Introduction: The molecular pathogenesis of Alzheimer’s disease (AD) is still not clear, and the relationship between gene expression profile for different brain regions has not been studied. Objective: Bioinformatic analysis at the genetic level has become the best way for the pathogenesis research of AD, which can analyze the abovementioned relationship. Methods: In this study, the datasets of AD were obtained from the Gene Expression Omnibus (GEO), and Qlucore Omics Explorer (QOE) software was used to screen differentially expressed genes of GSE36980 and GSE9770 and verify gene expression of GSE63060. The Gene Ontology (GO) function enrichment analysis of these selected genes was conducted by Database for Annotation, Visualization, and Integrated Discovery (DAVID), and then the gene/protein interaction network was established by STRING to find the related proteins. R language was used for drafting maps and plots. Results: There were 20 differentially expressed genes related to AD selected from GSE36980 (p = 6.2e−6, q = 2.9422e−4) and GSE9770 (p = 3.3e−4, q = 0.016606). Their expression levels of the AD group were lower than those in the control group and varied among different brain regions. Cellular morphogenesis and establishment or maintenance of cell polarity were enriched, and LRRTM1 and RASAL1 were identified by the integration network. Moreover, the analysis of GSE63060 verified the expression level of LRRTM1 and RASAL1 in Alzheimer’s patients, which was much lower than that in normal people aged >65 years. Conclusions: The pathogenesis of AD at molecular levels may link to cell membrane structures and signal transduction; hence, a list of 20 genes, including LRRTM1 and RASAL1,potentially are important for the discovery of treatment target or molecular marker of AD.

中文翻译：

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阿尔茨海默病分子发病机制的遗传学分析

简介：阿尔茨海默病（AD）的分子发病机制尚不清楚，不同大脑区域的基因表达谱之间的关系尚未研究。目的：基因水平的生物信息学分析已成为AD发病机制研究的最佳途径，可以分析上述关系。方法：本研究从Gene Expression Omnibus（GEO）中获取AD数据集，利用Qlucore Omics Explorer（QOE）软件筛选GSE36980和GSE9770差异表达基因，验证GSE63060基因表达情况。这些选定基因的基因本体论 (GO) 功能富集分析由注释、可视化和集成发现数据库 (DAVID) 进行，然后通过STRING建立基因/蛋白质相互作用网络，寻找相关蛋白质。R 语言用于绘制地图和绘图。结果：从GSE36980（p = 6.2e-6，q = 2.9422e-4）和GSE9770（p = 3.3e-4，q = 0.016606）中选出20个与AD相关的差异表达基因。AD组它们的表达水平低于对照组，并且在不同脑区之间存在差异。丰富了细胞形态发生和细胞极性的建立或维持，并且通过整合网络识别了 LRRTM1 和 RASAL1。此外，GSE63060的分析证实了阿尔茨海默病患者LRRTM1和RASAL1的表达水平，远低于65岁以上的正常人。结论：AD在分子水平的发病机制可能与细胞膜结构和信号转导有关；因此，包括 LRRTM1 和 RASAL1 在内的 20 个基因的列表可能对发现 AD 的治疗靶点或分子标记很重要。