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Identification of Potential MicroRNA Biomarkers by Meta-analysis

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Computational Drug Discovery and Design

Part of the book series: Methods in Molecular Biology ((MIMB,volume 1762))

Abstract

Meta-analysis statistically assesses the results (e.g., effect sizes) across independent studies that are conducted in accordance with similar protocols and objectives. Current genomic meta-analysis studies do not perform extensive re-analysis on raw data because full data access would not be commonplace, although the best practice of open research for sharing well-formed data have been actively advocated. This chapter describes a simple and easy-to-follow method for conducting meta-analysis of multiple studies without using raw data. Examples for meta-analysis of microRNAs (miRNAs) are provided to illustrate the method. MiRNAs are potential biomarkers for early diagnosis and epigenetic monitoring of diseases. A number of miRNAs have been identified to be differentially expressed, i.e., overexpressed or underexpressed, under diseased states but only a small fraction would be highly effective biomarkers or therapeutic targets of diseases. The meta-analysis method as described in this chapter aims to identify the miRNAs that are consistently found dysregulated across independent studies as biomarkers.

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Author information

Authors and Affiliations

  1. State Key Laboratory of Quality Research in Chinese Medicine, Institute of Chinese Medical Sciences, University of Macau, Taipa, Macao, China

    Hongmei Zhu & Siu-wai Leung

  2. School of Informatics, University of Edinburgh, Edinburgh, UK

    Siu-wai Leung

Authors
  1. Hongmei Zhu
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  2. Siu-wai Leung
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Editor information

Editors and Affiliations

  1. Department of Basic and Applied Sciences, Dayananda Sagar University, Bangalore, KA, India

    Mohini Gore

  2. Department of Biotechnology, Shivaji University, Kolhapur, MH, India

    Umesh B. Jagtap

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Zhu, H., Leung, Sw. (2018). Identification of Potential MicroRNA Biomarkers by Meta-analysis. In: Gore, M., Jagtap, U. (eds) Computational Drug Discovery and Design. Methods in Molecular Biology, vol 1762. Humana Press, New York, NY. https://doi.org/10.1007/978-1-4939-7756-7_24

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  • DOI: https://doi.org/10.1007/978-1-4939-7756-7_24

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  • Publisher Name: Humana Press, New York, NY

  • Print ISBN: 978-1-4939-7755-0

  • Online ISBN: 978-1-4939-7756-7

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