Tan Qifa, Zhou Desheng, Guo Yuan, Chen Haijun, Xie Peng
Ganzhou City Key Laboratory of Mental Health, The Third People’s Hospital of Ganzhou City, Ganzhou 341000, Jiangxi, China.
Guangzhou Medical University, Guangzhou 510182, Guangdong, China.
Aging (Albany NY). 2024 Oct 31;16(21):13340-13355. doi: 10.18632/aging.206146.
As a progressive neurodegenerative disease, the comprehensive understanding of the pathogenesis of Alzheimer's disease (AD) is yet to be clarified. Modifications in RNA, including m6A/m5C/m1A, affect the onset and progression of many diseases. Consequently, this study focuses on the role of methylation modification in the pathogenesis of AD.
Three AD-related datasets, namely GSE33000, GSE122063, and GSE44770, were acquired from GEO. Differential analysis of m6A/m5C/m1A regulator genes was conducted. Applying a consensus clustering approach, distinct subtypes within AD were identified as per the expression patterns of relevant differentially expressed genes. Machine learning models were constructed to identify five significant genes from the best model. The analysis of hub gene-based drug regulatory networks and ceRNA regulatory networks was conducted by Cytoscape.
In comparison to non-AD patients, 24 genes were identified as dysregulated in AD patients, and these genes were associated with various immunological characteristics. Two distinct clusters were successfully identified through consensus clustering, with cluster 2 demonstrating higher immune characteristics compared to cluster 1. The performance of four machine learning models was determined by conducting a receiver operating characteristic (ROC) analysis. The analysis revealed that the SVM model achieved the highest AUC value of 0.947. Five genes (YTHDF1, METTL3, DNMT1, DNMT3A, ALKBH1) were selected as the predicted genes. Finally, a hub gene-based Gene-Drug regulatory network and a ceRNA regulatory network were successfully developed.
The findings offered fresh perspectives on the molecular patterns and immune mechanisms underlying AD, contributing valuable insights into our understanding of this complex neurodegenerative disorder.
作为一种进行性神经退行性疾病，阿尔茨海默病（AD）发病机制的全面理解仍有待阐明。RNA修饰，包括m6A/m5C/m1A，影响许多疾病的发生和发展。因此，本研究聚焦于甲基化修饰在AD发病机制中的作用。
从基因表达综合数据库（GEO）获取三个与AD相关的数据集，即GSE33000、GSE122063和GSE44770。对m6A/m5C/m1A调节基因进行差异分析。应用共识聚类方法，根据相关差异表达基因的表达模式识别AD内的不同亚型。构建机器学习模型以从最佳模型中识别五个重要基因。通过Cytoscape进行基于枢纽基因的药物调控网络和竞争性内源RNA（ceRNA）调控网络分析。
与非AD患者相比，在AD患者中鉴定出24个失调基因，这些基因与各种免疫特征相关。通过共识聚类成功识别出两个不同的簇，与簇1相比，簇2表现出更高的免疫特征。通过进行受试者工作特征（ROC）分析确定了四种机器学习模型的性能。分析显示支持向量机（SVM）模型获得最高的曲线下面积（AUC）值0.947。选择五个基因（YTHDF1、METTL3、DNMT1、DNMT3A、ALKBH1）作为预测基因。最后，成功构建了基于枢纽基因的基因 - 药物调控网络和ceRNA调控网络。
这些发现为AD潜在的分子模式和免疫机制提供了新的视角，为我们理解这种复杂的神经退行性疾病提供了有价值的见解。
