Transcriptome analysis reveals potential marker genes for diagnosis of Alzheimer's disease and vascular dementia

• Published in: Frontiers in genetics Vol. 13; p. 1038585
• Main Authors: Wang, Li, Yu, Chunjiang, Tao, Ye, Yang, Xiumei, Jiang, Qiao, Yu, Haiyu, Zhang, Jiejun
• Format: Journal Article
• Language: English
• Published: Switzerland Frontiers Media S.A 24.11.2022
• Subjects: Alzheimer’s disease; Genetics; immune cell infiltration; potential mechanism; vascular dementia; WGCNA; immune cell infiltration; potential mechanism; Alzheimer’s disease; WGCNA; vascular dementia
• ISSN: 1664-8021; 1664-8021
• DOI: 10.3389/fgene.2022.1038585

Alzheimer's disease (AD) and vascular dementia (VD) are the two most common forms of dementia, share similar symptoms, and are sometimes difficult to distinguish. To investigate the potential mechanisms by which they differ, we identified differentially expressed genes in blood and brain samples from patients with these diseases, and performed weighted gene co-expression network analysis and other bioinformatics analyses. Weighted gene co-expression network analysis resulted in mining of different modules based on differences in gene expression between these two diseases. Enrichment analysis and generation of a protein-protein interaction network were used to identify core pathways for each disease. Modules were significantly involved in cAMP and AMPK signaling pathway, which may be regulated cell death in AD and VD. Genes of cAMP and neurotrophin signaling pathways, including , , , , , and , were identified as key markers. Using the least absolute shrinkage and selection operator method, a diagnostic model for AD and VD was generated and verified through analysis of gene expression in blood of patients. Furthermore, single sample gene set enrichment analysis was used to characterize immune cell infiltration into brain tissue. That results showed that infiltration of DCs and pDCs cells was increased, and infiltration of B cells and TFH cells was decreased in the brain tissues of patients with AD and VD. In summary, classification based on target genes showed good diagnostic efficiency, and filled the gap in the diagnostic field or optimizes the existing diagnostic model, which could be used to distinguish between AD and VD.