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Updated: Jun 6, 2026
Mapping Alzheimer's Disease Variants to Their Target Genes Using Computational Analysis of Chromatin Configuration
Published on: January 9, 2020
Identification of the m6A/m5C/m1A methylation modification genes in Alzheimer's disease based on bioinformatic
Qifa Tan1, Desheng Zhou2, Yuan Guo1
1Ganzhou City Key Laboratory of Mental Health, The Third People’s Hospital of Ganzhou City, Ganzhou 341000, Jiangxi, China.
Background:
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.
Materials And Methods:
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.
Results:
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.
Conclusions:
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.
Insights
This study explores RNA methylation
Area of Science:
- Neuroscience
- Genomics
- Immunology
Background:
- Alzheimer's disease (AD) pathogenesis remains incompletely understood.
- RNA modifications, including m6A, m5C, and m1A, are implicated in disease progression.
- This research investigates the role of RNA methylation in AD.
Purpose of the Study:
- To identify key RNA methylation regulators in Alzheimer's disease.
- To explore the subtypes and immune characteristics of AD based on methylation patterns.
- To develop predictive models and regulatory networks for AD.
Main Methods:
- Differential gene expression analysis of RNA methylation regulators in AD datasets (GSE33000, GSE122063, GSE44770).
- Consensus clustering to identify AD subtypes.
- Machine learning models (including SVM) to select significant genes.
- Construction of gene-drug and ceRNA regulatory networks using Cytoscape.
Main Results:
- 24 dysregulated methylation-associated genes identified in AD patients, linked to immune characteristics.
- Two distinct AD subtypes discovered, with Cluster 2 showing heightened immune activity.
- SVM model achieved high accuracy (AUC=0.947) in predicting AD, identifying five key genes (YTHDF1, METTL3, DNMT1, DNMT3A, ALKBH1).
- Established gene-drug and ceRNA regulatory networks.
Conclusions:
- The study provides novel insights into the molecular and immune mechanisms of AD.
- Findings contribute to a better understanding of Alzheimer's disease pathogenesis.
- Identified key genes and networks offer potential therapeutic targets.
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