Du Wenlong, Yu Shihui, Liu Ruiyao, Kong Qingqing, Hao Xin, Liu Yi
Department of Biophysics, School of Life Sciences, Xuzhou Medical University, Xuzhou, 221004, Jiangsu, China.
Department of Bioinformatics, School of Life Sciences, Xuzhou Medical University, Xuzhou, 221004, Jiangsu, China.
J Mol Neurosci. 2025 Jan 14;75(1):5. doi: 10.1007/s12031-024-02291-7.
Alzheimer's disease (AD), a prevalent neurodegenerative disorder, is characterized by mitochondrial dysfunction and immune dysregulation. This study is aimed at developing a risk prediction model for AD by integrating multi-omics data and exploring the interplay between mitochondrial energy metabolism-related genes (MEMRGs) and immune cell dynamics. We integrated four GEO datasets (GSE132903, GSE29378, GSE33000, GSE5281) for differential gene expression analysis, functional enrichment, and weighted gene co-expression network analysis (WGCNA). We identified two key gene modules (turquoise and magenta) significantly correlated with AD. Subsequently, we constructed a risk prediction model incorporating five MEMRGs (MRPL15, RBP4, ABCA1, MPV17, and MRPL37) and clinical factors using LASSO regression. The model demonstrated robust predictive performance (AUC > 0.815) in both internal and external validation (GSE44770) cohorts. Downregulation of MRPL15, RBP4, MPV17, and MRPL37 in AD brain regions (validated using AlzData and qRT-PCR) suggests impaired mitochondrial function. Conversely, ABCA1 upregulation may represent a compensatory response. Furthermore, significant differences in immune cell proportions, particularly gamma delta T cells (p = 0.002) and activated CD4 memory T cells (p = 0.027), were found between AD and non-demented samples. We observed significant correlations between MEMRG expression and specific immune cell fractions, indicating a potential link between mitochondrial dysfunction and immune dysregulation in AD. Our study provides a reliable risk prediction model for AD and highlights the crucial roles of MEMRGs and immune responses in disease pathogenesis, offering potential targets for therapeutic interventions.
阿尔茨海默病（AD）是一种常见的神经退行性疾病，其特征为线粒体功能障碍和免疫失调。本研究旨在通过整合多组学数据，开发一种AD风险预测模型，并探索线粒体能量代谢相关基因（MEMRGs）与免疫细胞动态之间的相互作用。我们整合了四个GEO数据集（GSE132903、GSE29378、GSE33000、GSE5281）进行差异基因表达分析、功能富集和加权基因共表达网络分析（WGCNA）。我们鉴定出两个与AD显著相关的关键基因模块（绿松石色和品红色）。随后，我们使用LASSO回归构建了一个包含五个MEMRGs（MRPL15、RBP4、ABCA1、MPV17和MRPL37）和临床因素的风险预测模型。该模型在内部和外部验证（GSE44770）队列中均表现出强大的预测性能（AUC > 0.815）。AD脑区中MRPL15、RBP4、MPV17和MRPL37的下调（使用AlzData和qRT-PCR验证）表明线粒体功能受损。相反，ABCA1的上调可能代表一种代偿反应。此外，在AD和非痴呆样本之间发现免疫细胞比例存在显著差异，特别是γδ T细胞（p = 0.002）和活化的CD4记忆T细胞（p = 0.027）。我们观察到MEMRG表达与特定免疫细胞亚群之间存在显著相关性，表明AD中线粒体功能障碍与免疫失调之间存在潜在联系。我们的研究为AD提供了一个可靠的风险预测模型，并突出了MEMRGs和免疫反应在疾病发病机制中的关键作用，为治疗干预提供了潜在靶点。
