ArticleFrontiers in aging neuroscience2022
Identification of Differential Genes of DNA Methylation Associated With Alzheimer's Disease Based on Integrated Bioinformatics and Its Diagnostic Significance.
Article in Frontiers in aging neuroscience, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers.
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Who cites it
8 citing papers in PubMed, 10 citations in OpenAlex.
- Cell-to-cell variability and gain of methylation at polycomb CpG islands as a hallmark of aging.Nature communications · 2026Article
- Identification and Validation of Potential Biomarkers and Therapeutic Targets of COVID-19-related Depression.Combinatorial chemistry & high throughput screening · 2025Article
- Methods in DNA methylation array dataset analysis: A review.Computational and structural biotechnology journal · 2024Review
- A Study on Autophagy Related Biomarkers in Alzheimer's Disease Based on Bioinformatics.Cellular and molecular neurobiology · 2023Article
- Identification of key lipid metabolism-related genes in Alzheimer's disease.Lipids in health and disease · 2023Article
- Perinatal S-Adenosylmethionine Supplementation RepressesInternational journal of molecular sciences · 2023Article
- Machine learning models identify ferroptosis-related genes as potential diagnostic biomarkers for Alzheimer's disease.Frontiers in aging neuroscience · 2022Article
- Comparing gradient boosting and neural networks in the prediction of intersecting genes in gingival epithelial immunity.Journal of Indian Society of PeriodontologyArticle
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Authors and funding
3 authors at 1 institution in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Background: Alzheimer's disease (AD) is a common neurodegenerative disease. The pathogenesis is complex and has not been clearly elucidated, and there is no effective treatment. Recent studies have demonstrated that DNA methylation is closely associated with the pathogenesis of AD, which sheds light on investigating potential biomarkers for the diagnosis of early AD and related possible therapeutic approaches. Methods: Alzheimer's disease patients samples and healthy controls samples were collected from two datasets in the GEO database. Using LIMMA software package in R language to find differentially expressed genes (DEGs). Afterward, DEGs have been subjected to enrichment analysis of GO and KEGG pathways. The PPI networks and Hub genes were created and visualized based on the STRING database and Cytoscape. ROC curves were further constructed to analyze the accuracy of these genes for AD diagnosis. Results: Analysis of the GSE109887 and GSE97760 datasets showed 477 significant DEGs. GO and KEGG enrichment analysis showed terms related to biological processes related to these genes. The top ten Hub genes were found on the basis of the PPI network using the CytoHubba plugin, and the AUC areas of these top ranked genes were all greater than 0.7, showing satisfactory diagnostic accuracy. Conclusion: The study identified the top 10 Hub genes associated with AD-related DNA methylation, of which
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