Faculty of Mechanics and Mathematics, Lomonosov Moscow State University, Leninskie Gory 1, 119991 Moscow, Russia.
P.A. Hertsen Moscow Oncology Research Center, Branch of National Medical Research Radiological Center, Ministry of Health of the Russian Federation, Second Botkinsky lane 3, 125284 Moscow, Russia.
Int J Mol Sci. 2020 Feb 12;21(4):1228. doi: 10.3390/ijms21041228.
One of the main disadvantages of using DNA microarrays for miRNA expression profiling is the inability of adequate comparison of expression values across different miRNAs. This leads to a large amount of miRNAs with high scores which are actually not expressed in examined samples, i.e., false positives. We propose a post-processing algorithm which performs scoring of miRNAs in the results of microarray analysis based on expression values, time of discovery of miRNA, and correlation level between the expressions of miRNA and corresponding pre-miRNA in considered samples. The algorithm was successfully validated by the comparison of the results of its application to miRNA microarray breast tumor samples with publicly available miRNA-seq breast tumor data. Additionally, we obtained possible reasons why miRNA can appear as a false positive in microarray study using paired miRNA sequencing and array data. The use of DNA microarrays for estimating miRNA expression profile is limited by several factors. One of them consists of problems with comparing expression values of different miRNAs. In this work, we show that situation can be significantly improved if some additional information is taken into consideration in a comparison.
使用 DNA 微阵列进行 miRNA 表达谱分析的主要缺点之一是无法充分比较不同 miRNA 的表达值。这导致大量得分较高的 miRNA 实际上在检查样本中没有表达，即假阳性。我们提出了一种后处理算法，该算法基于 miRNA 在微阵列分析结果中的表达值、miRNA 的发现时间以及考虑样本中 miRNA 和相应前体 miRNA 表达之间的相关水平来对 miRNA 进行评分。通过将其应用于 miRNA 微阵列乳腺癌样本的结果与公开的 miRNA-seq 乳腺癌数据进行比较，成功验证了该算法。此外，我们还获得了使用配对 miRNA 测序和阵列数据进行微阵列研究时 miRNA 出现假阳性的可能原因。使用 DNA 微阵列估计 miRNA 表达谱受到几个因素的限制。其中之一是比较不同 miRNA 的表达值存在问题。在这项工作中，我们表明如果在比较中考虑一些额外的信息，情况可以得到显著改善。
