Huang Cheng, Liu Ying, Wang Shuxin, Xia Jinjun, Hu Di, Xu Rui
Department of Neurology, Second Affiliated Hospital of Army Medical University (Xinqiao Hospital), Chongqing, China.
Department of Geriatrics, Chongqing General Hospital, Chongqing University, Chongqing, China.
Neuromolecular Med. 2025 Jan 6;27(1):6. doi: 10.1007/s12017-024-08822-0.
Alzheimer's disease (AD) is a prototypical neurodegenerative disorder, predominantly affecting individuals in the presenile and elderly populations, with an etiology that remains elusive. This investigation aimed to elucidate the alterations in anoikis-related genes (ARGs) in the AD brain, thereby expanding the repertoire of biomarkers for the disease. Using publically available gene expression data for the hippocampus from both healthy and AD subjects, differentially expressed genes (DEGs) were identified. Subsequent intersection with a comprehensive list of 575 ARGs yielded a subset for enrichment analysis. Machine learning algorithms were employed to identify potential biomarker, which was validated in an AD animal model. Additionally, gene set enrichment analysis was conducted on the biomarker and its interacting genes and microRNAs were predicted through online databases. To assess its biological functions, the expression of the marker was suppressed in an in vitro model to examine cell viability and inflammation-related indicators. Furthermore, following treatment with the inhibitor, the dysregulated metabolites in the hippocampus of the model mice were evaluated. Forty-seven ARGs were ultimately identified, with HSP90B1 emerging as a central marker. HSP90B1 was found to be significantly up-regulated in AD hippocampal samples and its inhibition conferred increased cell viability and reduced levels of inflammatory factors in amyloid β-protein (Aβ)-treated cells. A total of 24 differentially expressed metabolites were confidently identified between model mice and those with low HSP90B1 expression, with bioinformatics analysis shedding light on the molecular underpinnings of HSP90B1's involvement in AD. Collectively, these findings may inform novel insights into the pathogenesis, mechanisms, or therapeutic strategies for AD.
阿尔茨海默病（AD）是一种典型的神经退行性疾病，主要影响老年前期和老年人群体，其病因仍不清楚。本研究旨在阐明AD大脑中失巢凋亡相关基因（ARGs）的变化，从而扩大该疾病的生物标志物范围。利用健康受试者和AD受试者海马体的公开基因表达数据，确定差异表达基因（DEGs）。随后与575个ARGs的综合列表进行交叉分析，得到一个用于富集分析的子集。采用机器学习算法识别潜在的生物标志物，并在AD动物模型中进行验证。此外，对生物标志物及其相互作用基因进行基因集富集分析，并通过在线数据库预测微小RNA。为了评估其生物学功能，在体外模型中抑制该标志物的表达，以检测细胞活力和炎症相关指标。此外，在用抑制剂处理后，评估模型小鼠海马体中失调的代谢物。最终确定了47个ARGs，其中HSP90B1成为核心标志物。研究发现HSP90B1在AD海马样本中显著上调，抑制它可提高细胞活力，并降低淀粉样β蛋白（Aβ）处理细胞中的炎症因子水平。在模型小鼠和低HSP90B1表达的小鼠之间共确定了24种差异表达的代谢物，生物信息学分析揭示了HSP90B1参与AD的分子基础。总的来说，这些发现可能为AD的发病机制、机理或治疗策略提供新的见解。
