Wu Yufu, Yang Jiali, Guo Linlin, Hao Dandan, Zhang Wan, Chen Hu, Pei Lin
Neurology Department, Beijing Geriatric Hospital, Beijing, 100095, China.
School of Basic Medicine, Hebei University of Chinese Medicine, Shijiazhuang, 050200, China.
Comb Chem High Throughput Screen. 2025;28(6):944-962. doi: 10.2174/0113862073291279240409035856.
Alzheimer's disease (AD) is a prevalent neurodegenerative condition among the elderly population and the most common form of dementia, however, we lack potent interventions to arrest its inherent pathogenic vectors. Robust evidence indicates thermoregulatory perturbations during and before the onset of symptoms. Therefore, temperature-regulated biomarkers may offer clues to therapeutic targets during the presymptomatic stage.
The purpose of this study is to develop and assess a thermoregulation-related gene prediction model for Alzheimer's Disease diagnosis.
This study aims to utilize microarray bioinformatic analysis to identify the potential biomarkers of AD by analyzing four microarray datasets (GSE48350, GSE5281, GSE122063, and GSE181279) of AD patients. Furthermore, thermoregulation-associated hub genes were identified, and the expression patterns in the brain were explored. In addition, we explored the infiltration of immune cells with thermoregulation-related hub genes. Diagnostic marker validation was then performed at the single-cell level. Finally, the prediction of targeted drugs was performed based on the hub genes.
Through the analysis of four datasets pertaining to AD, a total of five genes associated with temperature regulation were identified. Notably, CCK, CXCR4, SLC27A4, and SLC17A6 emerged as diagnostic markers indicative of AD-related brain injury. Furthermore, in the examination of peripheral blood samples from AD patients, SLC27A4 and CXCR4 were identified as pivotal diagnostic indicators. Regrettably, animal experimentation was not pursued to validate the data; rather, an assessment of temperature regulation-related genes was conducted. Future investigations will be undertaken to establish the correlation between these genes and AD pathology.
Overall, CCK, CXCR4, SLC27A4, and SLC17A6 can be considered pivotal biomarkers for diagnosing the pathogenesis and molecular functions of AD.
阿尔茨海默病（AD）是老年人群中常见的神经退行性疾病，也是最常见的痴呆形式，然而，我们缺乏有效的干预措施来阻止其内在的致病因素。有力证据表明，在症状出现期间及之前存在体温调节紊乱。因此，温度调节生物标志物可能为症状前阶段的治疗靶点提供线索。
本研究旨在开发并评估一种用于阿尔茨海默病诊断的体温调节相关基因预测模型。
本研究旨在利用微阵列生物信息分析，通过分析AD患者的四个微阵列数据集（GSE48350、GSE5281、GSE122063和GSE181279）来识别AD的潜在生物标志物。此外，确定了与体温调节相关的枢纽基因，并探索了其在大脑中的表达模式。此外，我们研究了与体温调节相关枢纽基因的免疫细胞浸润情况。然后在单细胞水平上进行诊断标志物验证。最后，基于枢纽基因进行靶向药物预测。
通过对四个与AD相关的数据集进行分析，共鉴定出五个与温度调节相关的基因。值得注意的是，CCK、CXCR4、SLC27A4和SLC1 [此处原文似乎有误，应为SLC17A6]被确定为指示AD相关脑损伤的诊断标志物。此外，在对AD患者外周血样本的检测中，SLC27A4和CXCR4被确定为关键诊断指标。遗憾的是，未进行动物实验来验证数据；而是对与温度调节相关的基因进行了评估。未来将进行进一步研究以确定这些基因与AD病理学之间的相关性。
总体而言，CCK、CXCR4、SLC27A4和SLC17A6可被视为诊断AD发病机制和分子功能的关键生物标志物。
