ArticleBioMed research international2021
A Comprehensive Analysis Identified Hub Genes and Associated Drugs in Alzheimer's Disease.
Article in BioMed research international, 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.
- Impact of Mast Cell Activation on Neurodegeneration: A Potential Role for Gut-Brain Axis andNeurology international · 2024Review
- Microglial cannabinoid receptor type II stimulation improves cognitive impairment and neuroinflammation in Alzheimer's disease mice by controlling astrocyte activation.Cell death & disease · 2024Article
- SR-TWAS: leveraging multiple reference panels to improve transcriptome-wide association study power by ensemble machine learning.Nature communications · 2024Article
- Gene Co-Expression Analysis of Multiple Brain Tissues Reveals Correlation of FAM222A Expression with Multiple Alzheimer's Disease-Related Genes.Journal of Alzheimer's disease : JAD · 2024Article
- Sevoflurane upregulates neuron death process-related Ddit4 expression by NMDAR in the hippocampus.Aging · 2023Article
- Genome-wide association study of brain biochemical phenotypes reveals distinct genetic architecture of Alzheimer's disease related proteins.Molecular neurodegeneration · 2023Article
- Landscape of immune infiltration in entorhinal cortex of patients with Alzheimer's disease.Frontiers in pharmacology · 2022Article
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Authors and funding
8 authors.
Funding
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Abstract
Alzheimer's disease (AD) is the most common neurodegenerative disease among the elderly and has become a growing global health problem causing great concern. However, the pathogenesis of AD is unclear and no specific therapeutics are available to provide the sustained remission of the disease. In this study, we used comprehensive bioinformatics to determine 158 potential genes, whose expression levels changed between the entorhinal and temporal lobe cortex samples from cognitively normal individuals and patients with AD. Then, we clustered these genes in the protein-protein interaction analysis and identified six significant genes that had more biological functions. Besides, we conducted a drug-gene interaction analysis of module genes in the drug-gene interaction database and obtained 26 existing drugs that might be applied for the prevention and treatment of AD. In addition, a predictive model was built based on the selected genes using different machine learning algorithms to identify individuals with AD. These findings may provide new insights into AD therapy.
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