Faculty of Medicine and Health Technology, Tampere University, Tampere, Finland.
Institute of Biomedicine, University of Eastern Finland, Kuopio, Finland.
Neurobiol Aging. 2019 Dec;84:98-108. doi: 10.1016/j.neurobiolaging.2019.07.004. Epub 2019 Jul 16.
Early identification and treatment of Alzheimer's disease (AD) is hampered by the lack of easily accessible biomarkers. Currently available fluid biomarkers of AD provide indications of the disease stage; however, these are measured in the cerebrospinal fluid, requiring invasive procedures, which are not applicable at the population level. Thus, gene expression profiling of blood provides a viable alternative as a way to screen individuals at risk of AD. Previous studies have shown that despite the limited permeability of the blood-brain barriers, expression profiles of blood genes can be used for the diagnosis and prognosis of several brain disorders. Here, we propose a new approach to pathway analysis of blood gene expression profiles to classify healthy (control [CTL]), mildly cognitively impaired (mild cognitive impairment [MCI]; preclinical stage of AD), and AD subjects. In the pathway analysis, gene expression data are mapped to pathway scores according to a predefined gene set instead of considering each gene separately. The robustness of the analysis enables detection of weak differences between groups owing to the inherent dimension reduction. Our proposed method for pathway analysis takes advantage of linear discriminant analysis for identifying a linear combination of features best separating groups of subjects within each gene set. The gene expression data were retrieved from Gene Expression Omnibus (batch 1: GSE63060; batch 2: GSE63061). Predefined gene sets for pathway analysis were obtained from the Broad Institute Collection of Curated Pathways. The method achieved a 10-fold cross-validated area under receiver operating characteristic curve of 0.84 for classification of AD versus CTL and 0.80 for classification of mild cognitive impairment versus CTL. These results reveal the good potential of blood-based biomarkers for assisting early diagnosis and disease monitoring of AD.
早期识别和治疗阿尔茨海默病（AD）受到缺乏易于获取的生物标志物的阻碍。目前可用的 AD 液 体生物标志物提供了疾病阶段的指示；然而，这些是在脑脊液中测量的，需要进行侵入性程序，这在人群水平上是不适用的。因此，血液中的基因表达谱分析为筛选 AD 风险个体提供了可行的替代方法。先前的研究表明，尽管血脑屏障的通透性有限，但血液基因的表达谱可用于几种脑疾病的诊断和预后。在这里，我们提出了一种新的血液基因表达谱途径分析方法，用于对健康（对照 [CTL]）、轻度认知障碍（轻度认知障碍 [MCI]；AD 的临床前阶段）和 AD 患者进行分类。在途径分析中，根据预定义的基因集将基因表达数据映射到途径评分，而不是分别考虑每个基因。由于固有维度降低，分析的稳健性能够检测到组之间的微弱差异。我们提出的途径分析方法利用线性判别分析来识别最佳分离每个基因集中的组的特征的线性组合。基因表达数据从基因表达综合数据库（批次 1：GSE63060；批次 2：GSE63061）中检索。途径分析的预定义基因集从 Broad Institute 精选途径集合中获得。该方法在 10 倍交叉验证中，AD 与 CTL 分类的接收器操作特性曲线下面积为 0.84，轻度认知障碍与 CTL 分类的面积为 0.80。这些结果表明，基于血液的生物标志物具有很好的潜力，可用于辅助 AD 的早期诊断和疾病监测。
