Zhao Yilong, Xing Wen, Chen Weiqi, Wang Yilong
Department of Neurology, Beijing Tiantan Hospital, Capital Medical University, Beijing, China.
China National Clinical Research Center for Neurological Diseases, Beijing, China.
Front Immunol. 2025 Mar 24;16:1560438. doi: 10.3389/fimmu.2025.1560438. eCollection 2025.
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.
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).
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() and CKLF-like MARVEL transmembrane domain containing 5 () exhibit good diagnostic value. Their high confidence levels were confirmed by validating each biomarker using a different dataset. According to GeneMANIA, VaD pathophysiology is strongly associated with immune and inflammatory responses. The data were used to construct miRNA hub gene, TFs-hub gene, and drug-hub gene networks. Varying levels of immune cell dysregulation were also observed. In the animal experiments, a BCAS mouse model was employed to mimic VaD in humans, further confirmed using the Morris water maze test. The mRNA expression of and was significantly reduced in the BCAS group, which was consistent with the results of the integrated bioinformatics analysis.
and are differentially expressed in the frontal lobes of BCAS mice, suggesting their potential as biomarkers for diagnosing and prognosis of VaD. These findings pave the way for exploring novel molecular mechanisms aimed at preventing or treating VaD.
本研究旨在通过生物信息学方法结合小鼠生物学实验,鉴定关键免疫基因,为血管性痴呆(VaD)的发病机制和诊断提供新的视角。
我们从基因表达综合数据库(GSE186798)中获取基因表达谱。利用综合生物信息学和机器学习技术分析基因表达数据,以确定诊断VaD的潜在关键免疫相关基因。此外,通过受试者工作特征曲线分析评估诊断准确性。利用数据库预测微小RNA、转录因子(TF)和药物调控的枢纽基因。研究免疫细胞浸润情况,以探讨VaD患者免疫细胞的失调。为评估认知障碍,对双侧颈总动脉狭窄(BCAS)小鼠在慢性脑灌注不足30天后进行行为测试。使用定量聚合酶链反应(qPCR)测定BCAS小鼠中枢纽基因的表达。
基因集富集分析和基因集变异分析结果表明,VaD患者中免疫相关通路上调。联合免疫数据集中共纳入1620个免疫基因,并使用GSE186798数据集检测了323个差异表达基因。通过差异基因分析鉴定出13个潜在基因。以免疫系统为主要研究对象进行蛋白质-蛋白质相互作用网络设计和功能富集分析。为评估诊断价值,使用机器学习选择了两个潜在的核心基因。两个假定的枢纽基因,Rac家族小GTP酶1(RAC1)和含CKLF样MARVEL跨膜结构域5(CMTM5)表现出良好的诊断价值。通过使用不同数据集验证每个生物标志物,证实了它们的高置信度。根据GeneMANIA,VaD的病理生理学与免疫和炎症反应密切相关。利用这些数据构建了微小RNA枢纽基因、转录因子-枢纽基因和药物-枢纽基因网络。还观察到不同程度的免疫细胞失调。在动物实验中,采用BCAS小鼠模型模拟人类VaD,通过莫里斯水迷宫试验进一步证实。BCAS组中RAC1和CMTM5的mRNA表达显著降低,这与综合生物信息学分析结果一致。
RAC1和CMTM5在BCAS小鼠额叶中差异表达,表明它们作为VaD诊断和预后生物标志物的潜力。这些发现为探索预防或治疗VaD的新分子机制铺平了道路。