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Inferring probabilistic miRNA-mRNA interaction signatures in cancers: a role-switch approach

Aberrant microRNA (miRNA) expression is implicated in tumorigenesis. The underlying mechanisms are unclear because the regulations of each miRNA on potentially hundreds of mRNAs are sample specific. We describe a novel approach to infer Probabilistic MiRNA-mRNA Interaction Signature ('ProMISe') from... Full description

Journal Title: Nucleic acids research May 2014, Vol.42(9), pp.e76
Main Author: Li, Yue
Other Authors: Liang, Cheng , Wong, Ka-Chun , Jin, Ke , Zhang, Zhaolei
Format: Electronic Article Electronic Article
Language: English
Subjects:
ID: E-ISSN: 1362-4962 ; PMID: 24609385 Version:1 ; DOI: 10.1093/nar/gku182
Link: http://pubmed.gov/24609385
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recordid: medline24609385
title: Inferring probabilistic miRNA-mRNA interaction signatures in cancers: a role-switch approach
format: Article
creator:
  • Li, Yue
  • Liang, Cheng
  • Wong, Ka-Chun
  • Jin, Ke
  • Zhang, Zhaolei
subjects:
  • Models, Genetic
  • Breast Neoplasms -- Metabolism
  • Micrornas -- Metabolism
  • RNA, Messenger -- Metabolism
  • Thyroid Neoplasms -- Metabolism
ispartof: Nucleic acids research, May 2014, Vol.42(9), pp.e76
description: Aberrant microRNA (miRNA) expression is implicated in tumorigenesis. The underlying mechanisms are unclear because the regulations of each miRNA on potentially hundreds of mRNAs are sample specific. We describe a novel approach to infer Probabilistic MiRNA-mRNA Interaction Signature ('ProMISe') from a single pair of miRNA-mRNA expression profile. Our model considers mRNA and miRNA competition as a probabilistic function of the expressed seeds (matches). To demonstrate ProMISe, we extensively exploited The Cancer Genome Atlas data. As a target predictor, ProMISe identifies more confidence/validated targets than other methods. Importantly, ProMISe confers higher cancer diagnostic power than using expression profiles alone. Gene set enrichment analysis on averaged ProMISe uniquely revealed respective target enrichments of oncomirs miR-21 and 145 in glioblastoma and ovarian cancers. Moreover, comparing matched breast (BRCA) and thyroid (THCA) tumor/normal samples uncovered thousands of tumor-related...
language: eng
source:
identifier: E-ISSN: 1362-4962 ; PMID: 24609385 Version:1 ; DOI: 10.1093/nar/gku182
fulltext: fulltext
issn:
  • 13624962
  • 1362-4962
url: Link


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titleInferring probabilistic miRNA-mRNA interaction signatures in cancers: a role-switch approach
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descriptionAberrant microRNA (miRNA) expression is implicated in tumorigenesis. The underlying mechanisms are unclear because the regulations of each miRNA on potentially hundreds of mRNAs are sample specific. We describe a novel approach to infer Probabilistic MiRNA-mRNA Interaction Signature ('ProMISe') from a single pair of miRNA-mRNA expression profile. Our model considers mRNA and miRNA competition as a probabilistic function of the expressed seeds (matches). To demonstrate ProMISe, we extensively exploited The Cancer Genome Atlas data. As a target predictor, ProMISe identifies more confidence/validated targets than other methods. Importantly, ProMISe confers higher cancer diagnostic power than using expression profiles alone. Gene set enrichment analysis on averaged ProMISe uniquely revealed respective target enrichments of oncomirs miR-21 and 145 in glioblastoma and ovarian cancers. Moreover, comparing matched breast (BRCA) and thyroid (THCA) tumor/normal samples uncovered thousands of tumor-related...
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abstractAberrant microRNA (miRNA) expression is implicated in tumorigenesis. The underlying mechanisms are unclear because the regulations of each miRNA on potentially hundreds of mRNAs are sample specific. We describe a novel approach to infer Probabilistic MiRNA-mRNA Interaction Signature ('ProMISe') from a single pair of miRNA-mRNA expression profile. Our model considers mRNA and miRNA competition as a probabilistic function of the expressed seeds (matches). To demonstrate ProMISe, we extensively exploited The Cancer Genome Atlas data. As a target predictor, ProMISe identifies more confidence/validated targets than other methods. Importantly, ProMISe confers higher cancer diagnostic power than using expression profiles alone. Gene set enrichment analysis on averaged ProMISe uniquely revealed respective target enrichments of oncomirs miR-21 and 145 in glioblastoma and ovarian cancers. Moreover, comparing matched breast (BRCA) and thyroid (THCA) tumor/normal samples uncovered thousands of tumor-related...
doi10.1093/nar/gku182
pmid24609385
date2014-05