State Key Laboratory of Quality Research in Chinese Medicine, Institute of Chinese Medical Sciences, University of Macau, Avenida da Universidade, Taipa, 999078, Macao, China.
BGI College & Henan Institute of Medical and Pharmaceutical Sciences, Zhengzhou University, Zhengzhou, Henan, China.
Alzheimers Res Ther. 2021 Jul 9;13(1):126. doi: 10.1186/s13195-021-00862-z.
Blood circulating microRNAs that are specific for Alzheimer's disease (AD) can be identified from differentially expressed microRNAs (DEmiRNAs). However, non-reproducible and inconsistent reports of DEmiRNAs hinder biomarker development. The most reliable DEmiRNAs can be identified by meta-analysis. To enrich the pool of DEmiRNAs for potential AD biomarkers, we used a machine learning method called adaptive boosting for miRNA disease association (ABMDA) to identify eligible candidates that share similar characteristics with the DEmiRNAs identified from meta-analysis. This study aimed to identify blood circulating DEmiRNAs as potential AD biomarkers by augmenting meta-analysis with the ABMDA ensemble learning method.
Studies on DEmiRNAs and their dysregulation states were corroborated with one another by meta-analysis based on a random-effects model. DEmiRNAs identified by meta-analysis were collected as positive examples of miRNA-AD pairs for ABMDA ensemble learning. ABMDA identified similar DEmiRNAs according to a set of predefined criteria. The biological significance of all resulting DEmiRNAs was determined by their target genes according to pathway enrichment analyses. The target genes common to both meta-analysis- and ABMDA-identified DEmiRNAs were collected to construct a network to investigate their biological functions.
A systematic database search found 7841 studies for an extensive meta-analysis, covering 54 independent comparisons of 47 differential miRNA expression studies, and identified 18 reliable DEmiRNAs. ABMDA ensemble learning was conducted based on the meta-analysis results and the Human MicroRNA Disease Database, which identified 10 additional AD-related DEmiRNAs. These 28 DEmiRNAs and their dysregulated pathways were related to neuroinflammation. The dysregulated pathway related to neuronal cell cycle re-entry (CCR) was the only statistically significant pathway of the ABMDA-identified DEmiRNAs. In the biological network constructed from 1865 common target genes of the identified DEmiRNAs, the multiple core ubiquitin-proteasome system, that is involved in neuroinflammation and CCR, was highly connected.
This study identified 28 DEmiRNAs as potential AD biomarkers in blood, by meta-analysis and ABMDA ensemble learning in tandem. The DEmiRNAs identified by meta-analysis and ABMDA were significantly related to neuroinflammation, and the ABMDA-identified DEmiRNAs were related to neuronal CCR.
从差异表达 microRNA (DEmiRNA) 中可以鉴定出针对阿尔茨海默病 (AD) 的特异性血液循环 microRNA。然而，DEmiRNA 的非重复性和不一致报告阻碍了生物标志物的开发。通过荟萃分析可以鉴定出最可靠的 DEmiRNA。为了丰富潜在 AD 生物标志物的 DEmiRNA 库，我们使用一种称为 miRNA 疾病关联的自适应增强 (ABMDA) 的机器学习方法来鉴定与荟萃分析中鉴定的 DEmiRNA 具有相似特征的合格候选者。本研究旨在通过使用自适应增强元分析集成学习方法来鉴定血液循环 DEmiRNA 作为潜在的 AD 生物标志物。
通过基于随机效应模型的荟萃分析相互印证 DEmiRNA 研究，以汇集 DEmiRNA 的失调状态。荟萃分析中鉴定的 DEmiRNA 被收集为 miRNA-AD 对的阳性示例，用于 ABMDA 集成学习。ABMDA 根据一组预设标准根据一组预设标准确定类似的 DEmiRNA。根据通路富集分析，通过其靶基因确定所有鉴定出的 DEmiRNA 的生物学意义。汇集同时被荟萃分析和 ABMDA 鉴定的 DEmiRNA 的共同靶基因，以构建网络来研究它们的生物学功能。
系统数据库搜索发现了 7841 项研究，进行了广泛的荟萃分析，涵盖了 47 项差异 miRNA 表达研究的 54 项独立比较，确定了 18 个可靠的 DEmiRNA。基于荟萃分析结果和人类 microRNA 疾病数据库进行了 ABMDA 集成学习，确定了 10 个额外的 AD 相关 DEmiRNA。这 28 个 DEmiRNA 及其失调途径与神经炎症有关。与神经元细胞周期再进入 (CCR) 相关的失调途径是 ABMDA 鉴定的 DEmiRNA 中唯一具有统计学意义的途径。在鉴定的 DEmiRNA 的 1865 个共同靶基因构建的生物网络中，高度连接的是涉及神经炎症和 CCR 的多个核心泛素蛋白酶体系统。
本研究通过荟萃分析和 ABMDA 集成学习相结合，鉴定出 28 个作为血液中潜在 AD 生物标志物的 DEmiRNA。荟萃分析和 ABMDA 鉴定的 DEmiRNA 与神经炎症显著相关，ABMDA 鉴定的 DEmiRNA 与神经元 CCR 相关。
