Zhang Fan, Zhong Si Ran, Yang Si Man, Wei Yu Ting, Wang Jing Jing, Huang Jin Lan, Wu Deng Pan, Zhong Zhen Guo
Pharmacy School, Guangxi University of Chinese Medicine, Nanning 530200, China.
Jiangsu Key Laboratory of New Drug Research and Clinical Pharmacy, Pharmacy School, Xuzhou Medical University, Xuzhou, Jiangsu 221004, China.
Chin Med Sci J. 2020 Dec 31;35(4):330-341. doi: 10.24920/003695.
Objective Alzheimer's disease (AD) is the most common cause of dementia. The pathophysiology of the disease mostly remains unearthed, thereby challenging drug development for AD. This study aims to screen high throughput gene expression data using weighted co-expression network analysis (WGCNA) to explore the potential therapeutic targets.Methods The dataset of GSE36980 was obtained from the Gene Expression Omnibus (GEO) database. Normalization, quality control, filtration, and soft-threshold calculation were carried out before clustering the co-expressed genes into different modules. Furthermore, the correlation coefficients between the modules and clinical traits were computed to identify the key modules. Gene ontology and pathway enrichment analyses were performed on the key module genes. The STRING database was used to construct the protein-protein interaction (PPI) networks, which were further analyzed by Cytoscape app (MCODE). Finally, validation of hub genes was conducted by external GEO datasets of GSE 1297 and GSE 28146.Results Co-expressed genes were clustered into 27 modules, among which 6 modules were identified as the key module relating to AD occurrence. These key modules are primarily involved in chemical synaptic transmission (GO:0007268), the tricarboxylic acid (TCA) cycle and respiratory electron transport (R-HSA-1428517). , , , , , were found as the hub genes and their expression were validated by external datasets.Conclusions Through modules co-expression network analyses and PPI network analyses, we identified the hub genes of AD, including , , , , and . Among them, three hub genes (, , ) might contribute to AD pathogenesis through pathway of TCA cycle.
目的 阿尔茨海默病（AD）是痴呆最常见的病因。该疾病的病理生理学大多仍未被揭示，这给AD的药物研发带来了挑战。本研究旨在使用加权共表达网络分析（WGCNA）筛选高通量基因表达数据，以探索潜在的治疗靶点。
方法 从基因表达综合数据库（GEO）获取GSE36980数据集。在将共表达基因聚类到不同模块之前，进行归一化、质量控制、过滤和软阈值计算。此外，计算模块与临床特征之间的相关系数以识别关键模块。对关键模块基因进行基因本体论和通路富集分析。使用STRING数据库构建蛋白质 - 蛋白质相互作用（PPI）网络，并通过Cytoscape应用程序（MCODE）进行进一步分析。最后，通过GSE 1297和GSE 28146的外部GEO数据集对枢纽基因进行验证。
结果 共表达基因被聚类到27个模块中，其中6个模块被确定为与AD发生相关的关键模块。这些关键模块主要参与化学突触传递（GO:0007268）、三羧酸（TCA）循环和呼吸电子传递（R-HSA-1428517）。发现 、 、 、 、 为枢纽基因，其表达通过外部数据集得到验证。
结论 通过模块共表达网络分析和PPI网络分析，我们鉴定出AD的枢纽基因，包括 、 、 、 、 和 。其中，三个枢纽基因（ 、 、 ）可能通过TCA循环途径促成AD发病机制。
