scholarly journals An improved method for genome wide DNA methylation profiling correlated to transcription and genomic instability in two breast cancer cell lines

BMC Genomics ◽  
2009 ◽  
Vol 10 (1) ◽  
pp. 223 ◽  
Author(s):  
Jian Li ◽  
Fei Gao ◽  
Ning Li ◽  
Shengting Li ◽  
Guangliang Yin ◽  
...  
Keyword(s):  
Breast Cancer ◽  
Dna Methylation ◽  
Genomic Instability ◽  
Cell Lines ◽  
Breast Cancer Cell ◽  
Breast Cancer Cell Lines ◽  
Improved Method ◽  
Genome Wide ◽  
Dna Methylation Profiling ◽  
Methylation Profiling

scholarly journals Gamma-Normal-Gamma Mixture Model for Detecting Differentially Methylated Loci in Three Breast Cancer Cell Lines

2007 ◽  
Vol 3 ◽  
pp. 117693510700300 ◽  
Author(s):  
Abbas Khalili ◽  
Dustin Potter ◽  
Pearlly Yan ◽  
Lang Li ◽  
Joe Gray ◽  
...  
Keyword(s):  
Breast Cancer ◽  
Mixture Model ◽  
Cell Lines ◽  
Cancer Cell ◽  
Breast Cancer Cell ◽  
Goodness Of Fit ◽  
Cpg Island ◽  
Cancer Cell Lines ◽  
Breast Cancer Cell Lines ◽  
Methylation Profiling

With state-of-the-art microarray technologies now available for whole genome CpG island (CGI) methylation profiling, there is a need to develop statistical models that are specifically geared toward the analysis of such data. In this article, we propose a Gamma-Normal-Gamma (GNG) mixture model for describing three groups of CGI loci: hypomethylated, undifferentiated, and hypermethylated, from a single methylation microarray. This model was applied to study the methylation signatures of three breast cancer cell lines: MCF7, T47D, and MDAMB361. Biologically interesting and interpretable results are obtained, which highlights the heterogeneity nature of the three cell lines. This underlies the premise for the need of analyzing each of the microarray slides individually as opposed to pooling them together for a single analysis. Our comparisons with the fitted densities from the Normal-Uniform (NU) mixture model in the literature proposed for gene expression analysis show an improved goodness of fit of the GNG model over the NU model. Although the GNG model was proposed in the context of single-slide methylation analysis, it can be readily adapted to analyze multi-slide methylation data as well as other types of microarray data.

scholarly journals Hemimethylation Patterns in Breast Cancer Cell Lines

2019 ◽  
Vol 18 ◽  
pp. 117693511987295 ◽  
Author(s):  
Shuying Sun ◽  
Yu Ri Lee ◽  
Brittany Enfield
Keyword(s):  
Breast Cancer ◽  
Dna Methylation ◽  
Tumor Growth ◽  
Cell Lines ◽  
Cancer Cell ◽  
Breast Cancer Cell ◽  
Cancer Cell Lines ◽  
Breast Cancer Cell Lines ◽  
Entire Genome ◽  
Cpg Sites

DNA methylation is an epigenetic event that involves adding a methyl group to the cytosine (C) site, especially the one that pairs with a guanine (G) site (ie, CG or CpG site), in a human genome. This event plays an important role in both cancerous and normal cell development. Previous studies often assume symmetric methylation on both DNA strands. However, asymmetric methylation, or hemimethylation (methylation that occurs only on 1 DNA strand), does exist and has been reported in several studies. Due to the limitation of previous DNA methylation sequencing technologies, researchers could only study hemimethylation on specific genes, but the overall genomic hemimethylation landscape remains relatively unexplored. With the development of advanced next-generation sequencing techniques, it is now possible to measure methylation levels on both forward and reverse strands at all CpG sites in an entire genome. Analyzing hemimethylation patterns may potentially reveal regions related to undergoing tumor growth. For our research, we first identify hemimethylated CpG sites in breast cancer cell lines using Wilcoxon signed rank tests. We then identify hemimethylation patterns by grouping consecutive hemimethylated CpG sites based on their methylation states, methylation “M” or unmethylation “U.” These patterns include regular (or consecutive) hemimethylation clusters (eg, “MMM” on one strand and “UUU” on another strand) and polarity (or reverse) clusters (eg, “MU” on one strand and “UM” on another strand). Our results reveal that most hemimethylation clusters are the polarity type, and hemimethylation does occur across the entire genome with notably higher numbers in the breast cancer cell lines. The lengths or sizes of most hemimethylation clusters are very short, often less than 50 base pairs. After mapping hemimethylation clusters and sites to corresponding genes, we study the functions of these genes and find that several of the highly hemimethylated genes may influence tumor growth or suppression. These genes may also indicate a progressing transition to a new tumor stage.

scholarly journals Genome-wide nucleosome occupancy and DNA methylation profiling of four human cell lines

Genomics Data ◽  
2015 ◽  
Vol 3 ◽  
pp. 94-96 ◽  
Author(s):  
Aaron L. Statham ◽  
Phillippa C. Taberlay ◽  
Theresa K. Kelly ◽  
Peter A. Jones ◽  
Susan J. Clark
Keyword(s):  
Dna Methylation ◽  
Cell Lines ◽  
Human Cell ◽  
Nucleosome Occupancy ◽  
Human Cell Lines ◽  
Genome Wide ◽  
Dna Methylation Profiling ◽  
Methylation Profiling

Translational disequilibrium as an interference marker to study miRNA and methylation silencing of APRIN, a stem cell regulator in breast cancer microchimerism

2009 ◽  
Vol 27 (15_suppl) ◽  
pp. 11109-11109
Author(s):  
P. Geck ◽  
V. Denes ◽  
M. Pilichowska ◽  
A. Makarovskiy ◽  
G. A. Carpinito
Keyword(s):  
Breast Cancer ◽  
Dna Methylation ◽  
Stem Cell ◽  
Cell Lines ◽  
Cancer Cell ◽  
Breast Cancer Cell ◽  
Cancer Cell Lines ◽  
Breast Cancer Cell Lines ◽  
Fetal Microchimerism ◽  
Clinical Breast

11109 Background: Gene silencing is universally observed in cancer and involves promoter DNA methylation. We found that a cohesin-related stem cell regulator, APRIN (Pds5B) was silenced in breast cancer clinical samples. Surprisingly, in 40% of these samples DNA methylation was not involved. Furthermore, in some breast cancer cell lines the APRIN protein was silenced without transcript downregulation or promoter methylation. This “translational disequilibrium” has been frequently reported with other proteins, but without mechanistic explanations. Recent results with RNA interference indicate that gene repression through microRNAs (typically mismatched) is mostly translational without transcript degradation. We propose, therefore, that the puzzling translational disequilibrium phenomenon is a new form of epigenetic silencing by miRNA mechanisms. We aim (i) to verify miRNA epigenetics of APRIN silencing in breast cancer cell lines; (ii) to study clinical breast cancer samples for methylation vs. miRNAs mechanisms in APRIN translational disequilibrium; and (iii) to investigate if miRNA silencing of APRIN affects a fetal embryonic stem cell pool in breast cancer (microchimerism). Methods: (i) We used miRNA mimics and miRNA inhibitors in breast cancer cell lines to verify specific miRNA involvement in APRIN silencing. (ii) We used immunohistochemistry with bisulfite converted DNA for methylation and microdissected RNA for microRNA interference studies from 56 clinical breast cancer samples. (iii) We used Y-chromosome markers on microdissected DNA for fetal microchimerism studies. Results: (i) We found that in breast cancer cell lines with APRIN translational disequilibrium a set of microRNAs correlate with APRIN silencing. (ii) We found miRNA related mechanisms in about 35 percent of breast cancer samples where APRIN was silenced and (iii) APRIN may specifically affect stem cells of fetal origin in the mother's mammary gland and contribute to cancer. Conclusions: The novel miRNA-based mechanism maybe a new epigenetic factor of gene silencing in cancer. We experimentally confirmed a set of APRIN specific miRNAs and established preliminary correlations with fetal microchimerism in breast cancer. No significant financial relationships to disclose.

scholarly journals High-density array analysis of DNA methylation in Tamoxifen-resistant breast cancer cell lines

Epigenetics ◽  
2013 ◽  
Vol 9 (2) ◽  
pp. 297-307 ◽  
Author(s):  
Kristin E Williams ◽  
Douglas L Anderton ◽  
Maxwell P Lee ◽  
Brian T Pentecost ◽  
Kathleen F Arcaro
Keyword(s):  
Breast Cancer ◽  
Dna Methylation ◽  
Cell Lines ◽  
Cancer Cell ◽  
Breast Cancer Cell ◽  
High Density ◽  
Breast Cancer Cell Lines ◽  
Array Analysis ◽  
Tamoxifen Resistant ◽  
Resistant Breast Cancer Cell
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