阿尔茨海默病：一项综合生物信息学和机器学习分析揭示了谷氨酰胺代谢相关基因生物标志物。

Alzheimer's disease: an integrative bioinformatics and machine learning analysis reveals glutamine metabolism-associated gene biomarkers.

作者信息

Xing Naifei, Yan Jingwei, Gao Rong, Zhang Aihua, He Huiyan, Zheng Man, Li Guojing

机构信息

Yantai Affiliated Hospital of Binzhou Medical University, Yantai, Shandong, 264100, PR China.

Dongying People's Hospital (Dongying Hospital of Shandong Provincial Hospital Group), Dongying, Shandong, 257091, PR China.

出版信息

BMC Pharmacol Toxicol. 2025 Jan 28;26(1):19. doi: 10.1186/s40360-025-00852-z.

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.

背景

阿尔茨海默病（AD）是与年龄相关的认知衰退的一个标志，由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）代谢的失调，已成为AD病理生理学中一个关键但未充分探索的方面，这代表了我们目前对该疾病理解上的重大差距。

方法

为了研究谷氨酰胺代谢基因（GlnMgs）在AD中的作用，我们进行了全面的生物信息学分析。我们首先从34个候选基因的精选列表中识别出差异表达的GlnMgs。随后，我们使用基因集富集分析（GSEA）和基因集变异分析（GSVA）来评估这些GlnMgs的生物学意义。利用套索回归和支持向量机递归特征消除（SVM-RFE）等先进技术来识别关键枢纽基因，并评估14个核心GlnMgs在AD中的诊断潜力。此外，我们检查了它们与临床参数的相关性，并在多个独立的AD队列（GSE5281、GSE37263、GSE106241、GSE132903、GSE63060）中验证了它们的表达。

结果

我们的严谨分析确定了14个GlnMgs——谷氨酰胺酶2（GLS2）、谷氨酰胺酶（GLS）、谷氨酸脱氢酶2（GLUD2）、谷氨酰胺合成酶（GLUL）、天冬氨酸氨基转移酶1（GOT1）、组氨酸氨裂解酶（HAL）、天冬氨酸-α-脱羧酶（AADAT）、全氟辛酸（PFAS）、天冬酰胺合成酶结构域包含蛋白1（ASNSD1）、磷酸核糖焦磷酸酰胺转移酶（PPAT）、亚硝酸盐还原酶2（NIT2）、醛脱氢酶5家族成员A1（ALDH5A1）、天冬酰胺合成酶相关谷氨酸裂合酶1（ASRGL1）和天冬氨酸-γ-转氨酶（ATCAY）——作为AD发病机制的潜在促成因素。这些基因参与了重要的生物学过程，包括脂质转运和含嘌呤化合物的代谢，以响应营养物质的可用性。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效用。