Cancer LncRNA Census reveals evidence for deep functional conservation of long noncoding RNAs in tumorigenesis
Name
s42003-019-0741-7.pdf
Description
Published version
Size
1.23 MB
Format
Adobe PDF
Checksum (MD5)
f75ff50c76f65c78eb1e6e66c473e123
Author(s)
Carlevaro-Fita, Joana
Lanzós, Andrés
Feuerbach, Lars
Hong, Chen
Mas-Ponte, David
Pedersen, Jakob Skou
Johnson, Rory
Kellis, Manolis
PCAWG Drivers and Functional Interpretation Group
Date Issued
February 2020
Journal
Communications Biology
Publisher
Springer Science and Business Media LLC
Citation
Carlevaro-Fita, Joana et al. "Cancer LncRNA Census reveals evidence for deep functional conservation of long noncoding RNAs in tumorigenesis." Communications Biology 3, 1 (February 2020): 56 © 2020 The Author(s)
Version
Final published version
Abstract
Long non-coding RNAs (lncRNAs) are a growing focus of cancer genomics studies, creating the need for a resource of lncRNAs with validated cancer roles. Furthermore, it remains debated whether mutated lncRNAs can drive tumorigenesis, and whether such functions could be conserved during evolution. Here, as part of the ICGC/TCGA Pan-Cancer Analysis of Whole Genomes (PCAWG) Consortium, we introduce the Cancer LncRNA Census (CLC), a compilation of 122 GENCODE lncRNAs with causal roles in cancer phenotypes. In contrast to existing databases, CLC requires strong functional or genetic evidence. CLC genes are enriched amongst driver genes predicted from somatic mutations, and display characteristic genomic features. Strikingly, CLC genes are enriched for driver mutations from unbiased, genome-wide transposon-mutagenesis screens in mice. We identified 10 tumour-causing mutations in orthologues of 8 lncRNAs, including LINC-PINT and NEAT1, but not MALAT1. Thus CLC represents a dataset of high-confidence cancer lncRNAs. Mutagenesis maps are a novel means for identifying deeply-conserved roles of lncRNAs in tumorigenesis.
MIT Department
Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science
Massachusetts Institute of Technology. Computer Science and Artificial Intelligence Laboratory
Terms of Use
Creative Commons Attribution 4.0 International license
Persistent DSpace Link
DOI of Published Version
https://doi.org/10.1038/s42003-019-0741-7
