ArticleFrontiers in immunology2025
Integrated bioinformatics analysis and biological experiments to identify key immune genes in vascular dementia.
Article in Frontiers in immunology, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.
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Who cites it
2 citing papers in PubMed.
- Molecular pathways in vascular cognitive impairment and dementia: focus on synaptic plasticity and epigenetic modifications.Frontiers in aging neuroscience · 2026Review
- Temporal Evolution of Brain Inflammation in the Bilateral Common Carotid Artery Stenosis Model of Chronic Cerebral Hypoperfusion.Journal of inflammation research · 2026Review
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Authors and funding
4 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Objectives: This study aimed to identify key immune genes to provide new perspectives on the mechanisms and diagnosis of vascular dementia (VaD) based on bioinformatic methods combined with biological experiments in mice. Methods: We obtained gene expression profiles from a Gene Expression Omnibus database (GSE186798). The gene expression data were analysed using integrated bioinformatics and machine learning techniques to pinpoint potential key immune-related genes for diagnosing VaD. Moreover, the diagnostic accuracy was evaluated through receiver operating characteristic curve analysis. The microRNA, transcription factor (TF), and drug-regulating hub genes were predicted using the database. Immune cell infiltration has been studied to investigate the dysregulation of immune cells in patients with VaD. To evaluate cognitive impairment, mice with bilateral common carotid artery stenosis (BCAS) were subjected to behavioural tests 30 d after chronic cerebral hypoperfusion. The expression of hub genes in the BCAS mice was determined using a quantitative polymerase chain reaction(qPCR). Results: The results of gene set enrichment and gene set variation analyses indicated that immune-related pathways were upregulated in patients with VaD. A total of 1620 immune genes were included in the combined immune dataset, and 323 differentially expressed genes were examined using the GSE186798 dataset. Thirteen potential genes were identified using differential gene analysis. Protein-protein interaction network design and functional enrichment analysis were performed using the immune system as the main subject. To evaluate the diagnostic value, two potential core genes were selected using machine learning. Two putative hub genes, Rac family small GTPase 1( Conclusions:
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