Accounting for differential variability in detecting differentially methylated regions

被引:8
|
作者
Wang, Ya [1 ]
Teschendorff, Andrew E. [2 ,3 ]
Widschwendter, Martin [2 ]
Wang, Shuang [1 ]
机构
[1] Columbia Univ, Mailman Sch Publ Hlth, Dept Biostat, 722 West 168th St, New York, NY 10032 USA
[2] UCL, Dept Womens Canc, London, England
[3] Chinese Acad Sci, Shanghai Inst Biol Sci, CAS MPG Partner Inst Computat Biol, CAS Key Lab Computat Biol, Shanghai, Peoples R China
关键词
DNA methylation; differential variability; algorithm; differentially methylated regions; DNA-METHYLATION; GENE-EXPRESSION; PROFILING REVEALS; CANCER; CELL; HYPERMETHYLATION; MECHANISMS; PROMOTER; DISEASE; IDENTIFICATION;
D O I
10.1093/bib/bbx097
中图分类号
Q5 [生物化学];
学科分类号
071010 ; 081704 ;
摘要
DNA methylation plays an essential role in cancer. Differential variability (DV) in cancer was recently observed that contributes to cancer heterogeneity and has been shown to be crucial in detecting epigenetic field defects, DNA methylation alterations happening early in carcinogenesis. As neighboring CpG sites are highly correlated, here, we present a new method to detect differentially methylated regions (DMRs) that uses combined signals from differential methylation and DV between sample groups. We demonstrated in simulation studies the superior performance of the new method than existing methods that use only one type of signals when true DMRs have both. Applications to DNA methylation data of breast invasive carcinoma (BRCA) and kidney renal clear cell carcinoma (KIRC) from The Cancer Genome Atlas (TCGA) and BRCA from Gene Expression Omnibus (GEO) suggest that the new method identified additional cancer-related DMRs that were missed by methods using one type of signals. Replication analyses using two independent BRCA data sets suggest that DMRs detected based on DV are reproducible. Only the new method identified epigenetic field defects when comparing normal tissues adjacent to tumors and normal tissues from age-matched cancer-free women from the GEO BRCA data and confirmed their enrichment in the progression to breast cancer.
引用
收藏
页码:47 / 57
页数:11
相关论文
共 50 条
  • [21] Identification of Imprinted Genes and Their Differentially Methylated Regions in Porcine
    Z. Yin
    X. Zhang
    J. Li
    Y. Jiao
    Q. Kong
    Y. Mu
    Russian Journal of Genetics, 2019, 55 : 1488 - 1498
  • [22] Efficient detection of differentially methylated regions using DiMmeR
    Almeida, Diogo
    Skov, Ida
    Silva, Artur
    Vandin, Fabio
    Tan, Qihua
    Rottger, Richard
    Baumbach, Jan
    BIOINFORMATICS, 2017, 33 (04) : 549 - 551
  • [23] Maintaining memory of silencing at imprinted differentially methylated regions
    Voon, Hsiao P. J.
    Gibbons, Richard J.
    CELLULAR AND MOLECULAR LIFE SCIENCES, 2016, 73 (09) : 1871 - 1879
  • [24] Identification of Imprinted Genes and Their Differentially Methylated Regions in Porcine
    Yin, Z.
    Zhang, X.
    Li, J.
    Jiao, Y.
    Kong, Q.
    Mu, Y.
    RUSSIAN JOURNAL OF GENETICS, 2019, 55 (12) : 1488 - 1498
  • [25] Identification of differentially methylated regions (DMRs) of neuronatin in mice
    Xu, Yuxin
    Liu, Zhiquan
    Wang, Tiedong
    Chen, Xianju
    Deng, Jichao
    Chen, Mao
    Li, Zhanjun
    SPRINGERPLUS, 2016, 5
  • [26] Maintaining memory of silencing at imprinted differentially methylated regions
    Hsiao P. J. Voon
    Richard J. Gibbons
    Cellular and Molecular Life Sciences, 2016, 73 : 1871 - 1879
  • [27] Correction to: Detecting differentially methylated regions using a fast wavelet-based approach to functional association analysis
    William R. P. Denault
    Astanand Jugessur
    BMC Bioinformatics, 22
  • [28] A Blind and Independent Benchmark Study for Detecting Differentially Methylated Regions in Plants (vol 36, pg 3314, 2020)
    Kreutz, Clemens
    Can, Nilay S.
    Bruening, Ralf Schulze
    Meyberg, Rabea
    Merai, Zsuzsanna
    Fernandez-Pozo, Noe
    Rensing, Stefan A.
    BIOINFORMATICS, 2020, 36 (17) : 4673 - 4673
  • [29] Comprehensive analysis of differentially methylated regions in colorectal cancer (CRC).
    Solari, Omid
    Constantin, Tudor
    Jiang, Yuqian
    Chan, Wengching
    Tunc, Ilker
    Srinivasan, Preethi
    Kordi, Misagh
    Santaguida, Marianne
    Mitchell, Breeana L.
    Aleshin, Alexey
    Swenerton, Ryan
    Babiarz, Joshua
    Kawli, Trupti
    Reiter, Johannes G.
    JOURNAL OF CLINICAL ONCOLOGY, 2024, 42 (3_SUPPL) : 73 - 73
  • [30] A Novel Method for Identification and Quantification of Consistently Differentially Methylated Regions
    Hsiao, Ching-Lin
    Hsieh, Ai-Ru
    Lian, Ie-Bin
    Lin, Ying-Chao
    Wang, Hui-Min
    Fann, Cathy S. J.
    PLOS ONE, 2014, 9 (05):