4.8 Article

Biclustering analysis of transcriptome big data identifies condition-specific microRNA targets

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NUCLEIC ACIDS RESEARCH
卷 47, 期 9, 页码 -

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OXFORD UNIV PRESS
DOI: 10.1093/nar/gkz139

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资金

  1. National Research Foundation (NRF) of Korea, Genomics Program [2016M3C9A3945893]
  2. Basic Science Research Program (NRF) [2017R1E1A1A03070107, NRF-2018R1A5A1024340]
  3. Bio-Synergy Research Project [NRF-2017M3A9C4065956]
  4. NRF [NRF-2016M3C9A3945893]
  5. National Research Foundation of Korea [2017R1E1A1A03070107] Funding Source: Korea Institute of Science & Technology Information (KISTI), National Science & Technology Information Service (NTIS)

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We present a novel approach to identify human microRNA (miRNA) regulatory modules (mRNA targets and relevant cell conditions) by biclustering a large collection of mRNA fold-change data for sequence-specific targets. Bicluster targets were assessed using validated messenger RNA (mRNA) targets and exhibited on an average 17.0% (median improved gain in certainty (sensitivity + specificity). The net gain was further increased up to 32.0% (median 33.4%) by incorporating functional networks of targets. We analyzed cancer-specific biclusters and found that the PI3K/Akt signaling pathway is strongly enriched with targets of a few miRNAs in breast cancer and diffuse large B-cell lymphoma. Indeed, five independent prognostic miRNAs were identified, and repression of bicluster targets and pathway activity by miR-29 was experimentally validated. In total, 29 898 biclusters for 459 human miRNAs were collected in the BiMIR database where biclusters are searchable for miRNAs, tissues, diseases, keywords and target genes.

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