Yan Chen, Chen Li, Yinhui Yao, Yazhen Shang
Institute of Traditional Chinese Medicine, Chengde Medical University / Hebei Province Key Research Office of Traditional Chinese Medicine Against Dementia / Hebei Province Key Laboratory of Traditional Chinese Medicine Research and Development / Hebei Key Laboratory of Nerve Injury and Repair, Chengde, 067000, P.R. China.
Faculty of Integrated Traditional Chinese and Western Medicine, Hebei University of Chinese Medicine, Shijiazhuang, P.R. China.
Curr Alzheimer Res. 2024;21(6):437-455. doi: 10.2174/0115672050338777241028071955.
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.
Here, an attempt is made in this study to identify oligodendrocyte-related biomarkers of AD.
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.
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.
It was found that 9 oligodendrocyte genes can be used to create a diagnostic tool for AD, which could help in developing new treatments.
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AD病理学的普遍特征。当少突胶质细胞受到β-淀粉样蛋白（Aβ）毒性作用时，这种损伤会损害髓鞘的结构完整性，并导致髓鞘相关蛋白减少。因此，髓鞘完整性受损会导致神经信号传递减慢或停止，并最终导致认知功能障碍和AD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。
本研究旨在识别AD的少突胶质细胞相关生物标志物。
从基因表达综合数据库中获取AD数据集，并用于一致性聚类以识别亚类。通过差异表达基因（DEG）分析和少突胶质细胞基因集富集来识别枢纽基因。使用CIBERSORT方法进行免疫浸润分析。使用机器学习算法和逻辑回归识别特征基因。开发了用于预测AD的诊断列线图，并使用外部数据集和AD模型进行验证。使用极端求和算法识别一种小分子化合物。
通过检查DEG与少突胶质细胞基因之间的重叠，发现46个基因与AD进展显著相关。识别出AD的两个亚类，即A簇和B簇，并使用机器学习算法识别出9个特征基因以构建列线图。富集分析表明，9个基因参与细胞凋亡和神经元发育。免疫浸润分析发现AD患者与对照组之间免疫细胞存在差异。外部数据集和RT-qPCR验证显示AD患者与对照组之间特征基因存在差异。预测出5种小分子化合物。
发现9个少突胶质细胞基因可用于创建AD的诊断工具，这有助于开发新的治疗方法。
