Crinet: A computational tool to infer genome-wide competing endogenous RNA (ceRNA) interactions.

To understand driving biological factors for complex diseases like cancer, regulatory circuity of genes needs to be discovered. Recently, a new gene regulation mechanism called competing endogenous RNA (ceRNA) interactions has been discovered. Certain genes targeted by common microRNAs (miRNAs) &quo...

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Main Authors: Ziynet Nesibe Kesimoglu, Serdar Bozdag
Format: Article
Language:English
Published: Public Library of Science (PLoS) 2021-01-01
Series:PLoS ONE
Online Access:https://doi.org/10.1371/journal.pone.0251399
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spelling doaj-75aad5680b7f4a668c135b46a8e8122c2021-05-29T04:31:52ZengPublic Library of Science (PLoS)PLoS ONE1932-62032021-01-01165e025139910.1371/journal.pone.0251399Crinet: A computational tool to infer genome-wide competing endogenous RNA (ceRNA) interactions.Ziynet Nesibe KesimogluSerdar BozdagTo understand driving biological factors for complex diseases like cancer, regulatory circuity of genes needs to be discovered. Recently, a new gene regulation mechanism called competing endogenous RNA (ceRNA) interactions has been discovered. Certain genes targeted by common microRNAs (miRNAs) "compete" for these miRNAs, thereby regulate each other by making others free from miRNA regulation. Several computational tools have been published to infer ceRNA networks. In most existing tools, however, expression abundance sufficiency, collective regulation, and groupwise effect of ceRNAs are not considered. In this study, we developed a computational tool named Crinet to infer genome-wide ceRNA networks addressing critical drawbacks. Crinet considers all mRNAs, lncRNAs, and pseudogenes as potential ceRNAs and incorporates a network deconvolution method to exclude the spurious ceRNA pairs. We tested Crinet on breast cancer data in TCGA. Crinet inferred reproducible ceRNA interactions and groups, which were significantly enriched in the cancer-related genes and processes. We validated the selected miRNA-target interactions with the protein expression-based benchmarks and also evaluated the inferred ceRNA interactions predicting gene expression change in knockdown assays. The hub genes in the inferred ceRNA network included known suppressor/oncogene lncRNAs in breast cancer showing the importance of non-coding RNA's inclusion for ceRNA inference. Crinet-inferred ceRNA groups that were consistently involved in the immune system related processes could be important assets in the light of the studies confirming the relation between immunotherapy and cancer. The source code of Crinet is in R and available at https://github.com/bozdaglab/crinet.https://doi.org/10.1371/journal.pone.0251399
collection DOAJ
language English
format Article
sources DOAJ
author Ziynet Nesibe Kesimoglu
Serdar Bozdag
spellingShingle Ziynet Nesibe Kesimoglu
Serdar Bozdag
Crinet: A computational tool to infer genome-wide competing endogenous RNA (ceRNA) interactions.
PLoS ONE
author_facet Ziynet Nesibe Kesimoglu
Serdar Bozdag
author_sort Ziynet Nesibe Kesimoglu
title Crinet: A computational tool to infer genome-wide competing endogenous RNA (ceRNA) interactions.
title_short Crinet: A computational tool to infer genome-wide competing endogenous RNA (ceRNA) interactions.
title_full Crinet: A computational tool to infer genome-wide competing endogenous RNA (ceRNA) interactions.
title_fullStr Crinet: A computational tool to infer genome-wide competing endogenous RNA (ceRNA) interactions.
title_full_unstemmed Crinet: A computational tool to infer genome-wide competing endogenous RNA (ceRNA) interactions.
title_sort crinet: a computational tool to infer genome-wide competing endogenous rna (cerna) interactions.
publisher Public Library of Science (PLoS)
series PLoS ONE
issn 1932-6203
publishDate 2021-01-01
description To understand driving biological factors for complex diseases like cancer, regulatory circuity of genes needs to be discovered. Recently, a new gene regulation mechanism called competing endogenous RNA (ceRNA) interactions has been discovered. Certain genes targeted by common microRNAs (miRNAs) "compete" for these miRNAs, thereby regulate each other by making others free from miRNA regulation. Several computational tools have been published to infer ceRNA networks. In most existing tools, however, expression abundance sufficiency, collective regulation, and groupwise effect of ceRNAs are not considered. In this study, we developed a computational tool named Crinet to infer genome-wide ceRNA networks addressing critical drawbacks. Crinet considers all mRNAs, lncRNAs, and pseudogenes as potential ceRNAs and incorporates a network deconvolution method to exclude the spurious ceRNA pairs. We tested Crinet on breast cancer data in TCGA. Crinet inferred reproducible ceRNA interactions and groups, which were significantly enriched in the cancer-related genes and processes. We validated the selected miRNA-target interactions with the protein expression-based benchmarks and also evaluated the inferred ceRNA interactions predicting gene expression change in knockdown assays. The hub genes in the inferred ceRNA network included known suppressor/oncogene lncRNAs in breast cancer showing the importance of non-coding RNA's inclusion for ceRNA inference. Crinet-inferred ceRNA groups that were consistently involved in the immune system related processes could be important assets in the light of the studies confirming the relation between immunotherapy and cancer. The source code of Crinet is in R and available at https://github.com/bozdaglab/crinet.
url https://doi.org/10.1371/journal.pone.0251399
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