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The Model of Aging Acceleration Network Reveals the Correlation of Alzheimer’S Disease and Aging at System Level

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The Model of Aging Acceleration Network Reveals the Correlation of Alzheimer’S Disease and Aging at System Level Hindawi BioMed Research International Volume 2019, Article ID 4273108, 11 pages https://doi.org/10.1155/2019/4273108 Research Article The Model of Aging Acceleration Network Reveals the Correlation of Alzheimer’s Disease and Aging at System Level Mengyu Zhou,1 Xiaoqiong Xia,1 Hao Yan,1 Sijia Li,1 Shiyu Bian,2 Xianzheng Sha ,1 and Yin Wang 1,3 1 Department of Biomedical Engineering, School of Fundamental Sciences, China Medical University, Shenyang 110122, Liaoning Province, China 2China Medical University-Te Queen’s University of Belfast Joint College, China Medical University, Shenyang 110122, Liaoning Province, China 3Tumor Etiology and Screening Department of Cancer Institute and General Surgery, Te First Afliated Hospital of China Medical University, 155# North Nanjing Street, Heping District, Shenyang 110001, Liaoning Province, China Correspondence should be addressed to Xianzheng Sha; [email protected] and Yin Wang; [email protected] Received 26 January 2019; Revised 11 June 2019; Accepted 2 July 2019; Published 14 July 2019 Academic Editor: Wen-Quan Zou Copyright © 2019 Mengyu Zhou et al. Tis is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. 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 profles, 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 identifed to provide insights into genes associated with AD. Besides, aging scores were calculated to refect 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 afecting the function of the immune system and the energy metabolism process, where the immune system may play a more prominent role in AD. 1. Introduction Obviously, AD has its distinct etiology and neuropathol- ogy. However, it has been proposed that if we wanted It has been reported that the number of people who develop to have a comprehensive understanding of AD, we must dementia is increasing rapidly—one case every 3 seconds [1]. study the relationship between aging and AD [4]. In fact, In 2018, there were about 50 million people in the world aging had a lot to do with AD. According to the Baltimore afected by dementia, and the main form was Alzheimer’s Longitudinal Study of Aging (BLSA), the incidence rate of AD disease (AD). Actually, AD is a very common neurode- increasedvisiblyafertheageof60[5].Tatwastosayaging generative disease characterized by the abnormal level of brainsweremuchsusceptibletoADcomparedwithyoung amyloid �-peptide (A�) and tau protein [2]. Alzheimer’s brains. Second, there was a phenomenon called normalcy- disease can afect the patients’ normal life and reduce the life pathology homology. For example, in the frontotemporal quality. Moreover, the cost of the treatment is very expensive, region, cortical reductions were evident in elderly people, resulting in an economic impact on the health care system including those who were nondemented. Interestingly, these [3].Withthegrowthofelderlypeople,itisnotdifcultto regions were also vulnerable to AD [6]. Tird, AD patients conclude that dementia will become a global problem. and healthy elderly people had some common symptoms, 2 BioMed Research International Table 1: Te details of the data sets used to construct the aging predictor, the AD predictor, and the network predictor as well as the data set used to construct the aging acceleration network. Training data set Test data set Age≤50: 275 samples Age≤50: 129 samples Te data set for the aging predictor Age>50: 575 samples Age>50: 272 samples Te data set for the AD predictor, the network predictor, and the Normal: 850 samples Normal: 401 samples aging acceleration network AD: 662 samples AD: 312 samples Calculating Construction Gene Modeling the aging scores of the expression aging predictor aging profiles of and identifying normal samples acceleration aging biomarkers network Network Preprocessing analyses and and enrichment standardization analyses Cell signaling Modeling the AD Construction Gene pathways expression predictor and of the aging- profiles of AD identifying AD AD bipartite samples biomarkers graph Figure 1: Te workfow in this work. such as the episodic memory decline [7, 8], brain atrophy summary, the association between aging and AD biomarkers [9–11], and the deposition of amyloid protein [12]. Moreover, was investigated by our computational pipeline. the accelerated decline of brain volume has already been observed in mild cognitive impairment (MCI) compared 2. Results and Discussion with the normal aging, which was of great importance in understanding AD [10]. Terefore, we sought to study the 2.1. Identifcation of Aging and AD Related Risk Biomarkers. relationship between aging and AD from the perspective of We analyzed the gene expression data, including 11,333 genes aging acceleration. We proposed a hypothesis that accelerated basedon1,251normalsamplesfrom22tissuesand974AD aging was one of the most important factors to promote the samples from 20 tissues (Table S1). Te details of samples in progression of AD by afecting some molecular mechanisms. the training dataset and the independent test dataset were With the generation of large and complex datasets from showninTable1.WeappliedthereliefFalgorithmtorank biological experiments, methods to analyze these datasets the 11,333 genes and the nearest neighbor algorithm (NNA) became diverse. Machine learning is one of the most powerful to construct the aging and AD predictors, respectively. Ten, toolstocopewiththehugeandcomplexdatasets.Itcan the 5-fold cross-validation was used to evaluate the predictive learn the pattern of the given data and make predictions accuraciesandselectpropernumberofkeymarkers.Te for new data [13]. More importantly, the strength of this learning curves in the training datasets were shown in Figures approach is that it can sif through quantities of data to fnd 2(a) and 2(b). We considered the top 44 genes as aging related predictive patterns. In addition, network biology can study risk biomarkers and the top 98 genes as AD related risk the interactions between molecules and provide biological biomarkers (Table S2), based on the cross-validation. Next, information based on the machine learning methods [14, 15]. the two predictors were validated in the independent test In this paper, we utilized machine learning techniques to datasets. Te receiver operating characteristic (ROC) curves analyze the relationship between aging and AD and studied were shown in Figures 3(a) and 3(b). Te area under the ROC the mechanisms underlying them. First, we identifed aging curve (AUC) for the aging predictor was 0.8169 and the AD and AD related risk biomarkers, respectively. Second, we predictor was 0.6850. Furthermore, the accuracy of the aging established an aging score comprising the aging biomarkers. predictor was 0.8806 and the AD predictor was 0.7359. In Ten the aging acceleration network was constructed based summary, these results showed that the predictors were with on the correlation between each pair of genes and aging both high accuracy and efciency. scores. Finally, network analyses and enrichment analyses Te selected biomarkers were with critical functions. For werealsoconductedtounderstandtherelevantbiologi- example, among the 44 aging biomarkers, the gene with cal mechanisms. Te workfow was shown in Figure 1. In the highest ranking was USMG5 (DAPIT). As Ohsakaya BioMed Research International 3 e learning curve of the aging predictor e learning curve of the AD predictor 1 0.75 0.9 0.7 0.8 0.65 Accuracy Accuracy 0.7 0.6 0.6 0.5 0.55 0 20 40 60 80 100 0 20 40 60 80 100 Number of features Number of features (a) (b) e learning curve of the network predictor 0.7 0.65 0.6 Accuracy 0.55 0.5 0 20406080100 Number of features (c) Figure 2: Te learning curves of the three predictors. (a) Te learning curve of the aging predictor; (b) the learning curve of the AD predictor; and (c) the learning curve of the network predictor. S and his colleagues reported, DAPIT was associated with AD.Asagingprogressed,mostmetabolicactivitieswithin mitochondrial ATP synthase [16]. It played an active role the cell gradually declined, including the dysfunction of in the energy metabolism of cells by maintaining the ATP mitochondria. When these changes afected the normal synthase population in mitochondria. Further, changes in function of neurons in the brain, they may contribute to the energy metabolism with age were a decisive factor of aging pathology associated with AD. [17].
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