Discovering DNA methylation patterns for long non-coding RNAs associated with cancer subtypes. (August 2017)

• Record Type: Journal Article
• Authors: Ma, Xiaoke; Yu, Liang; Wang, Peizhuo; Yang, Xiaofei
• Is Part Of: Computational biology and chemistry. Volume 69(2017)
• Page Start: 164
• Page End: 170
• Publication Date: 2017-08
• Subjects: Cancer subtype -- Long noncoding RNA (lncRNA) -- DNA methylation -- Network biology; Chemistry -- Data processing -- Periodicals; Biology -- Data processing -- Periodicals; Biochemistry -- Data processing; Biology -- Data processing; Molecular biology -- Data processing; Periodicals; Electronic journals; 542.85
• DOI: 10.1016/j.compbiolchem.2017.03.014
• Languages: English
• ISSNs: 1476-9271

Abstract

Abstract : Graphical abstract: Abstract : Highlights: A systematic pipeline is proposed to discover methylation patterns for lncRNAs. Subtype specific and common lncRNAs differ greatly in functions and structures. Methylation modules enhances the accuracy of prediction of cancers. 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.