Identification of Potential MicroRNA Biomarkers by Meta-analysis

  • Hongmei Zhu
  • Siu-wai Leung
Protocol
First Online:
Part of the Methods in Molecular Biology book series (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.

Key words

microRNA Noncoding RNAs Meta-analysis Quality assessment Biomarkers Differential expression Early diagnosis 
This is a preview of subscription content, log in to check access

Supplementary material

421474_1_En_24_MOESM1_ESM.csv (1 kb)
Example dataset with dysregulated miRNAs

References

  1. 1.
    Koricheva J, Gurevitch J (2013) Place of meta-analysis among other methods of research synthesis. In: Koricheva J, Gurevitch J, Mengersen K (eds) Handbook of meta-analysis in ecology and evolution. Princeton University PressGoogle Scholar
  2. 2.
    Borenstein M, Hedges LV, Higgins JPT, Rothstein HR (2009) Introduction to meta-analysis. John Wiley & Sons, Ltd., Chichester. https://doi.org/10.1002/9780470743386 CrossRefGoogle Scholar
  3. 3.
    Evangelou E, Ioannidis JPA (2013) Meta-analysis methods for genome-wide association studies and beyond. Nat Rev Genet 14:379–389. https://doi.org/10.1038/nrg3472 CrossRefPubMedGoogle Scholar
  4. 4.
    Levinson DF (2005) Meta-analysis in psychiatric genetics. Curr Psychiatry Rep 7:143–151. https://doi.org/10.1007/s11920-005-0012-9 CrossRefPubMedGoogle Scholar
  5. 5.
    Borenstein M (2009) Effect sizes for continuous data. In: Cooper H, Hedges LV, Valentine JC (eds) The handbook of research synthesis and meta-analysis. Russell Sage FoundationGoogle Scholar
  6. 6.
    Tseng GC, Ghosh D, Feingold E (2012) Comprehensive literature review and statistical considerations for microarray meta-analysis. Nucleic Acids Res 40:3785–3799. https://doi.org/10.1093/nar/gkr1265 CrossRefPubMedPubMedCentralGoogle Scholar
  7. 7.
    Zhu H, Leung SW (2015) Identification of microRNA biomarkers in type 2 diabetes: a meta-analysis of controlled profiling studies. Diabetologia 58:900–911. https://doi.org/10.1007/s00125-015-3510-2 CrossRefPubMedGoogle Scholar
  8. 8.
    Moher D, Liberati A, Tetzlaff J et al (2009) Preferred reporting items for systematic reviews and meta-analyses: the PRISMA statement (reprinted from annals of internal medicine). Phys Ther 89:873–880. https://doi.org/10.1371/journal.pmed.1000097 PubMedGoogle Scholar
  9. 9.
    Minimum information about a microarray experiment – MIAME. http://www.mged.org/Workgroups/MIAME/miame_2.0.html
  10. 10.
    Kahn SE (2001) Clinical review 135: the importance of beta-cell failure in the development and progression of type 2 diabetes. J Clin Endocrinol Metab 86:4047–4058PubMedGoogle Scholar
  11. 11.
    Karolina DS, Armugam A, Sepramaniam S, Jeyaseelan K (2012) MiRNAs and diabetes mellitus. Expert Rev Endocrinol Metab 7:281–300. https://doi.org/10.1586/eem.12.21 CrossRefGoogle Scholar
  12. 12.
    Winer N, Sowers JR (2004) Epidemiology of diabetes. J Clin Pharmacol 44:397–405. https://doi.org/10.1177/0091270004263017 CrossRefPubMedGoogle Scholar
  13. 13.
    International Diabetes Federation (IDF) (2015) IDF Diabetes Atlas, 7th edn. idf.org. doi:https://doi.org/10.1289/image.ehp.v119.i03
  14. 14.
    Shantikumar S, Caporali A, Emanueli C (2012) Role of microRNAs in diabetes and its cardiovascular complications. Cardiovasc Res 93:583–593. https://doi.org/10.1093/cvr/cvr300 CrossRefPubMedGoogle Scholar
  15. 15.
    Rodriguez A, Griffiths-Jones S, Ashurst JL, Bradley A (2004) Identification of mammalian microRNA host genes and transcription units. Genome Res 14(10):1902. https://doi.org/10.1101/gr.2722704 CrossRefPubMedPubMedCentralGoogle Scholar
  16. 16.
    Lee Y, Kim M, Han J et al (2004) MicroRNA genes are transcribed by RNA polymerase II. EMBO J 23:4051–4060. https://doi.org/10.1038/sj.emboj.7600385 CrossRefPubMedPubMedCentralGoogle Scholar
  17. 17.
    He L, Hannon GJ (2004) MicroRNAs: small RNAs with a big role in gene regulation. Nat Rev Genet 5:522–531. https://doi.org/10.1038/nrg1379 CrossRefPubMedGoogle Scholar
  18. 18.
    Bartel DP (2004) MicroRNAs: genomics, biogenesis, mechanism, and function. Cell 116:281–297. https://doi.org/10.1016/S0092-8674(04)00045-5 CrossRefPubMedGoogle Scholar
  19. 19.
    Arroyo JD, Chevillet JR, Kroh EM et al (2011) Argonaute2 complexes carry a population of circulating microRNAs independent of vesicles in human plasma. Proc Natl Acad Sci U S A 108:5003–5008. https://doi.org/10.1073/pnas.1019055108 CrossRefPubMedPubMedCentralGoogle Scholar
  20. 20.
    Vickers KC, Palmisano BT, Shoucri BM et al (2011) MicroRNAs are transported in plasma and delivered to recipient cells by high-density lipoproteins. Nat Cell Biol 13:423–433. https://doi.org/10.1038/ncb2210 CrossRefPubMedPubMedCentralGoogle Scholar
  21. 21.
    Gibbings DJ, Ciaudo C, Erhardt M, Voinnet O (2009) Multivesicular bodies associate with components of miRNA effector complexes and modulate miRNA activity. Nat Cell Biol 11:1143–1149. https://doi.org/10.1038/ncb1929 CrossRefPubMedGoogle Scholar
  22. 22.
    Suárez Y, Sessa WC (2009) MicroRNAs as novel regulators of angiogenesis. Circ Res 104:442–454. https://doi.org/10.1161/CIRCRESAHA.108.191270 CrossRefPubMedPubMedCentralGoogle Scholar
  23. 23.
    Carrington JC, Ambros V (2003) Role of microRNAs in plant and animal development. Science 301:336–338. https://doi.org/10.1126/science.1085242 CrossRefPubMedGoogle Scholar
  24. 24.
    Fabian MR, Sonenberg N, Filipowicz W (2010) Regulation of mRNA translation and stability by microRNAs. Annu Rev Biochem 79:351–379. https://doi.org/10.1146/annurev-biochem-060308-103103 CrossRefPubMedGoogle Scholar
  25. 25.
    Bagge A, Clausen TR, Larsen S et al (2012) MicroRNA-29a is up-regulated in beta-cells by glucose and decreases glucose-stimulated insulin secretion. Biochem Biophys Res Commun 426:266–272. https://doi.org/10.1016/j.bbrc.2012.08.082 CrossRefPubMedGoogle Scholar
  26. 26.
    Karolina DS, Tavintharan S, Armugam A et al (2012) Circulating miRNA profiles in patients with metabolic syndrome. J Clin Endocrinol Metab 97:E2271–E2276. https://doi.org/10.1210/jc.2012-1996 CrossRefPubMedGoogle Scholar
  27. 27.
    Locke JM, Harries LW (2012) MicroRNA expression profiling of human islets from individuals with and without type 2 diabetes: promises and pitfalls. Biochem Soc Trans 40:800–803. https://doi.org/10.1042/BST20120049 CrossRefPubMedGoogle Scholar
  28. 28.
    Liu J, Liu W, Ying H et al (2012) Analysis of microRNA expression profile induced by AICAR in mouse hepatocytes. Gene 512:364–372. https://doi.org/10.1016/j.gene.2012.09.118 CrossRefPubMedGoogle Scholar
  29. 29.
    Begley CG, Ellis LM (2012) Raise standards for preclinical cancer research. Nature 483:531–533. https://doi.org/10.1038/483531a CrossRefPubMedGoogle Scholar
  30. 30.
    Prinz F, Schlange T, Asadullah K (2011) Believe it or not: how much can we rely on published data on potential drug targets? Nat Rev Drug Discov 10:712. https://doi.org/10.1038/nrd3439-c1 CrossRefPubMedGoogle Scholar
  31. 31.
    Mobley A, Linder SK, Braeuer R et al (2013) A survey on data reproducibility in cancer research provides insights into our limited ability to translate findings from the laboratory to the clinic. PLoS One 8:e63221. https://doi.org/10.1371/journal.pone.0063221 CrossRefPubMedPubMedCentralGoogle Scholar
  32. 32.
    Guay C, Roggli E, Nesca V et al (2011) Diabetes mellitus, a microRNA-related disease? Transl Res 157:253–264. https://doi.org/10.1016/j.trsl.2011.01.009 CrossRefPubMedGoogle Scholar
  33. 33.
    Guay C, Jacovetti C, Nesca V (2012) Emerging roles of non-coding RNAs in pancreatic β-cell function and dysfunction. Diabetes Obes Metab 14:12–21CrossRefPubMedGoogle Scholar
  34. 34.
    Hamar P (2012) Role of regulatory microRNAs in type 2 diabetes mellitus related inflammation. Nucleic Acids Ther 22:289–294Google Scholar
  35. 35.
    McClelland AD, Kantharidis P (2014) MicroRNA in the development of diabetic complications. Clin Sci (Lond) 126:95–110. https://doi.org/10.1042/CS20130079 CrossRefGoogle Scholar
  36. 36.
    Natarajan R, Putta S, Kato M (2012) MicroRNAs and diabetic complications. J Cardiovasc Transl Res 5:413–422CrossRefPubMedPubMedCentralGoogle Scholar
  37. 37.
    Viechtbauer W (2010) Conducting meta-analyses in R with the metafor package. J Stat Softw 36:1–48CrossRefGoogle Scholar
  38. 38.
    Centre for Reviews and Dissemination (2009) Systematic reviews: CRDs guidance for undertaking reviews in health care. Cent Rev Dissemination, Univ York, 2008. https://doi.org/10.1017/CBO9781107415324.004
  39. 39.
    Park N, Zhou H, Elashoff D (2009) Salivary microRNA: discovery, characterization, and clinical utility for oral cancer detection. Clin Cancer Res 15:5473–5477CrossRefPubMedPubMedCentralGoogle Scholar
  40. 40.
    Hanke M, Hoefig K, Merz H et al (2010) A robust methodology to study urine microRNA as tumor marker: microRNA-126 and microRNA-182 are related to urinary bladder cancer. Urol Oncol 28:655–661. https://doi.org/10.1016/j.urolonc.2009.01.027 CrossRefPubMedGoogle Scholar
  41. 41.
    Weber JA, Baxter DH, Zhang S et al (2010) The microRNA spectrum in 12 body fluids. Clin Chem 56:1733–1741CrossRefPubMedPubMedCentralGoogle Scholar
  42. 42.
    Yamada Y, Enokida H, Kojima S et al (2011) MiR-96 and miR-183 detection in urine serve as potential tumor markers of urothelial carcinoma: correlation with stage and grade, and comparison with urinary cytology. Cancer Sci 102:522–529. https://doi.org/10.1111/j.1349-7006.2010.01816.x CrossRefPubMedGoogle Scholar
  43. 43.
    Bustin S, Benes V, Garson J et al (2009) The MIQE guidelines: minimum information for publication of quantitative real-time PCR experiments. Clin Chem 55:611–622. https://doi.org/10.1373/clinchem.2008.112797 CrossRefPubMedGoogle Scholar
  44. 44.
    R Core Team (2015) R: A language and environment for statistical computing. R Found Stat Comput, Vienna, Austria 0:{ISBN} 3-900051-07-0. doi:ISBN 3-900051-07-0Google Scholar

Copyright information

© Springer Science+Business Media, LLC, part of Springer Nature 2018

Authors and Affiliations

  • Hongmei Zhu
    • 1
  • Siu-wai Leung
    • 1
    • 2
  1. 1.State Key Laboratory of Quality Research in Chinese Medicine, Institute of Chinese Medical SciencesUniversity of MacauTaipaChina
  2. 2.School of InformaticsUniversity of EdinburghEdinburghUK

Personalised recommendations