The DOI matches its Crossref record; 49 of the 49 references it cites were found in a registry or an index.
Small sample sizes in high-throughput miRNA screens: A common pitfall for the identification of miRNA biomarkers
Open accessAbstract
Since the discovery of microRNAs (miRNAs), circulating miRNAs have been proposed as biomarkers for disease. Consequently, many groups have tried to identify circulating miRNA biomarkers for various types of diseases including cardiovascular disease and cancer. However, the replicability of these experiments has been disappointingly low. In order to identify circulating miRNA candidate biomarkers, in general, first an unbiased high-throughput screen is performed in which a large number of miRNAs is detected and quantified in the circulation. Because these are costly experiments, many of such studies have been performed using a low number of study subjects (small sample size). Due to lack of power in small sample size experiments, true effects are often missed and many of the detected effects are wrong. Therefore, it is important to have a good estimate of the appropriate sample size for a miRNA high-throughput screen. In this review, we discuss the effects of small sample sizes in high-throughput screens for circulating miRNAs. Using data from a miRNA high-throughput experiment on isolated monocytes, we illustrate that the implementation of power calculations in a high-throughput miRNA discovery experiment will avoid unnecessarily large and expensive experiments, while still having enough power to be able to detect clinically important differences.
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“Kok et al. (2018) notes, for example, that one may need around 19 samples per group to detect a fold change of 1.5 in microRNA data at 80% power and false discovery rate <10%.”
From Missing data in multi-omics integration: Recent advances through artificial intelligence (2023) · cites this paper as Kok et al., 2018
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Bergel, C. C., Yolgösteren, A., Eryılmaz, I. E., Egeli, U., Koşukçu, C., Ozkaya, G., Gürsel, B. E., Biçer, M., Çeçener, G., ÖZOĞLU, E., et al. (2026) · Current Genomics · doi:10.2174/0113892029361154251029052509
Евгеньевна, Т. Е., Yakimov, A. M., Vasilieva, A. A., Константинович, Р. Ф., Дмитриевна, Р. А., Попов, А. А., & Дмитренко, Д. В. (2026) · Neuroscience · doi:10.1016/j.neuroscience.2026.05.014
MIAT: An important long non-coding RNA regulating cardiovascular and cerebrovascular diseases
Pan, Y., Yu, W. J., Han, Y., & Shao, Y. (2026) · Frontiers in Cardiovascular Medicine · doi:10.3389/fcvm.2026.1791657
Silva, T. K. D., Stenos, J., Graves, S., Bhatta, T. R., Unsworth, N. B., Farr, R. J., Stewart, C. R., & Firestone, S. M. (2026) · International Journal of Molecular Sciences · doi:10.3390/ijms27198444
Metadata
- Authors
- Kok, M. G. M., de Ronde, M. W. J., Moerland, P. D., Ruijter, J. M., Creemers, E. E., & Pinto-Sietsma, S. J.
- Affiliations
- University of Amsterdam (NL)
- Amsterdam UMC Location University of Amsterdam (NL)
- Venue
- Biomolecular Detection and Quantification
- Publisher
- Elsevier BV
- Published
- 2018-05
- Volume
- 15
- Pages
- 1-5
- ISSN
- 2214-7535 (ISSN-L)
- PMC
- PMC5737945
- OpenAlex
- W2776332611
- Cited by
- 105
- References
- 49
- Citations by year
- 2026: 5 · 2025: 15 · 2024: 18 · 2023: 9 · 2022: 17 · 2021: 13 · 2020: 16 · 2019: 7 · 2018: 5
- FWCI
- 2.57
- Type
- journal article
- Language
- en
- Open access
- hybrid
- doi.org
- Licence
- Locations
- Biomolecular Detection and Quantification (publishedVersion, cc-by-nc-nd)
- PubMed Central (publishedVersion, cc-by-nc-nd)
- PubMed (publishedVersion)
- Data Archiving and Networked Services (DANS) (submittedVersion)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion)
- Pure Amsterdam UMC (submittedVersion)
- Topics
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Cancer Research › MicroRNA in disease regulation
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Gene expression and cancer classification
- Physical Sciences › Mathematics › Statistics and Probability › Statistical Methods in Epidemiology
- Keywords
- microRNA biomarkers, circulating microRNAs, small sample size, statistical power, sample size determination, power analysis, replicability, microRNA discovery
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