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通过综合生物信息学和炎症浸润鉴定血管性痴呆中免疫相关基因

Identification of immune-associated genes in vascular dementia by integrated bioinformatics and inflammatory infiltrates.

作者信息

Wu Fangchao, Zhang Junling, Wang Qian, Liu Wenxin, Zhang Xinlei, Ning Fangli, Cui Mengmeng, Qin Lei, Zhao Guohua, Liu Di, Lv Shi, Xu Yuzhen

机构信息

Department of Rehabilitation Medicine, Sir Run Run Shaw Hospital, Zhejiang University School of Medicine, Hangzhou 310016, China.

Shandong Medicine Technician College, Taian 271000, China.

出版信息

Heliyon. 2024 Feb 10;10(4):e26304. doi: 10.1016/j.heliyon.2024.e26304. eCollection 2024 Feb 29.

Abstract

OBJECTIVE

Dysregulation of the immune system plays a vital role in the pathological process of vascular dementia, and this study aims to spot critical biomarkers and immune infiltrations in vascular dementia employing a bioinformatics approach.

METHODS

We acquired gene expression profiles from the Gene Expression Database. The gene expression data were analyzed using the bioinformatics method to identify candidate immune-related central genes for the diagnosis of vascular dementia. and the diagnostic value of nomograms and Receiver Operating Characteristic (ROC) curves were evaluated. We also examined the role of the VaD hub genes. Using the database and potential therapeutic drugs, we predicted the miRNA and lncRNA controlling the Hub genes. Immune cell infiltration was initiated to examine immune cell dysregulation in vascular dementia.

RESULTS

1321 immune genes were included in the combined immune dataset, and 2816 DEGs were examined in GSE122063. Twenty potential genes were found using differential gene analysis and co-expression network analysis. PPI network design and functional enrichment analysis were also done using the immune system as the main subject. To create the nomogram for evaluating the diagnostic value, four potential core genes were chosen by machine learning. All four putative center genes and nomograms have a solid diagnostic value (AUC ranged from 0.81 to 0.92). Their high confidence level became unquestionable by validating each of the four biomarkers using a different dataset. According to GeneMANIA and GSEA enrichment investigations, the pathophysiology of VaD is strongly related to inflammatory responses, drug reactions, and central nervous system degeneration. The data and Hub genes were used to construct a ceRNA network that includes three miRNAs, 90 lncRNA, and potential VaD therapeutics. Immune cells with varying dysregulation were also found.

CONCLUSION

Using bioinformatic techniques, our research identified four immune-related candidate core genes (HMOX1, EBI3, CYBB, and CCR5). Our study confirms the role of these Hub genes in the onset and progression of VaD at the level of immune infiltration. It predicts potential RNA regulatory pathways control VaD progression, which may provide ideas for treating clinical disease.

摘要

目的

免疫系统失调在血管性痴呆的病理过程中起重要作用,本研究旨在采用生物信息学方法找出血管性痴呆中的关键生物标志物和免疫浸润情况。

方法

我们从基因表达数据库获取基因表达谱。使用生物信息学方法分析基因表达数据,以鉴定用于诊断血管性痴呆的候选免疫相关核心基因,并评估列线图和受试者工作特征(ROC)曲线的诊断价值。我们还研究了血管性痴呆枢纽基因的作用。利用数据库和潜在治疗药物,我们预测了调控枢纽基因的miRNA和lncRNA。启动免疫细胞浸润研究以检查血管性痴呆中的免疫细胞失调情况。

结果

合并后的免疫数据集中包含1321个免疫基因,在GSE122063中检测到2816个差异表达基因(DEG)。通过差异基因分析和共表达网络分析发现了20个潜在基因。还以免疫系统为主要研究对象进行了蛋白质-蛋白质相互作用(PPI)网络设计和功能富集分析。为创建用于评估诊断价值的列线图,通过机器学习选择了4个潜在核心基因。所有4个假定的中心基因和列线图都具有可靠的诊断价值(AUC范围为0.81至0.92)。通过使用不同数据集对这4种生物标志物中的每一种进行验证,它们的高置信度变得毋庸置疑。根据GeneMANIA和基因集富集分析(GSEA)研究,血管性痴呆的病理生理学与炎症反应、药物反应和中枢神经系统退化密切相关。利用数据和枢纽基因构建了一个ceRNA网络,其中包括3个miRNA、90个lncRNA以及潜在的血管性痴呆治疗药物。还发现了免疫细胞失调程度各异的情况。

结论

通过生物信息学技术,我们的研究鉴定出4个免疫相关候选核心基因(血红素加氧酶1、EBI3、细胞色素b-245β链和C-C趋化因子受体5)。我们的研究在免疫浸润水平上证实了这些枢纽基因在血管性痴呆发病和进展中的作用。它预测了控制血管性痴呆进展的潜在RNA调控途径,这可能为临床疾病治疗提供思路。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/362f/10879030/40c182633d1c/gr1.jpg

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