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
  • [31] 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
  • [32] Integrative analysis of methylome and transcriptome in human blood identifies extensive sex- and immune cell-specific differentially methylated regions
    Mamrut, Shimrat
    Avidan, Nili
    Staun-Ram, Elsebeth
    Ginzburg, Elizabeta
    Truffault, Frederique
    Berrih-Aknin, Sonia
    Miller, Ariel
    EPIGENETICS, 2015, 10 (10) : 943 - 957
  • [33] Detecting multiple differentially methylated CpG sites and regions related to dimensional psychopathology in youths
    Leticia M. Spindola
    Marcos L. Santoro
    Pedro M. Pan
    Vanessa K. Ota
    Gabriela Xavier
    Carolina M. Carvalho
    Fernanda Talarico
    Patrick Sleiman
    Michael March
    Renata Pellegrino
    Elisa Brietzke
    Rodrigo Grassi-Oliveira
    Jair J. Mari
    Ary Gadelha
    Euripedes C. Miguel
    Luis A. Rohde
    Rodrigo A. Bressan
    Diego R. Mazzotti
    João R. Sato
    Giovanni A. Salum
    Hakon Hakonarson
    Sintia I. Belangero
    Clinical Epigenetics, 2019, 11
  • [34] Discovery and development of differentially methylated regions in human papillomavirus-related oropharyngeal squamous cell carcinoma
    Ren, Shuling
    Gaykalova, Daria
    Wang, Jennifer
    Guo, Theresa
    Danilova, Ludmila
    Favorov, Alexander
    Fertig, Elana
    Bishop, Justin
    Khan, Zubair
    Flam, Emily
    Wysocki, Piotr T.
    DeJong, Peter
    Ando, Mizuo
    Liu, Chao
    Sakai, Akihiro
    Fukusumi, Takahito
    Haft, Sunny
    Sadat, Sayed
    Califano, Joseph A.
    INTERNATIONAL JOURNAL OF CANCER, 2018, 143 (10) : 2425 - 2436
  • [35] Differentially Methylated Genomic Regions in Birth-Weight Discordant Twin Pairs
    Chen, Mubo
    Baumbach, Jan
    Vandin, Fabio
    Rottger, Richard
    Barbosa, Eudes
    Dong, Mingchui
    Frost, Morten
    Christiansen, Lene
    Tan, Qihua
    ANNALS OF HUMAN GENETICS, 2016, 80 (02) : 81 - 87
  • [36] GeneDMRs: An R Package for Gene-Based Differentially Methylated Regions Analysis
    Wang, Xiao
    Hao, Dan
    Kadarmideen, Haja N.
    JOURNAL OF COMPUTATIONAL BIOLOGY, 2021, 28 (03) : 304 - 316
  • [37] Dysregulation of DNA methylation patterns may identify patients with breast cancer resistant to endocrine therapy: A predictive classifier based on differentially methylated regions
    Zhang, Fan
    Cui, Yukun
    ONCOLOGY LETTERS, 2019, 18 (02) : 1287 - 1303
  • [38] Regulatory Elements in Low-Methylated Regions Predict Directional Change of Gene Expression
    Hu, Hong
    Xu, Jingting
    Dai, Yang
    IEEE JOURNAL OF BIOMEDICAL AND HEALTH INFORMATICS, 2015, 19 (04) : 1293 - 1300
  • [39] Differentially Methylated DNA Regions in Monozygotic Twin Pairs Discordant for Rheumatoid Arthritis: An Epigenome-Wide study
    Svendsen, Anders J.
    Gervin, Kristina
    Lyle, Robert
    Christiansen, Lene
    Kyvik, Kirsten
    Junker, Peter
    Nielsen, Christian
    Houen, Gunnar
    Tan, Qihua
    FRONTIERS IN IMMUNOLOGY, 2016, 7
  • [40] Differentially methylated gene regions between resistant and susceptible heat-phenotypes of the Pacific oyster Crassostrea gigas
    Roberto, Arredondo-Espinoza
    Ana, M. Ibarra
    Steven, Roberts B.
    Teresa, Sicard-Gonzalez Maria
    Cristina, Escobedo-Fregoso
    AQUACULTURE, 2021, 543