Paper Info
Title
Discovering DNA methylation patterns for long non-coding RNAs associated with cancer subtypes.
Abstract
Despite growing evidence demonstrates that the long non-coding ribonucleic acids (lncRNAs) are critical modulators for cancers, the knowledge about the DNA methylation patterns of lncRNAs is quite limited. We develop a systematic analysis pipeline to discover DNA methylation patterns for lncRNAs across multiple cancer subtypes from probe, gene and network levels. By using The Cancer Genome Atlas (TCGA) breast cancer methylation data, the pipeline discovers various DNA methylation patterns for lncRNAs across four major subtypes such as luminal A, luminal B, her2-enriched as well as basal-like. On the probe and gene level, we find that both differentially methylated probes and lncRNAs are subtype specific, while the lncRNAs are not as specific as probes. On the network level, the pipeline constructs differential co-methylation lncRNA network for each subtype. Then, it identifies both subtype specific and common lncRNA modules by simultaneously analyzing multiple networks. We show that the lncRNAs in subtype specific and common modules differ greatly in terms of topological structure, sequence conservation as well as expression. Furthermore, the subtype specific lncRNA modules serve as biomarkers to improve significantly the accuracy of breast cancer subtypes prediction. Finally, the common lncRNA modules associate with survival time of patients, which is critical for cancer therapy.
Year
DOI
Venue
2017
10.1016/j.compbiolchem.2017.03.014
Computational Biology and Chemistry
Keywords
Field
DocType
Cancer subtype,Long noncoding RNA (lncRNA),DNA methylation,Network biology
Genome,Gene,Breast cancer,Biology,Biological network,DNA methylation,Methylation,Biomarker (medicine),Bioinformatics,Genetics,Cancer
Journal
Volume
ISSN
Citations 
69
1476-9271
2
PageRank 
References 
Authors
0.40
7
4
Name
Order
Citations
PageRank
Xiaoke Ma17611.69
Yu Liang22112.01
Peizhuo Wang341.10
Xiaofei Yang491.48