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Genome-wide circulating microRNA expression profiling indicates biomarkers for epilepsy

Wang, Jun, Yu, Jin-Tai, Tan, Lin, Tian, Yan, Ma, Jing, Tan, Chen-Chen, Wang, Hui-Fu, Liu, Ying, Tan, Meng-Shan, Jiang, Teng, Tan, Lan·2015·Scientific Reports
DOI 10.1038/srep09522PMC PMC4379481
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Abstract

MicroRNAs (miRNAs) have been proposed as biomarkers for cancer and other diseases due to their stability in serum. In epilepsy, miRNAs have almost been studied in brain tissues and in animals' circulation, but not in circulation of human. To date, a major challenge is to develop biomarkers to improve the current diagnosis of epilepsy. The aim of this study was to evaluate whether circulating miRNAs can be used as biomarkers for epilepsy. We measured the differences in serum miRNA levels between 30 epilepsy patients and 30 healthy controls in discovery and training phases using Illumina HiSeq2000 sequencing followed by quantitative reverse transcriptase polymerase chain reaction (qRT-PCR) assays. The selected miRNAs were then validated in 117 epilepsy patients and 112 healthy controls by qRT-PCR. Let-7d-5p, miR-106b-5p, -130a-3p and -146a-5p were found up-regulated, whereas miR-15a-5p and -194-5p were down-regulated in epilepsy patients compared to controls (P < 0.0001). Among these miRNAs, miR-106b-5p had the best diagnostic value for epilepsy with 80.3% sensitivity and 81.2% specificity. Circulating miRNAs were differentially regulated in epilepsy patients as compared with controls. MiR-106b-5p may serve as a novel, noninvasive biomarker to improve the current diagnosis of epilepsy.

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Figure 1 The circulating miRNAs signatures identified by Illumina Hiseq2000 sequencing. The length distribution and frequency percentages of the sequences identified in healthy controls (A) and epilepsy patients (B); RNA species in healthy controls (C) and epilepsy patients (D); and RNA read counts in healthy controls (E) and epilepsy patients (F).
Figure 1 The circulating miRNAs signatures identified by Illumina Hiseq2000 sequencing. The length distribution and frequency percentages of the sequences identified in healthy controls (A) and epilepsy patients (B); RNA species in healthy controls (C) and epilepsy patients (D); and RNA read counts in healthy controls (E) and epilepsy patients (F).CC BY · PubMed Central
Figure 2 Differential expression levels of significant miRNAs in training phase. Expression levels of the miRNAs were normalized to spiked-in cel-miR-39 and were calculated utilizing the 2−ΔΔCt method. Mann-Whitney U test was used to determine statistical significance. The black dots and stars represent the outliers. The black dots: Values > Qu + 1.5IQR; the stars: Values > Qu + 3.0IQR. Qu, upper quartile; IQR, inter-quartile range.
Figure 2 Differential expression levels of significant miRNAs in training phase. Expression levels of the miRNAs were normalized to spiked-in cel-miR-39 and were calculated utilizing the 2−ΔΔCt method. Mann-Whitney U test was used to determine statistical significance. The black dots and stars represent the outliers. The black dots: Values > Qu + 1.5IQR; the stars: Values > Qu + 3.0IQR. Qu, upper quartile; IQR, inter-quartile range.CC BY · PubMed Central
Figure 3 Differential expression levels of significant miRNAs in validation phase. Expression levels of the miRNAs were normalized to spiked-in cel-miR-39 and were calculated utilizing the 2−ΔΔCt method. Mann-Whitney U test was used to determine statistical significance.
Figure 3 Differential expression levels of significant miRNAs in validation phase. Expression levels of the miRNAs were normalized to spiked-in cel-miR-39 and were calculated utilizing the 2−ΔΔCt method. Mann-Whitney U test was used to determine statistical significance.CC BY · PubMed Central
Figure 4 Receiver operating characteristic (ROC) curve analysis using 6 miRNAs selected in validation phase for discriminating epilepsy from healthy controls. For the up-regulated miRNAs, the normalized expression level of miRNAs (2−ΔΔCt) was selected as the test variable, and for the down-regulated miRNAs, the logarithm of the normalized expression level (2−ΔΔCt) was selected as the test variable. AUC, area under the ROC curve.
Figure 4 Receiver operating characteristic (ROC) curve analysis using 6 miRNAs selected in validation phase for discriminating epilepsy from healthy controls. For the up-regulated miRNAs, the normalized expression level of miRNAs (2−ΔΔCt) was selected as the test variable, and for the down-regulated miRNAs, the logarithm of the normalized expression level (2−ΔΔCt) was selected as the test variable. AUC, area under the ROC curve.CC BY · PubMed Central
Figure 5 Overview of the study design. qRT-PCR, quantitative reverse transcriptase polymerase chain reaction.
Figure 5 Overview of the study design. qRT-PCR, quantitative reverse transcriptase polymerase chain reaction.CC BY · PubMed Central

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Metadata

Authors
Wang, J., Yu, J.-T., Tan, L., Tian, Y., Ma, J., Tan, C.-C., Wang, H.-F., Liu, Y., Tan, M.-S., Jiang, T., & Tan, L.
Venue
Scientific Reports
Publisher
Springer Science and Business Media LLC
Published
2015-03-31
Volume
5
Issue
1
ISSN
2045-2322
DOI
10.1038/srep09522
PMC
PMC4379481
Cited by
140
References
1
Language
en
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CoChat. (2026). Citation check: Genome-wide circulating microRNA expression profiling indicates biomarkers for epilepsy. https://app.cochat.ai/papers/10.1038/srep09522
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Wang, J., Yu, J.-T., Tan, L., Tian, Y., Ma, J., Tan, C.-C., Wang, H.-F., Liu, Y., Tan, M.-S., Jiang, T., & Tan, L. (2015). Genome-wide circulating microRNA expression profiling indicates biomarkers for epilepsy. Scientific Reports. https://doi.org/10.1038/srep09522