ArticleBMC bioinformatics2012
Identification of markers associated with global changes in DNA methylation regulation in cancers.
Article in BMC bioinformatics, 2012. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
8 citing papers in PubMed.
- MECoRank: cancer driver genes discovery simultaneously evaluating the impact of SNVs and differential expression on transcriptional networks.BMC medical genomics · 2019Article
- Topological Characterization of Human and Mouse mInternational journal of genomics · 2018Article
- Shared kernel Bayesian screening.Biometrika · 2015Article
- Evaluation of microarray-based DNA methylation measurement using technical replicates: the Atherosclerosis Risk In Communities (ARIC) Study.BMC bioinformatics · 2014Article
- TMEFF2 and SARDH cooperate to modulate one-carbon metabolism and invasion of prostate cancer cells.The Prostate · 2013Article
- Identification of thresholds for dichotomizing DNA methylation data.EURASIP journal on bioinformatics & systems biology · 2013Article
- TreeVis: a MATLAB-based tool for tree visualization.Computer methods and programs in biomedicine · 2013Article
- The mammary gland carcinogens: the role of metal compounds and organic solvents.International journal of breast cancer · 2013Article
Corrections and comments
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Authors and funding
2 authors.
Funding
Abstract
DNA methylation exhibits different patterns in different cancers. DNA methylation rates at different genomic loci appear to be highly correlated in some samples but not in others. We call such phenomena conditional concordant relationships (CCRs). In this study, we explored DNA methylation patterns in 12 common cancers using data of 2434 patient samples collected by The Cancer Genome Atlas project. We developed an exploratory method to characterize CCRs in the methylation data and identified the 200 gene markers whose on-and-off statuses in DNA methylation are most significantly associated with drastic changes in CCRs throughout the genome. Clustering analysis of the methylation data of the 200 markers showed that they are tightly associated with cancer subtypes. We also generated a library of the significant CCRs that may be of interest to future studies of the regulation network of DNA methylation in cancer.
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Registered trials
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