Identifying the Role of Oligodendrocyte Genes in the Diagnosis of Alzheimer's Disease through Machine Learning and Bioinformatics Analysis.

Yan Chen, Chen Li, Yinhui Yao, Yazhen Shang
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Abstract

Background: Due to the heterogeneity of Alzheimer's disease (AD), the underlying pathogenic mechanisms have not been fully elucidated. Oligodendrocyte (OL) damage and myelin degeneration are prevalent features of AD pathology. When oligodendrocytes are subjected to amyloid-beta (Aβ) toxicity, this damage compromises the structural integrity of myelin and results in a reduction of myelin-associated proteins. Consequently, the impairment of myelin integrity leads to a slowdown or cessation of nerve signal transmission, ultimately contributing to cognitive dysfunction and the progression of AD. Consequently, elucidating the relationship between oligodendrocytes and AD from the perspective of oligodendrocytes is instrumental in advancing our understanding of the pathogenesis of AD.

Objective: Here, an attempt is made in this study to identify oligodendrocyte-related biomarkers of AD.

Methods: AD datasets were obtained from the Gene Expression Omnibus database and used for consensus clustering to identify subclasses. Hub genes were identified through differentially expressed genes (DEGs) analysis and oligodendrocyte gene set enrichment. Immune infiltration analysis was conducted using the CIBERSORT method. Signature genes were identified using machine learning algorithms and logistic regression. A diagnostic nomogram for predicting AD was developed and validated using external datasets and an AD model. A small molecular compound was identified using the eXtreme Sum algorithm.

Results: 46 genes were found to be significantly correlated with AD progression by examining the overlap between DEGs and oligodendrocyte genes. Two subclasses of AD, Cluster A, and Cluster B, were identified, and 9 signature genes were identified using a machine learning algorithm to construct a nomogram. Enrichment analysis showed that 9 genes are involved in apoptosis and neuronal development. Immune infiltration analysis found differences in immune cell presence between AD patients and controls. External datasets and RT-qPCR verification showed variation in signature genes between AD patients and controls. Five small molecular compounds were predicted.

Conclusion: It was found that 9 oligodendrocyte genes can be used to create a diagnostic tool for AD, which could help in developing new treatments.

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通过机器学习和生物信息学分析确定少突胶质细胞基因在阿尔茨海默病诊断中的作用。
背景:由于阿尔茨海默病(AD)的异质性,其潜在的致病机制尚未完全阐明。少突胶质细胞(OL)损伤和髓鞘变性是阿尔茨海默病的普遍病理特征。当少突胶质细胞受到淀粉样β(Aβ)毒性作用时,这种损伤会损害髓鞘结构的完整性,导致髓鞘相关蛋白减少。因此,髓鞘完整性受损导致神经信号传输减慢或停止,最终导致认知功能障碍和注意力缺失症的进展。因此,从少突胶质细胞的角度阐明少突胶质细胞与AD之间的关系有助于推进我们对AD发病机制的理解。目的:本研究试图鉴定AD的少突胶质细胞相关生物标志物:方法:从基因表达总库数据库(Gene Expression Omnibus database)中获取 AD 数据集,并利用共识聚类确定亚类。通过差异表达基因(DEGs)分析和少突胶质细胞基因组富集确定枢纽基因。免疫浸润分析采用 CIBERSORT 方法进行。利用机器学习算法和逻辑回归确定了特征基因。利用外部数据集和 AD 模型开发并验证了预测 AD 的诊断提名图。使用 eXtreme Sum 算法确定了一种小分子化合物:结果:通过研究 DEG 与少突胶质细胞基因之间的重叠,发现 46 个基因与 AD 的进展有显著相关性。确定了AD的两个亚类,即A群和B群,并利用机器学习算法构建了一个提名图,确定了9个特征基因。富集分析表明,9个基因涉及细胞凋亡和神经元发育。免疫浸润分析发现,AD 患者和对照组的免疫细胞存在差异。外部数据集和 RT-qPCR 验证显示,AD 患者和对照组之间的特征基因存在差异。预测了五种小分子化合物:结论:研究发现,9 个少突胶质细胞基因可用于创建 AD 诊断工具,这有助于开发新的治疗方法。
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