Nucleic Acids Res. 2013 Mar 1;41(5):e62. doi: 10.1093/nar/gks1439.

Widespread inference of weighted microRNA-mediated gene regulation in cancer transcriptome analysis.

Suzuki HI, Mihira H, Watabe T, Sugimoto K, Miyazono K.

Department of Molecular Pathology, Graduate School of Medicine, The University of Tokyo, 7-3-1 Hongo, Tokyo 113-0033, Japan.

 

Abstract

MicroRNAs (miRNAs) comprise a gene-regulatory network through sequence complementarity with target mRNAs. Previous studies have shown that mammalian miRNAs decrease many target mRNA levels and reduce protein production predominantly by target mRNA destabilization. However, it has not yet been fully assessed whether this scheme is widely applicable to more realistic conditions with multiple miRNA fluctuations. By combining two analytical frameworks for detecting the enrichment of gene sets, Gene Set Enrichment Analysis (GSEA) and Functional Assignment of miRNAs via Enrichment (FAME), we developed GSEA-FAME analysis (GFA), which enables the prediction of miRNA activities from mRNA expression data using rank-based enrichment analysis and weighted evaluation of miRNA-mRNA interactions. This cooperative approach delineated a better widespread correlation between miRNA expression levels and predicted miRNA activities in cancer transcriptomes, thereby providing proof-of-concept of the mRNA-destabilization scenario. In an integrative analysis of The Cancer Genome Atlas (TCGA) multidimensional data including profiles of both mRNA and miRNA, we also showed that GFA-based inference of miRNA activity could be used for the selection of prognostic miRNAs in the development of cancer survival prediction models. This approach proposes a next-generation strategy for the interpretation of miRNA function and identification of target miRNAs as biomarkers and therapeutic targets.

PMID: 23275554

 

Supplement

MicroRNAs (miRNAs) function as key regulators in fundamental biological processes (1). Many studies have shown miRNA dysregulation in diverse cancer types and links between altered miRNA activities and modification of gene regulatory networks in cancer. Several recent studies showed that mammalian miRNAs decrease many target mRNA levels and reduce protein production predominantly by destabilization of target mRNA, indicating the mRNA-destabilization (suppression) scenario. This mRNA-destabilization scenario might be utilized for the interpretation of miRNA function by analyzing transcriptome data.

In this study, we devised Gene Set Enrichment Analysis (GSEA)-Functional Assignment of miRNAs via Enrichment (FAME) analysis (GFA), which predicts miRNA activities from RNA expression data, using rank-based enrichment analysis and the evaluation of weighted miRNA-mRNA interactions (2). We combined GSEA and FAME to consider weak mRNA changes with biological relevance and the variability of the strength of correlations between a miRNA and its target genes. The cooperative strategy has yielded a better widespread correlation between miRNA expression levels and inferred miRNA activities in cancer transcriptomes. This analysis thus provided a proof-of-concept of the mRNA-destabilization scenario in which miRNAs decrease the expression levels of target mRNAs. In an analysis of The Cancer Genome Atlas (TCGA) multidimensional data, the combination of miRNA profiling and inference of miRNA activity by GFA-based transcriptome analysis was useful for the selection of prognostic miRNAs in the development of cancer survival prediction models. In addition, we recently applied GFA to the expression profiling of peripheral T cell lymphomas (PTCL) and showed that mRNA profiling can be utilized for the reverse identification of disease-related miRNAs and target identification of disease-related miRNAs (3).

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References:

  1. Suzuki, H. I. & Miyazono, K. Emerging complexity of microRNA generation cascades. J Biochem 149, 15-25 (2011)
  2. Suzuki, H. I., Mihira, H., Watabe, T., Sugimoto, K. & Miyazono, K. Widespread inference of weighted microRNA-mediated gene regulation in cancer transcriptome analysis. Nucleic Acids Res 41, e62 (2013)
  3. Suzuki, H. I. et al. Computational dissection of distinct microRNA activity signatures associated with peripheral T cell lymphoma subtypes. Leukemia 27, 2107-2111 (2013)

 

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