The Third Xiangya Hospital, Department of Neurology, Central South University, Changsha, 410000, China.
School of Life Sciences, Center for Medical Genetics and Hunan Key Laboratory of Medical Genetics, Central South University, Changsha, 410000, China.
Sci Rep. 2023 Sep 27;13(1):16252. doi: 10.1038/s41598-023-43595-9.
As the incidence of Alzheimer's disease (AD) increases year by year, more people begin to study this disease. In recent years, many studies on reactive oxygen species (ROS), neuroinflammation, autophagy, and other fields have confirmed that hypoxia is closely related to AD. However, no researchers have used bioinformatics methods to study the relationship between AD and hypoxia. Therefore, our study aimed to screen the role of hypoxia-related genes in AD and clarify their diagnostic significance. A total of 7681 differentially expressed genes (DEGs) were identified in GSE33000 by differential expression analysis and cluster analysis. Weighted gene co-expression network analysis (WGCNA) was used to detect 9 modules and 205 hub genes with high correlation coefficients. Next, machine learning algorithms were applied to 205 hub genes and four key genes were selected. Through the verification of external dataset and quantitative real-time PCR (qRT-PCR), the AD diagnostic model was established by ANTXR2, BDNF and NFKBIA. The bioinformatics analysis results suggest that hypoxia-related genes may increase the risk of AD. However, more in-depth studies are still needed to investigate their association, this article would guide the insights and directions for further research.
随着阿尔茨海默病（AD）的发病率逐年上升，越来越多的人开始研究这种疾病。近年来，许多关于活性氧（ROS）、神经炎症、自噬等领域的研究证实，缺氧与 AD 密切相关。然而，目前还没有研究人员使用生物信息学方法来研究 AD 和缺氧之间的关系。因此，我们的研究旨在筛选与缺氧相关的基因在 AD 中的作用，并阐明其诊断意义。通过差异表达分析和聚类分析，在 GSE33000 中鉴定出 7681 个差异表达基因（DEGs）。使用加权基因共表达网络分析（WGCNA）检测到 9 个模块和 205 个具有高相关系数的枢纽基因。接下来，应用机器学习算法对 205 个枢纽基因进行分析，选择了 4 个关键基因。通过外部数据集的验证和定量实时 PCR（qRT-PCR），通过 ANTXR2、BDNF 和 NFKBIA 建立了 AD 诊断模型。生物信息学分析结果表明，与缺氧相关的基因可能会增加 AD 的发病风险。然而，还需要进行更深入的研究来探讨它们之间的关系，本文将为进一步研究提供思路和方向。
