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

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.

[1]  Ash A. Alizadeh,et al.  Distinct types of diffuse large B-cell lymphoma identified by gene expression profiling , 2000, Nature.

[2]  D. Koller,et al.  A module map showing conditional activity of expression modules in cancer , 2004, Nature Genetics.

[3]  Ash A. Alizadeh,et al.  Prediction of survival in diffuse large-B-cell lymphoma based on the expression of six genes. , 2004, The New England journal of medicine.

[4]  J. Castle,et al.  Microarray analysis shows that some microRNAs downregulate large numbers of target mRNAs , 2005, Nature.

[5]  C. Burge,et al.  Conserved Seed Pairing, Often Flanked by Adenosines, Indicates that Thousands of Human Genes are MicroRNA Targets , 2005, Cell.

[6]  Pablo Tamayo,et al.  Gene set enrichment analysis: A knowledge-based approach for interpreting genome-wide expression profiles , 2005, Proceedings of the National Academy of Sciences of the United States of America.

[7]  Hans Lassmann,et al.  The Widespread Impact of Mammalian MicroRNAs on mRNA Repression and Evolution , 2005 .

[8]  Mihaela Zavolan,et al.  Inference of miRNA targets using evolutionary conservation and pathway analysis , 2007, BMC Bioinformatics.

[9]  D. Allison,et al.  Microarray data analysis: from disarray to consolidation and consensus , 2006, Nature Reviews Genetics.

[10]  V. Ambros,et al.  The regulation of genes and genomes by small RNAs , 2007, Development.

[11]  L. Lim,et al.  MicroRNA targeting specificity in mammals: determinants beyond seed pairing. , 2007, Molecular cell.

[12]  Joshua M. Korn,et al.  Comprehensive genomic characterization defines human glioblastoma genes and core pathways , 2008, Nature.

[13]  Martin M Matzuk,et al.  A bioinformatics tool for linking gene expression profiling results with public databases of microRNA target predictions. , 2008, RNA.

[14]  Sanghyuk Lee,et al.  miRGator: an integrated system for functional annotation of microRNAs , 2007, Nucleic Acids Res..

[15]  Chao Cheng,et al.  Inferring MicroRNA Activities by Combining Gene Expression with MicroRNA Target Prediction , 2008, PloS one.

[16]  Hiroshi I. Suzuki,et al.  Modulation of microRNA processing by p53 , 2009, Nature.

[17]  Robert L. Judson,et al.  Opposing microRNA families regulate self-renewal in mouse embryonic stem cells , 2010, Nature.

[18]  Hiroshi I. Suzuki,et al.  Dynamics of microRNA biogenesis: crosstalk between p53 network and microRNA processing pathway , 2010, Journal of Molecular Medicine.

[19]  R. Shamir,et al.  Towards computational prediction of microRNA function and activity , 2010, Nucleic acids research.

[20]  Michael T. McManus,et al.  Dicer1 and miR-219 Are Required for Normal Oligodendrocyte Differentiation and Myelination , 2010, Neuron.

[21]  M. Kimmel,et al.  Conflict of interest statement. None declared. , 2010 .

[22]  R. Taschereau,et al.  Rank–rank hypergeometric overlap: identification of statistically significant overlap between gene-expression signatures , 2010, Nucleic acids research.

[23]  Nicholas T. Ingolia,et al.  Mammalian microRNAs predominantly act to decrease target mRNA levels , 2010, Nature.

[24]  R. Sharan,et al.  Expander: from expression microarrays to networks and functions , 2010, Nature Protocols.

[25]  R. Agami,et al.  MicroRNA regulation by RNA-binding proteins and its implications for cancer , 2011, Nature Reviews Cancer.

[26]  Hyun Cheol Chung,et al.  A Densely Interconnected Genome-Wide Network of MicroRNAs and Oncogenic Pathways Revealed Using Gene Expression Signatures , 2011, PLoS genetics.

[27]  Ronald S Go,et al.  miRNA expression in diffuse large B-cell lymphoma treated with chemoimmunotherapy. , 2011, Blood.

[28]  Hiroshi I. Suzuki,et al.  MCPIP1 ribonuclease antagonizes dicer and terminates microRNA biogenesis through precursor microRNA degradation. , 2011, Molecular cell.

[29]  Zhongming Zhao,et al.  Uncovering MicroRNA and Transcription Factor Mediated Regulatory Networks in Glioblastoma , 2012, PLoS Comput. Biol..

[30]  A. Ballabio,et al.  Identification of microRNA-regulated gene networks by expression analysis of target genes , 2012, Genome research.

[31]  S. Chi,et al.  An alternative mode of microRNA target recognition , 2012, Nature Structural &Molecular Biology.

[32]  David Z. Chen,et al.  Architecture of the human regulatory network derived from ENCODE data , 2012, Nature.