Wang Liang-Jen, Li Sung-Chou, Lee Min-Jing, Chou Miao-Chun, Chou Wen-Jiun, Lee Sheng-Yu, Hsu Chih-Wei, Huang Lien-Hung, Kuo Ho-Chang
Department of Child and Adolescent Psychiatry, Kaohsiung Chang Gung Memorial Hospital, Chang Gung University College of Medicine, Kaohsiung, Taiwan.
Department of Chinese Medicine, Chang Gung University, Taoyuan, Taiwan.
Front Psychiatry. 2018 May 29;9:227. doi: 10.3389/fpsyt.2018.00227. eCollection 2018.
Attention-deficit/hyperactivity disorder (ADHD) is a highly genetic neurodevelopmental disorder, and its dysregulation of gene expression involves microRNAs (miRNAs). The purpose of this study was to identify potential miRNAs biomarkers and then use these biomarkers to establish a diagnostic panel for ADHD. RNA samples from white blood cells (WBCs) of five ADHD patients and five healthy controls were combined to create one pooled patient library and one control library. We identified 20 candidate miRNAs with the next-generation sequencing (NGS) technique (Illumina). Blood samples were then collected from a Training Set (68 patients and 54 controls) and a Testing Set (20 patients and 20 controls) to identify the expression profiles of these miRNAs with real-time quantitative reverse transcription polymerase chain reaction (qRT-PCR). We used receiver operating characteristic (ROC) curves and the area under the curve (AUC) to evaluate both the specificity and sensitivity of the probability score yielded by the support vector machine (SVM) model. We identified 13 miRNAs as potential ADHD biomarkers. The ΔCt values of these miRNAs in the Training Set were integrated to create a biomarker model using the SVM algorithm, which demonstrated good validity in differentiating ADHD patients from control subjects (sensitivity: 86.8%, specificity: 88.9%, AUC: 0.94, < 0.001). The results of the blind testing showed that 85% of the subjects in the Testing Set were correctly classified using the SVM model alignment (AUC: 0.91, < 0.001). The discriminative validity is not influenced by patients' age or gender, indicating both the robustness and the reliability of the SVM classification model. As measured in peripheral blood, miRNA-based biomarkers can aid in the differentiation of ADHD in clinical settings. Additional studies are needed in the future to clarify the ADHD-associated gene functions and biological mechanisms modulated by miRNAs.
注意力缺陷多动障碍(ADHD)是一种具有高度遗传性的神经发育障碍,其基因表达失调涉及微小RNA(miRNA)。本研究的目的是鉴定潜在的miRNA生物标志物,然后利用这些生物标志物建立ADHD诊断模型。将5例ADHD患者和5例健康对照者的白细胞(WBC)RNA样本合并,创建一个混合患者文库和一个对照文库。我们使用下一代测序(NGS)技术(Illumina)鉴定出20个候选miRNA。随后从训练集(68例患者和54例对照)和测试集(20例患者和20例对照)采集血样,通过实时定量逆转录聚合酶链反应(qRT-PCR)鉴定这些miRNA的表达谱。我们使用受试者工作特征(ROC)曲线和曲线下面积(AUC)来评估支持向量机(SVM)模型产生的概率评分的特异性和敏感性。我们鉴定出13个miRNA作为潜在的ADHD生物标志物。利用SVM算法将这些miRNA在训练集中的ΔCt值整合,创建一个生物标志物模型,该模型在区分ADHD患者和对照受试者方面具有良好的有效性(敏感性:86.8%,特异性:88.9%,AUC:0.94,P<0.001)。盲法测试结果显示,使用SVM模型比对,测试集中85%的受试者被正确分类(AUC:0.91,P<0.001)。判别效度不受患者年龄或性别的影响,表明SVM分类模型的稳健性和可靠性。在外周血中检测发现,基于miRNA的生物标志物有助于在临床环境中鉴别ADHD。未来需要进一步研究以阐明ADHD相关基因功能以及miRNA调节的生物学机制。