Li Hailong, Feng Fei, Xie Shoupin, Ma Yanping, Wang Yafeng, Zhang Fan, Wu Hongyan, Huang Shenghui
Department of General Practice, Luohu Clinical College, School of Medicine, Shantou University, Shenzhen, China.
Department of Geriatrics, Affiliated Hospital of Gansu University of Traditional Chinese Medicine, Lanzhou, China.
IET Syst Biol. 2025 Jan-Dec;19(1):e70018. doi: 10.1049/syb2.70018.
Mitochondrial dynamics (MD) play a crucial role in the genesis of Alzheimer's disease (AD); however, the molecular mechanisms underlying MD dysregulation in AD remain unclear. This study aimed to identify critical molecules of MD that contribute to AD progression using GEO data and bioinformatics approaches. The GSE63061 dataset comparing AD patients with healthy controls was analysed, WGCNA was employed to identify co-expression modules and differentially expressed genes (DEGs) and LASSO model was developed and verified using the DEGs to screen for potential biomarkers. A PPI network was built to predict upstream miRNAs, which were experimentally validated using luciferase reporter assays. A total of 3518 DEGs were identified (2209 upregulated, 1309 downregulated; |logFC| > 1.5, adjusted p < 0.05). WGCNA revealed 160 MD-related genes. LASSO regression selected HIBCH and MGME1 as novel biomarkers with significant downregulation in AD (fold change > 2, p < 0.001). KEGG enrichment analysis highlighted pathways associated with neurodegeneration. Luciferase assays confirmed direct binding of miR-922 to the 3'UTR of MGME1. HIBCH and MGME1 are promising diagnostic biomarkers for AD with AUC values of 0.73 and 0.74. Mechanistically, miR-922 was experimentally validated to directly bind MGME1 3'UTR.
线粒体动力学(MD)在阿尔茨海默病(AD)的发病机制中起着关键作用;然而,AD中MD失调的分子机制仍不清楚。本研究旨在利用GEO数据和生物信息学方法,确定促成AD进展的MD关键分子。分析了比较AD患者与健康对照的GSE63061数据集,采用加权基因共表达网络分析(WGCNA)来识别共表达模块和差异表达基因(DEG),并使用DEG建立和验证套索(LASSO)模型以筛选潜在生物标志物。构建蛋白质-蛋白质相互作用(PPI)网络来预测上游微小RNA(miRNA),并使用荧光素酶报告基因检测进行实验验证。共鉴定出3518个DEG(2209个上调,1309个下调;|logFC|>1.5,校正p<0.05)。WGCNA揭示了160个与MD相关的基因。LASSO回归选择羟异丁酰辅酶A水解酶(HIBCH)和线粒体基因组维护因子1(MGME1)作为AD中显著下调的新型生物标志物(变化倍数>2,p<0.001)。京都基因与基因组百科全书(KEGG)富集分析突出了与神经退行性变相关的通路。荧光素酶检测证实miR-922与MGME1的3'非翻译区(UTR)直接结合。HIBCH和MGME1是很有前景的AD诊断生物标志物,曲线下面积(AUC)值分别为0.73和0.74。从机制上讲,实验验证了miR-922直接结合MGME1的3'UTR。