ArticleInternational journal of general medicine2021
Identification of Hub Genes in Patients with Alzheimer Disease and Obstructive Sleep Apnea Syndrome Using Integrated Bioinformatics Analysis.
Article in International journal of general medicine, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.
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Who cites it
7 citing papers in PubMed.
- Inflammatory and Redox Blood Gene Expression Fingerprint of Severe Obstructive Sleep Apnoea in Patients With Mild Alzheimer's Disease.Journal of inflammation research · 2025Article
- A review and analysis of key biomarkers in Alzheimer's disease.Frontiers in neuroscience · 2024Review
- Identification of novel hub genes for Alzheimer's disease associated with the hippocampus using WGCNA and differential gene analysis.Frontiers in neuroscience · 2024Article
- Identification and Mendelian randomization validation of pathogenic gene biomarkers in obstructive sleep apnea.Frontiers in neurology · 2024Article
- Six mitophagy-related hub genes as peripheral blood biomarkers of Alzheimer's disease and their immune cell infiltration correlation.Frontiers in neuroscience · 2023Article
- Ferroptosis-related genes are considered as potential targets for CPAP treatment of obstructive sleep apnea.Frontiers in neurology · 2023Article
- Construction of a mitochondrial dysfunction related signature of diagnosed model to obstructive sleep apnea.Frontiers in genetics · 2022Article
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6 authors.
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Abstract
backgroundObstructive sleep apnea syndrome (OSA) is associated with an increased risk of Alzheimer's disease (AD). This study aimed to identify the key common genes in AD and OSA and explore molecular mechanism value in AD.
methodsExpression profiles GSE5281 and GSE135917 were acquired from Gene Expression Omnibus (GEO) database, respectively. Weighted gene co-expression network analysis (WGCNA) and R 4.0.2 software were used for identifying differentially expressed genes (DEGs) related to AD and OSA. Function enrichment analyses using Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway, and the protein-protein interaction network (PPI) using the STRING database were subsequently performed on the shared DEGs. Finally, the hub genes were screened from the PPI network using the MCC algorithm of CytoHubba plugin.
resultsSeven modules and four modules were the most significant with AD and OSA by WGCNA, respectively. A total of 33 common genes were screened in AD and OSA by VENN. Functional enrichment analysis indicated that DEGs were mainly involved in cellular response to oxidative stress, neuroinflammation. Among these DEGs, the top 10 hub genes (high scores in cytoHubba) were selected in the PPI network, including AREG, SPP1, CXCL2, ITGAX, DUSP1, COL1A1, SCD, ACTA2, CCND2, ATF3.
conclusionThis study presented ten target genes on the basis of common genes to AD and OSA. These candidate genes may provide a novel perspective to explore the underlying mechanism that OSA leads to an increased risk of AD at the transcriptome level.
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