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Inferring the Perturbed microRNA Regulatory Networks in Cancer Using Hierarchical Gene Co-Expression Signatures

MicroRNAs (miRNAs), a class of endogenous small regulatory RNAs, play important roles in many biological and physiological processes. The perturbations of some miRNAs, which are usually called as onco-microRNAs (onco-miRs), are significantly associated with multiple stages of cancer. Although hundre...

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Autores principales: Gu, Jin, Xuan, Zhenyu
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Public Library of Science 2013
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3835731/
https://www.ncbi.nlm.nih.gov/pubmed/24278370
http://dx.doi.org/10.1371/journal.pone.0081032
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author Gu, Jin
Xuan, Zhenyu
author_facet Gu, Jin
Xuan, Zhenyu
author_sort Gu, Jin
collection PubMed
description MicroRNAs (miRNAs), a class of endogenous small regulatory RNAs, play important roles in many biological and physiological processes. The perturbations of some miRNAs, which are usually called as onco-microRNAs (onco-miRs), are significantly associated with multiple stages of cancer. Although hundreds of miRNAs have been discovered, the perturbed miRNA regulatory networks and their functions are still poorly understood in cancer. Analyzing the expression patterns of miRNA target genes is a very useful strategy to infer the perturbed miRNA networks. However, due to the complexity of cancer transcriptome, current methods often encounter low sensitivity and report few onco-miR candidates. Here, we developed a new method, named miRHiC (enrichment analysis of miRNA targets in Hierarchical gene Co-expression signatures), to infer the perturbed miRNA regulatory networks by using the hierarchical co-expression signatures in large-scale cancer gene expression datasets. The method can infer onco-miR candidates and their target networks which are only linked to sub-clusters of the differentially expressed genes at fine scales of the co-expression hierarchy. On two real datasets of lung cancer and hepatocellular cancer, miRHiC uncovered several known onco-miRs and their target genes (such as miR-26, miR-29, miR-124, miR-125 and miR-200) and also identified many new candidates (such as miR-149, which is inferred in both types of cancers). Using hierarchical gene co-expression signatures, miRHiC can greatly increase the sensitivity for inferring the perturbed miRNA regulatory networks in cancer. All Perl scripts of miRHiC and the detailed documents are freely available on the web at http://bioinfo.au.tsinghua.edu.cn/member/jgu/miRHiC/.
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spelling pubmed-38357312013-11-25 Inferring the Perturbed microRNA Regulatory Networks in Cancer Using Hierarchical Gene Co-Expression Signatures Gu, Jin Xuan, Zhenyu PLoS One Research Article MicroRNAs (miRNAs), a class of endogenous small regulatory RNAs, play important roles in many biological and physiological processes. The perturbations of some miRNAs, which are usually called as onco-microRNAs (onco-miRs), are significantly associated with multiple stages of cancer. Although hundreds of miRNAs have been discovered, the perturbed miRNA regulatory networks and their functions are still poorly understood in cancer. Analyzing the expression patterns of miRNA target genes is a very useful strategy to infer the perturbed miRNA networks. However, due to the complexity of cancer transcriptome, current methods often encounter low sensitivity and report few onco-miR candidates. Here, we developed a new method, named miRHiC (enrichment analysis of miRNA targets in Hierarchical gene Co-expression signatures), to infer the perturbed miRNA regulatory networks by using the hierarchical co-expression signatures in large-scale cancer gene expression datasets. The method can infer onco-miR candidates and their target networks which are only linked to sub-clusters of the differentially expressed genes at fine scales of the co-expression hierarchy. On two real datasets of lung cancer and hepatocellular cancer, miRHiC uncovered several known onco-miRs and their target genes (such as miR-26, miR-29, miR-124, miR-125 and miR-200) and also identified many new candidates (such as miR-149, which is inferred in both types of cancers). Using hierarchical gene co-expression signatures, miRHiC can greatly increase the sensitivity for inferring the perturbed miRNA regulatory networks in cancer. All Perl scripts of miRHiC and the detailed documents are freely available on the web at http://bioinfo.au.tsinghua.edu.cn/member/jgu/miRHiC/. Public Library of Science 2013-11-20 /pmc/articles/PMC3835731/ /pubmed/24278370 http://dx.doi.org/10.1371/journal.pone.0081032 Text en © 2013 Gu, Xuan http://creativecommons.org/licenses/by/4.0/ 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 properly credited.
spellingShingle Research Article
Gu, Jin
Xuan, Zhenyu
Inferring the Perturbed microRNA Regulatory Networks in Cancer Using Hierarchical Gene Co-Expression Signatures
title Inferring the Perturbed microRNA Regulatory Networks in Cancer Using Hierarchical Gene Co-Expression Signatures
title_full Inferring the Perturbed microRNA Regulatory Networks in Cancer Using Hierarchical Gene Co-Expression Signatures
title_fullStr Inferring the Perturbed microRNA Regulatory Networks in Cancer Using Hierarchical Gene Co-Expression Signatures
title_full_unstemmed Inferring the Perturbed microRNA Regulatory Networks in Cancer Using Hierarchical Gene Co-Expression Signatures
title_short Inferring the Perturbed microRNA Regulatory Networks in Cancer Using Hierarchical Gene Co-Expression Signatures
title_sort inferring the perturbed microrna regulatory networks in cancer using hierarchical gene co-expression signatures
topic Research Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3835731/
https://www.ncbi.nlm.nih.gov/pubmed/24278370
http://dx.doi.org/10.1371/journal.pone.0081032
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