Prediction of Alzheimer's disease using blood gene expression data
Prediction of Alzheimer's disease using blood gene expression data
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DOI：
10.1038/s41598-020-60595-1
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发表时间：
2020-02-26
影响因子：
4.6
通讯作者：
Lee, Hyunju
中科院分区：
文献类型：
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作者：
Lee, Taesic;Lee, Hyunju
从血液样本中获得AD（Alzheimer's disease）相关基因的鉴定对于AD的早期诊断至关重要。我们使用了三个公共数据集，ADNI，AddNeuroMed1（ANM1）和ANM2。采用5种特征选择方法和5种分类器分别对AD相关基因进行筛选和对AD患者进行分类。在内部验证（每个数据集内的五重交叉验证）中，ADNI、ANMI和ANM2的曲线下面积（AUC）的最佳平均值分别为0.657、0.874和0.804。在外部验证（来自不同数据集的训练集和测试集）中，最佳AUC分别为0.697（训练：ADNI到测试：ANM1），0.764（ADNI到ANM2），0.619（ANM1到ADNI），0.79（ANM1到ANM2），0.655（ANM2到ADNI）和0.859（ANM2到ANM1）。这些结果表明，虽然ADNI的分类性能相对低于ANM1和ANM2，但使用血液基因表达训练的分类器可用于对其他数据集的AD进行分类。此外，通路分析显示AD相关基因富含炎症、线粒体和Wnt信号通路。我们的研究表明，血液基因表达数据对于预测AD分类很有用。
Identification of AD (Alzheimer's disease)-related genes obtained from blood samples is crucial for early AD diagnosis. We used three public datasets, ADNI, AddNeuroMed1 (ANM1), and ANM2, for this study. Five feature selection methods and five classifiers were used to curate AD-related genes and discriminate AD patients, respectively. In the internal validation (five-fold cross-validation within each dataset), the best average values of the area under the curve (AUC) were 0.657, 0.874, and 0.804 for ADNI, ANMI, and ANM2, respectively. In the external validation (training and test sets from different datasets), the best AUCs were 0.697 (training: ADNI to testing: ANM1), 0.764 (ADNI to ANM2), 0.619 (ANM1 to ADNI), 0.79 (ANM1 to ANM2), 0.655 (ANM2 to ADNI), and 0.859 (ANM2 to ANM1), respectively. These results suggest that although the classification performance of ADNI is relatively lower than that of ANM1 and ANM2, classifiers trained using blood gene expression can be used to classify AD for other data sets. In addition, pathway analysis showed that AD-related genes were enriched with inflammation, mitochondria, and Wnt signaling pathways. Our study suggests that blood gene expression data are useful in predicting the AD classification.