Zhang Kuo, Yang Kai, Yu Gongchang, Shi Bin
Neck-Pain Hospital of Shoulder and Lumbocrural Shandong First Medical University, Shandong First Medical University & Shandong Academy of Medical Sciences, Jinan, China.
Department of Neurology, Shandong University of Traditional Chinese Medicine, Jinan, China.
Curr Alzheimer Res. 2025;22(1):19-37. doi: 10.2174/0115672050353736241218054012.
Alzheimer's disease (AD) represents the most common neurodegenerative disorder, characterized by progressive cognitive decline and memory loss. Despite the recognition of mitochondrial dysfunction as a critical factor in the pathogenesis of AD, the specific molecular mechanisms remain largely undefined.
This study aimed to identify novel biomarkers and therapeutic strategies associated with mitochondrial dysfunction in AD by employing bioinformatics combined with machine learning methodologies. We performed Weighted Gene Co-expression Network Analysis (WGCNA) utilizing gene expression data from the NCBI Gene Expression Omnibus (GEO) database and isolated mitochondria-related genes through the MitoCarta3.0 database. By intersecting WGCNA-derived module genes with identified mitochondrial genes, we compiled a list of 60 mitochondrial dysfunction- related genes (MRGs) significantly enriched in pathways pertinent to mitochondrial function, such as the citrate cycle and oxidative phosphorylation.
Employing machine learning techniques, including random forest and LASSO, along with the CytoHubba algorithm, we identified key genes with strong diagnostic potential, such as ACO2, CS, MRPS27, SDHA, SLC25A20, and SYNJ2BP, verified through ROC analysis. Furthermore, an interaction network involving miRNA-MRGs-transcription factors and a protein-drug interaction network revealed potential therapeutic compounds such as Congo red and kynurenic acid that target MRGs.
These findings delineate the intricate role of mitochondrial dysfunction in AD and highlight promising avenues for further exploration of biomarkers and therapeutic interventions in this devastating disease.
阿尔茨海默病（AD）是最常见的神经退行性疾病，其特征为进行性认知衰退和记忆丧失。尽管线粒体功能障碍被认为是AD发病机制中的关键因素，但其具体分子机制仍 largely 未明确。
本研究旨在通过生物信息学与机器学习方法相结合，识别与AD中线粒体功能障碍相关的新型生物标志物和治疗策略。我们利用来自NCBI基因表达综合数据库（GEO）的基因表达数据进行加权基因共表达网络分析（WGCNA），并通过MitoCarta3.0数据库分离出线粒体相关基因。通过将WGCNA衍生的模块基因与已识别的线粒体基因进行交叉分析，我们编制了一份包含60个与线粒体功能障碍相关基因（MRGs）的列表，这些基因在与线粒体功能相关的途径中显著富集，如柠檬酸循环和氧化磷酸化。
采用包括随机森林和LASSO在内的机器学习技术以及CytoHubba算法，我们识别出具有强大诊断潜力的关键基因，如ACO2、CS、MRPS27、SDHA、SLC25A20和SYNJ2BP，并通过ROC分析进行了验证。此外，一个涉及miRNA-MRGs-转录因子的相互作用网络和一个蛋白质-药物相互作用网络揭示了潜在的治疗化合物，如针对MRGs的刚果红和犬尿喹啉酸。
这些发现描绘了线粒体功能障碍在AD中的复杂作用，并突出了在这种毁灭性疾病中进一步探索生物标志物和治疗干预措施的有前景的途径。
