Department of Biomedical Engineering, School of Fundamental Sciences, China Medical University, Shenyang 110122, Liaoning Province, China.
China Medical University-The Queen's University of Belfast Joint College, China Medical University, Shenyang 110122, Liaoning Province, China.
Biomed Res Int. 2019 Jul 14;2019:4273108. doi: 10.1155/2019/4273108. eCollection 2019.
As the incidence of senile dementia continues to increase, researches on Alzheimer's disease (AD) have become more and more important. Several studies have reported that there is a close relationship between AD and aging. Some researchers even pointed out that if we wanted to understand AD in depth, mechanisms of AD based on accelerated aging must be studied. Nowadays, machine learning techniques have been utilized to deal with large and complex profiles, thus playing an important role in disease researches (i.e., modelling biological systems, identifying key modules based on biological networks, and so on). Here, we developed an aging predictor and an AD predictor using machine learning techniques, respectively. Both aging and AD biomarkers were identified to provide insights into genes associated with AD. Besides, aging scores were calculated to reflect the aging process of brain tissues. As a result, the aging acceleration network and the aging-AD bipartite graph were constructed to delve into the relationship between AD and aging. Finally, a series of network and enrichment analyses were also conducted to gain further insights into the mechanisms of AD based on accelerated aging. In a word, our results indicated that aging may contribute to the development of AD by affecting the function of the immune system and the energy metabolism process, where the immune system may play a more prominent role in AD.
随着老年痴呆症发病率的不断上升，阿尔茨海默病（AD）的研究变得越来越重要。有几项研究报告称，AD 与衰老密切相关。一些研究人员甚至指出，如果我们想深入了解 AD，就必须研究基于加速衰老的 AD 机制。如今，机器学习技术已被用于处理大型复杂的数据集，因此在疾病研究中发挥着重要作用（即，对生物系统进行建模，根据生物网络识别关键模块等）。在这里，我们分别使用机器学习技术开发了衰老预测器和 AD 预测器。分别鉴定了衰老和 AD 的生物标志物，以提供与 AD 相关的基因的见解。此外，还计算了衰老评分以反映脑组织的衰老过程。结果构建了衰老加速网络和衰老-AD 二分图，以深入研究 AD 与衰老之间的关系。最后，还进行了一系列网络和富集分析，以深入了解基于加速衰老的 AD 机制。总之，我们的结果表明，衰老可能通过影响免疫系统和能量代谢过程的功能而导致 AD 的发生，其中免疫系统在 AD 中可能发挥更突出的作用。
