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2000
Volume 24, Issue 6
  • ISSN: 1871-5273
  • E-ISSN: 1996-3181

Abstract

Background

AD is a demyelinating disease. Myelin damage initiates the pathological process of AD, resulting in abnormal synaptic function and cognitive decline. The myelin sheath formed by oligodendrocytes (OL) is a crucial component of white matter. Investigating AD from the perspective of OL may offer novel diagnostic and therapeutic perspectives.

Objectives

This study aimed to analyze the association between OL-related genes and Alzheimer's disease (AD) using bioinformatics and verify this association molecular biology experiments.

Methods

The AD datasets were acquired from the Gene Expression Omnibus (GEO) database of NCBI. Consensus clustering was employed to determine the subtypes of AD, followed by evaluating the clinical characteristics of these subtypes. Subsequently, immune infiltration analysis of relevant genes and Weighted Gene Co-expression Network Analysis (WGCNA) were conducted to identify modules and hub genes associated with AD progression. The intersection of genes obtained was analyzed using Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analyses. To narrow down the scope and identify OL-related genes with diagnostic potential, three machine learning algorithms were utilized. In addition, the eXtreme Sum (XSum) algorithm was used to screen small molecule drug candidates based on the connectivity map (CMAP) database. Finally, these identified genes were validated using Real-time fluorescence quantitative PCR (RT-qPCR).

Results

Among the three subtypes of AD, Cluster A and Cluster C exhibited increased levels of Braak and neurofibrillary tangles compared to Cluster B. The proportion of females was greater than that of males among the three subclasses of AD. There were no significant differences in age among the three subclasses, but significant differences in gene expression existed. Through WGCNA analysis, 108 genes were identified. Among these, 16 genes were identified as shared genes by the least absolute shrinkage and selection operator (LASSO), random forest (RF), and support vector machines (SVM) algorithms, and logistic regression further determined 11 genes. The establishment of a nomogram demonstrated the significance of these 11 genes in AD. The “XSum” algorithm revealed five drugs with therapeutic potential for AD. RT-qPCR analysis revealed the upregulation and downregulation of the highlighted genes. According to this study, 11 genes related to OL were also found to be associated with immune cell infiltration in AD patients. These genes demonstrated potential diagnostic value for AD. Additionally, we screened five small molecular drugs that exhibit potential therapeutic effects on AD.

Conclusion

This research provides a new perspective for personalized clinical management and treatment of AD.

© 2025 Bentham Science Publishers
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Establishment and Validation of the Diagnostic Value of Oligodendrocyte- related Genes in Alzheimer's Disease
CNS Neurol. Disord. Drug Targets 24, 452 (2025); https://doi.org/10.2174/0118715273339310241205055554
/content/journals/cnsnddt/10.2174/0118715273339310241205055554
/content/journals/cnsnddt/10.2174/0118715273339310241205055554
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  • Article Type:     Research Article
Keyword(s): Alzheimer’s disease; bioinformatics; diagnosis; machine learning; nomogram; Oligodendrocyte

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