Medical Genome Center, National Center for Geriatrics and Gerontology, 7-430 Morioka-cho, Obu, 474-8511, Aichi, Japan.
Department of Medical Science Mathematics, Medical Research Institute, Tokyo Medical and Dental University (TMDU), Tokyo, 113-8510, Japan.
Alzheimers Res Ther. 2020 Jul 16;12(1):87. doi: 10.1186/s13195-020-00654-x.
With demographic shifts toward older populations, the number of people with dementia is steadily increasing. Alzheimer's disease (AD) is the most common cause of dementia, and no curative treatment is available. The current best strategy is to delay disease progression and to practice early intervention to reduce the number of patients that ultimately develop AD. Therefore, promising novel biomarkers for early diagnosis are urgently required.
To identify blood-based biomarkers for early diagnosis of AD, we performed RNA sequencing (RNA-seq) analysis of 610 blood samples, representing 271 patients with AD, 91 cognitively normal (CN) adults, and 248 subjects with mild cognitive impairment (MCI). We first estimated cell-type proportions among AD, MCI, and CN samples from the bulk RNA-seq data using CIBERSORT and then examined the differentially expressed genes (DEGs) between AD and CN samples. To gain further insight into the biological functions of the DEGs, we performed gene set enrichment analysis (GSEA) and network-based meta-analysis.
In the cell-type distribution analysis, we found a significant association between the proportion of neutrophils and AD prognosis at a false discovery rate (FDR) < 0.05. Furthermore, a similar trend emerged in the results of routine blood tests from a large number of samples (n = 3,099: AD, 1,605; MCI, 994; CN, 500). In addition, GSEA and network-based meta-analysis based on DEGs between AD and CN samples revealed functional modules and important hub genes associated with the pathogenesis of AD. The risk prediction model constructed by using the proportion of neutrophils and the most important hub genes (EEF2 and RPL7) achieved a high AUC of 0.878 in a validation cohort; when further applied to a prospective cohort, the model achieved a high accuracy of 0.727.
Our model was demonstrated to be effective in prospective AD risk prediction. These findings indicate the discovery of potential biomarkers for early diagnosis of AD, and their further improvement may lead to future practical clinical use.
随着人口向老龄化转变，痴呆症患者的数量稳步增加。阿尔茨海默病（AD）是痴呆症最常见的病因，目前尚无治愈方法。目前最好的策略是延缓疾病进展，并进行早期干预，以减少最终发展为 AD 的患者数量。因此，迫切需要有前途的新型生物标志物用于早期诊断。
为了确定用于 AD 早期诊断的血液生物标志物，我们对 610 份血液样本进行了 RNA 测序（RNA-seq）分析，这些样本代表了 271 名 AD 患者、91 名认知正常（CN）成年人和 248 名轻度认知障碍（MCI）患者。我们首先使用 CIBERSORT 从批量 RNA-seq 数据中估计 AD、MCI 和 CN 样本中的细胞类型比例，然后检查 AD 和 CN 样本之间的差异表达基因（DEGs）。为了更深入地了解 DEGs 的生物学功能，我们进行了基因集富集分析（GSEA）和基于网络的荟萃分析。
在细胞类型分布分析中，我们发现中性粒细胞比例与 AD 预后之间存在显著关联，假发现率（FDR）<0.05。此外，在大量样本的常规血液检测结果中也出现了类似的趋势（n=3099：AD，1605；MCI，994；CN，500）。此外，基于 AD 和 CN 样本之间的 DEGs 的 GSEA 和基于网络的荟萃分析揭示了与 AD 发病机制相关的功能模块和重要枢纽基因。使用中性粒细胞比例和最重要的枢纽基因（EEF2 和 RPL7）构建的风险预测模型在验证队列中实现了 0.878 的高 AUC；当进一步应用于前瞻性队列时，该模型实现了 0.727 的高准确率。
我们的模型在 AD 前瞻性风险预测中表现出有效性。这些发现表明，发现了 AD 早期诊断的潜在生物标志物，进一步改进可能会导致未来的实际临床应用。
