Kevins Jara-Medina, Luis Lillo, Constanza Lagunas, Gerardo Cabello-Guzmán, Francisco J Valenzuela-Melgarejo
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We identified common genes using the Venny tool, and their metabolic relevance associated with AD and the vascular system was analyzed using MetaboAnalyst tools.</p><p><strong>Results: </strong>Our bioinformatic analysis identified PRKCB, MAP2K2, ADCY1, GNA11, GNAQ, PRKACB, KCNMB4, CALD1, and GNAS as potentially involved in AD pathogenesis. These genes are associated with signal transductions, cell death signaling, and cytoskeleton, suggesting potential modulation of cellular physiology, including endoplasmic reticulum and mitochondrial activity.</p><p><strong>Conclusion: </strong>This study generates hypotheses regarding the roles of novel genes over critical pathways relevant to AD and its relation with vascular dysfunction. 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引用次数: 0
摘要
背景:阿尔茨海默病(AD)是最常见的痴呆症。此外,传统的阿尔茨海默病发病机制无法解释阿尔茨海默病发病之间约 25-30 年间观察到的微血管损伤,这导致了抑制或延缓神经退行性变的晚期应用治疗:我们的目的是在人脑样本中找出与 AD 血管破坏相关的差异表达基因:我们分析了GEO数据库中的1633份死后脑部样本,在应用临床和生物信息学排除标准后,对581份前额叶和额叶样本进行了分析。所有数据集均使用 NCBI 的 GEO2R 进行分析。我们使用 Venny 工具确定了常见基因,并使用 MetaboAnalyst 工具分析了它们与 AD 和血管系统有关的代谢相关性:我们的生物信息学分析发现 PRKCB、MAP2K2、ADCY1、GNA11、GNAQ、PRKACB、KCNMB4、CALD1 和 GNAS 可能与 AD 发病机制有关。这些基因与信号转导、细胞死亡信号转导和细胞骨架有关,表明它们可能调节细胞生理机能,包括内质网和线粒体的活性:本研究提出了有关新基因在AD关键通路中的作用及其与血管功能障碍关系的假设。这些发现为进一步研究痴呆症和注意力缺失症的发病机制提供了潜在的新靶点。
Identification of Vascular Genes Differentially Expressed in the Brain of Patients with Alzheimer's Disease.
Background: Alzheimer's disease (AD) plays a prominent role as the most common form of dementia. Moreover, the traditional mechanism of AD does not explain the microvascular damage observed in about 25-30 years between the onset of AD, which results in late application treatment that inhibits or delays neurodegeneration.
Objective: Our objective was to identify differentially expressed genes in human brain samples associated with vascular disruption in AD.
Methods: We analyzed 1633 post-mortem brain samples in the GEO database and, after applying clinical and bioinformatic exclusion criteria, worked with 581 prefrontal and frontal samples. All datasets were analyzed using GEO2R from NCBI. We identified common genes using the Venny tool, and their metabolic relevance associated with AD and the vascular system was analyzed using MetaboAnalyst tools.
Results: Our bioinformatic analysis identified PRKCB, MAP2K2, ADCY1, GNA11, GNAQ, PRKACB, KCNMB4, CALD1, and GNAS as potentially involved in AD pathogenesis. These genes are associated with signal transductions, cell death signaling, and cytoskeleton, suggesting potential modulation of cellular physiology, including endoplasmic reticulum and mitochondrial activity.
Conclusion: This study generates hypotheses regarding the roles of novel genes over critical pathways relevant to AD and its relation with vascular dysfunction. These findings suggest potential new targets for further investigation into the pathogenesis of dementia and AD.