阿尔茨海默病:综合生物信息学和机器学习分析揭示谷氨酰胺代谢相关基因生物标志物。

IF 2.8 3区 医学 Q2 PHARMACOLOGY & PHARMACY
Naifei Xing, Jingwei Yan, Rong Gao, Aihua Zhang, Huiyan He, Man Zheng, Guojing Li
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引用次数: 0

摘要

背景:阿尔茨海默病(AD)是与年龄相关的认知能力下降的标志,其独特的神经病理学定义。代谢失调,特别是涉及谷氨酰胺(Gln)代谢的代谢失调,已经成为阿尔茨海默病病理生理学的一个关键但未被充分探索的方面,这代表了我们目前对该疾病的理解存在重大差距。方法:对GlnMgs在AD中的作用进行了全面的生物信息学分析。我们首先从34个候选基因列表中识别差异表达的GlnMgs。随后,我们采用GSEA和GSVA来评估这些GlnMgs的生物学意义。利用Lasso回归和SVM-RFE等先进技术鉴定关键枢纽基因,并评估14个中心glnmg在AD中的诊断潜力。此外,我们研究了它们与临床参数的相关性,并验证了它们在多个独立AD队列(GSE5281、GSE37263、GSE106241、GSE132903、GSE63060)中的表达。结果:我们通过严格的分析确定了14种GlnMgs-GLS2、GLS、GLUD2、GLUL、GOT1、HAL、AADAT、PFAS、ASNSD1、PPAT、NIT2、ALDH5A1、ASRGL1和atcay -作为AD发病的潜在因素。这些基因与重要的生物过程有关,包括脂质转运和含嘌呤化合物的代谢,以响应营养的可用性。值得注意的是,这些GlnMgs显示出显著的诊断潜力,突出了它们作为AD诊断和预后生物标志物的实用性。结论:我们的研究发现了14种与AD有潜在联系的glnmg,扩大了我们对该疾病分子基础的理解,并为生物标志物的开发提供了有希望的途径。这些发现不仅增强了阿尔茨海默病的分子图谱,而且为未来的诊断和治疗创新铺平了道路,有可能重塑阿尔茨海默病的诊断和患者护理。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Alzheimer's disease: an integrative bioinformatics and machine learning analysis reveals glutamine metabolism-associated gene biomarkers.

Background: Alzheimer's disease (AD), a hallmark of age-related cognitive decline, is defined by its unique neuropathology. Metabolic dysregulation, particularly involving glutamine (Gln) metabolism, has emerged as a critical but underexplored aspect of AD pathophysiology, representing a significant gap in our current understanding of the disease.

Methods: To investigate the involvement of GlnMgs in AD, we conducted a comprehensive bioinformatic analysis. We began by identifying differentially expressed GlnMgs from a curated list of 34 candidate genes. Subsequently, we employed GSEA and GSVA to assess the biological significance of these GlnMgs. Advanced techniques such as Lasso regression and SVM-RFE were utilized to identify key hub genes and evaluate the diagnostic potential of 14 central GlnMgs in AD. Additionally, we examined their correlations with clinical parameters and validated their expression across multiple independent AD cohorts (GSE5281, GSE37263, GSE106241, GSE132903, GSE63060).

Results: Our rigorous analysis identified 14 GlnMgs-GLS2, GLS, GLUD2, GLUL, GOT1, HAL, AADAT, PFAS, ASNSD1, PPAT, NIT2, ALDH5A1, ASRGL1, and ATCAY-as potential contributors to AD pathogenesis. These genes were implicated in vital biological processes, including lipid transport and the metabolism of purine-containing compounds, in response to nutrient availability. Notably, these GlnMgs demonstrated significant diagnostic potential, highlighting their utility as both diagnostic and prognostic biomarkers for AD.

Conclusions: Our study uncovers 14 GlnMgs with potential links to AD, expanding our understanding of the disease's molecular underpinnings and offering promising avenues for biomarker development. These findings not only enhance the molecular landscape of AD but also pave the way for future diagnostic and therapeutic innovations, potentially reshaping AD diagnostics and patient care.

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来源期刊
BMC Pharmacology & Toxicology
BMC Pharmacology & Toxicology PHARMACOLOGY & PHARMACYTOXICOLOGY&nb-TOXICOLOGY
CiteScore
4.80
自引率
0.00%
发文量
87
审稿时长
12 weeks
期刊介绍: BMC Pharmacology and Toxicology is an open access, peer-reviewed journal that considers articles on all aspects of chemically defined therapeutic and toxic agents. The journal welcomes submissions from all fields of experimental and clinical pharmacology including clinical trials and toxicology.
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