Abstract
Differential microRNA (miRNA) expression profiling by high-throughput methods has generated a vast amount of information about the complex role of these small regulatory molecules in a broad spectrum of human diseases. However, the results of such studies are often inconsistent, mostly due to the lack of cross-platform standardization, ongoing discovery of novel miRNAs, and small sample size. Therefore, a critical and systematic analysis of all available information is essential for successful identification of the most relevant miRNAs. Meta-analysis approach allows integrating the results from several independent studies in order to achieve greater statistical power and estimate the variability between the studies. Here we describe as an example the use of a robust rank aggregation (RRA) method for identification of miRNA meta-signature in lung cancer. This method analyzes prioritized gene lists and finds commonly overlapping genes, which are ranked consistently better than expected by chance. An RRA approach not only helps to prioritize the putative targets for further experimental studies but also highlights the challenges related with the development of miRNA-based tests and emphasizes the need for rigorous evaluation of the results before proceeding to clinical trials.
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References
Esteller M (2011) Non-coding RNAs in human disease. Nat Rev Genet 12:861–874
Meyer SU, Kaiser S, Wagner C et al (2012) Profound effect of profiling platform and normalization strategy on detection of differentially expressed microRNAs—a comparative study. PLoS One 7:e38946
Morin RD, O’Connor MD, Griffith M et al (2008) Application of massively parallel sequencing to microRNA profiling and discovery in human embryonic stem cells. Genome Res 18:610–621
Ryu S, Joshi N, McDonnell K et al (2011) Discovery of novel human breast cancer microRNAs from deep sequencing data by analysis of pri-microRNA secondary structures. PLoS One 6:e16403
Pradervand S, Weber J, Thomas J et al (2009) Impact of normalization on miRNA microarray expression profiling. RNA 15:493–501
Kolde R, Laur S, Adler P et al (2012) Robust rank aggregation for gene list integration and meta-analysis. Bioinformatics 28:573–580
Vosa U, Vooder T, Kolde R et al (2013) Meta-analysis of microRNA expression in lung cancer. Int J Cancer 132:2884–2893
Kozomara A, Griffiths-Jones S (2011) miRBase: integrating microRNA annotation and deep-sequencing data. Nucleic Acids Res 39: D152–D157
Ashburner M, Ball CA, Blake JA et al (2000) Gene ontology: tool for the unification of biology. The Gene Ontology Consortium. Nat Genet 25:25–29
Vlachos IS, Kostoulas N, Vergoulis T et al (2012) DIANA miRPath v. 2.0: investigating the combinatorial effect of microRNAs in pathways. Nucleic Acids Res 40:W498–W504
Kowarsch A, Preusse M, Marr C et al (2011) miTALOS: analyzing the tissue-specific regulation of signaling pathways by human and mouse microRNAs. RNA 17:809–819
Yang JH, Li JH, Shao P et al (2011) starBase: a database for exploring microRNA-mRNA interaction maps from Argonaute CLIP-Seq and Degradome-Seq data. Nucleic Acids Res 39:D202–D209
Hsu SD, Lin FM, Wu WY et al (2011) miRTarBase: a database curates experimentally validated microRNA-target interactions. Nucleic Acids Res 39:D163–D169
Backes C, Keller A, Kuentzer J et al (2007) GeneTrail—advanced gene set enrichment analysis. Nucleic Acids Res 35:W186–W192
Lewis BP, Burge CB, Bartel DP (2005) Conserved seed pairing, often flanked by adenosines, indicates that thousands of human genes are microRNA targets. Cell 120:15–20
John B, Enright AJ, Aravin A et al (2004) Human MicroRNA targets. PLoS Biol 2: e363
Kertesz M, Iovino N, Unnerstall U et al (2007) The role of site accessibility in microRNA target recognition. Nat Genet 39:1278–1284
Krek A, Grun D, Poy MN et al (2005) Combinatorial microRNA target predictions. Nat Genet 37:495–500
Kiriakidou M, Nelson PT, Kouranov A et al (2004) A combined computational-experimental approach predicts human microRNA targets. Genes Dev 18:1165–1178
Rehmsmeier M, Steffen P, Hochsmann M et al (2004) Fast and effective prediction of microRNA/target duplexes. RNA 10:1507–1517
Dweep H, Sticht C, Pandey P et al (2011) miRWalk—database: prediction of possible miRNA binding sites by “walking” the genes of three genomes. J Biomed Inform 44:839–847
Sethupathy P, Corda B, Hatzigeorgiou AG (2006) TarBase: a comprehensive database of experimentally supported animal microRNA targets. RNA 12:192–197
da Huang W, Sherman BT, Lempicki RA (2009) Systematic and integrative analysis of large gene lists using DAVID bioinformatics resources. Nat Protoc 4:44–57
Reimand J, Arak T, Vilo J (2011) g:Profiler—a web server for functional interpretation of gene lists (2011 update). Nucleic Acids Res 39:W307–W315
Guan P, Yin Z, Li X et al (2012) Meta-analysis of human lung cancer microRNA expression profiling studies comparing cancer tissues with normal tissues. J Exp Clin Cancer Res 31:54
Acknowledgments
This research was financed by the University of Tartu (grant “Center of Translational Genomics”), the Estonian Government (grant #SF0180142s08), the European Commission through the European Regional Development Fund in the frame of grant “Centre of Excellence in Genomics,” Estonian Research Infrastructures Roadmap, and FP7 grant #313010.
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Võsa, U., Kolde, R., Vilo, J., Metspalu, A., Annilo, T. (2014). Comprehensive Meta-analysis of MicroRNA Expression Using a Robust Rank Aggregation Approach. In: Alvarez, M., Nourbakhsh, M. (eds) RNA Mapping. Methods in Molecular Biology, vol 1182. Humana Press, New York, NY. https://doi.org/10.1007/978-1-4939-1062-5_28
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DOI: https://doi.org/10.1007/978-1-4939-1062-5_28
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