The Institute of Reproduction and Stem Cell Engineering, School of Basic Medical Sciences, Central South University, Changsha, China.
Hunan Guangxiu Hospital, Hunan Normal University, Changsha, China.
Mol Neurobiol. 2024 Sep;61(9):6584-6598. doi: 10.1007/s12035-024-04011-z. Epub 2024 Feb 8.
This study aimed to identify autophagy-related candidate genes for the early diagnosis of Alzheimer's disease (AD) and elucidate their potential molecular mechanisms. Differentially expressed genes (DEGs) and phenotype-associated significant module genes were obtained using the "limma" package and weighted gene co-expression network analysis (WGCNA) based on hippocampal tissue datasets from AD patients and control samples. The intersection between the list of autophagy-related genes (ATGs), DEGs, and module genes was further investigated to obtain AD-autophagy-related differential expression genes (ATDEGs). Subsequently, the least absolute shrinkage and selection operator (LASSO) algorithm was utilized to identify hub genes, and a second intersection was performed with important module genes from the protein-protein interaction (PPI) network to obtain co-hub genes. Finally, a diagnostic model was constructed by receiver operating characteristic (ROC) analysis to determine the candidate genes with high diagnostic efficacy in the external validation set. Moreover, immune infiltration analysis was performed on AD patient brain tissues and explore the correlation between candidate genes and immune cells. We further analyzed the expression level of candidate genes in the SH-SY5Y cells with Aβ (25 µM). Among the 17 identified AD-ATDEGs, ATP6V1E1 stood out with area under the curve (AUC) values of 0.869, 0.817, and 0.714 in the external validation set, underscoring its high diagnostic efficacy in both hippocampal and peripheral blood contexts for AD patients. Meanwhile, ATP6V1E1 expression was positively correlated with effector memory CD4 + T cells, while negatively correlated with natural killer T cells and activated CD4 + T cells. Results from quantitative PCR (qPCR) and immunofluorescence assays indicated a reduction in ATP6V1E1 expression, aligning with our database analysis findings. In summary, ATP6V1E1 as a candidate gene provides a new perspective for the early identification and pathogenesis of AD.
本研究旨在鉴定阿尔茨海默病（AD）早期诊断的自噬相关候选基因，并阐明其潜在的分子机制。使用“limma”包和基于 AD 患者和对照样本海马组织数据集的加权基因共表达网络分析（WGCNA）获得差异表达基因（DEGs）和表型相关显著模块基因。进一步研究自噬相关基因（ATGs）、DEGs 和模块基因的列表之间的交集，以获得 AD 自噬相关差异表达基因（ATDEGs）。随后，使用最小绝对收缩和选择算子（LASSO）算法鉴定枢纽基因，并与蛋白质-蛋白质相互作用（PPI）网络中的重要模块基因进行第二次交集，以获得共枢纽基因。最后，通过接收者操作特征（ROC）分析构建诊断模型，以确定在外部验证集中具有高诊断效能的候选基因。此外，对 AD 患者脑组织进行免疫浸润分析，探讨候选基因与免疫细胞的相关性。我们进一步分析了 Aβ（25 µM）处理的 SH-SY5Y 细胞中候选基因的表达水平。在鉴定的 17 个 AD-ATDEGs 中，ATP6V1E1 脱颖而出，在外部验证集中的曲线下面积（AUC）值分别为 0.869、0.817 和 0.714，突显其在 AD 患者海马和外周血环境中具有较高的诊断效能。同时，ATP6V1E1 的表达与效应记忆 CD4+T 细胞呈正相关，与自然杀伤 T 细胞和活化 CD4+T 细胞呈负相关。定量 PCR（qPCR）和免疫荧光检测结果表明，ATP6V1E1 表达减少，与我们的数据库分析结果一致。总之，ATP6V1E1 作为候选基因，为 AD 的早期识别和发病机制提供了新视角。
