Wang Li, Yu Chunjiang, Tao Ye, Yang Xiumei, Jiang Qiao, Yu Haiyu, Zhang Jiejun
Department of Geriatrics, The Second Affiliated Hospital of the Harbin Medical University, Harbin, China.
Department of Neurology, The Second Affiliated Hospital of the Harbin Medical University, Harbin, China.
Front Genet. 2022 Nov 24;13:1038585. doi: 10.3389/fgene.2022.1038585. eCollection 2022.
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.
阿尔茨海默病(AD)和血管性痴呆(VD)是痴呆最常见的两种形式,症状相似,有时难以区分。为了研究它们不同的潜在机制,我们鉴定了这些疾病患者血液和脑样本中差异表达的基因,并进行了加权基因共表达网络分析和其他生物信息学分析。加权基因共表达网络分析基于这两种疾病之间基因表达的差异挖掘出不同的模块。富集分析和蛋白质 - 蛋白质相互作用网络的生成用于确定每种疾病的核心途径。模块显著参与了cAMP和AMPK信号通路,这可能在AD和VD中调节细胞死亡。cAMP和神经营养因子信号通路的基因,包括[此处原文缺失具体基因名称],被确定为关键标志物。使用最小绝对收缩和选择算子方法,生成了AD和VD的诊断模型,并通过分析患者血液中的基因表达进行了验证。此外,使用单样本基因集富集分析来表征免疫细胞向脑组织的浸润情况。结果表明,在AD和VD患者的脑组织中,DCs和pDCs细胞的浸润增加,而B细胞和TFH细胞的浸润减少。总之,基于靶基因的分类显示出良好的诊断效率,填补了诊断领域的空白或优化了现有的诊断模型,可用于区分AD和VD。