting epigenetic changes associated with early stages of transformation. In addition, our data provide a valuable resource for understanding epigenetic signatures that correlate with known breast cancer-associated oncogenic pathways.
Pubmed ID: 26783963 RIS Download
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Software platform for complex network analysis and visualization. Used for visualization of molecular interaction networks and biological pathways and integrating these networks with annotations, gene expression profiles and other state data.
View all literature mentionsTHIS RESOURCE IS NO LONGER IN SERVICE. Documented on February 28,2023. Software Python package that provides infrastructure to process data from high-throughput sequencing assays. While the main purpose of HTSeq is to allow you to write your own analysis scripts, customized to your needs, there are also a couple of stand-alone scripts for common tasks that can be used without any Python knowledge.
View all literature mentionsScript distributed with the HT-Seq Python framework for processing RNA-seq or DNA-seq data.
View all literature mentionsHuman and mouse genome annotation project which aims to identify all gene features in the human genome using computational analysis, manual annotation, and experimental validation.
View all literature mentionsSoftware package for differential gene expression analysis based on the negative binomial distribution. Used for analyzing RNA-seq data for differential analysis of count data, using shrinkage estimation for dispersions and fold changes to improve stability and interpretability of estimates.
View all literature mentionsCell line MDA-MB-231 is a Cancer cell line with a species of origin Homo sapiens (Human)
View all literature mentionsCell line MCF-10A is a Spontaneously immortalized cell line with a species of origin Homo sapiens
View all literature mentionsCell line MCF-7 is a Cancer cell line with a species of origin Homo sapiens (Human)
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