Document Type
Article
Language
eng
Publication Date
7-16-2018
Publisher
Public Library of Science (PLoS)
Source Publication
PLOS Computational Biology
Source ISSN
1553-7358
Abstract
MicroRNAs (miRNAs) inhibit expression of target genes by binding to their RNA transcripts. It has been recently shown that RNA transcripts targeted by the same miRNA could “compete” for the miRNA molecules and thereby indirectly regulate each other. Experimental evidence has suggested that the aberration of such miRNA-mediated interaction between RNAs—called competing endogenous RNA (ceRNA) interaction—can play important roles in tumorigenesis. Given the difficulty of deciphering context-specific miRNA binding, and the existence of various gene regulatory factors such as DNA methylation and copy number alteration, inferring context-specific ceRNA interactions accurately is a computationally challenging task. Here we propose a computational method called Cancerin to identify cancer-associated ceRNA interactions. Cancerin incorporates DNA methylation, copy number alteration, gene and miRNA expression datasets to construct cancer-specific ceRNA networks. We applied Cancerin to three cancer datasets from the Cancer Genome Atlas (TCGA) project. Our results indicated that ceRNAs were enriched with cancer-related genes, and ceRNA modules in the inferred ceRNA networks were involved in cancer-associated biological processes. Using LINCS-L1000 shRNA-mediated gene knockdown experiment in breast cancer cell line to assess accuracy, Cancerin was able to predict expression outcome of ceRNA genes with high accuracy.
Creative Commons License
This work is licensed under a Creative Commons Attribution 4.0 International License.
Recommended Citation
Do, Duc and Bozdag, Serdar, "Cancerin: A Computational Pipeline to Infer Cancer-Associated ceRNA Interaction Networks" (2018). Mathematics, Statistics and Computer Science Faculty Research and Publications. 644.
https://epublications.marquette.edu/mscs_fac/644
ADA accessible version
Comments
Published version. PLoS Computational Biology, Vol. 14, No. 7 (July 16, 2018): e1006318. DOI. © 2018 Do, Bozdag. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.