基于生信分析和动力学模拟鉴定阿尔茨海默病免疫标志物及靶向药食同源中药预测  

Identification of immune-related biomarkers of Alzheimer’s disease and prediction of medicine-food homology traditional Chinese medicines based on bioinformatics analysis and dynamic simulation

作　　者：梁朋朋 王雅乐 黄海 李桂云 吴红彦 LIANG Pengpeng;WANG Yale;HUANG Hai;LI Guiyun;WU Hongyan(Shenzhen Hospital,Shanghai University of Traditional Chinese Medicine,Shenzhen 518004,China)

机构地区：[1]上海中医药大学深圳医院,广东深圳518004

出　　处：《中草药》2024年第8期2667-2683,共17页Chinese Traditional and Herbal Drugs

基　　金：深圳市“医疗卫生三名工程”项目(SZZYSM202201007);罗湖区软科学研究计划项目(LX20210101);罗湖区软科学研究计划项目(LX202302101)。

摘　　要：目的分析阿尔茨海默病(Alzheimer’s,AD)免疫相关的生物标志物、发病机制、免疫浸润水平和潜在的靶向药食同源中药。方法从GEO数据库中下载GSE5281、GSE132903数据集的表达谱,获得AD差异表达基因(differentially expressed genes,DEGs)。采用加权共表达算法鉴定出AD重要模块基因,再从Imm Port Portal数据库获取免疫相关基因(immune-related genes,IRGs),将这些基因取交集得到免疫重要差异基因;随后应用最小绝对收缩和选择算法(least absolute shrinkage and selection operator,LASSO)及机器学习-支持向量机递归特征消除(support vector machine-recursive feature elimination,SVM-RFE)方法进行分析,筛选出AD共同的免疫相关标志物,并通过基因本体(gene ontology,GO)和京都基因与基因组百科全书(Kyoto encyclopedia of genes and genomes,KEGG)、基因集富集分析(gene set enrichment analysis,GSEA)探索生物途径。然后,通过受试者工作特征(receiver operator characteristic,ROC)曲线来评估其鉴别能力,并在GSE122063数据集中进行验证。此外,建立临床列线图和曲线进行临床应用评估。基于转录样本中不同细胞类型相对丰度算法(cell-type identification by estimating relative subsets of RNA transcripts,Cibersort)和单样本基因集富集分析(single-sample gene set enrichment analysis,ss GSEA)进行免疫细胞浸润分析。最后,运用Coremine Medical、Herb数据库进行中药和成分分析,并进行分子对接和动力学模拟。结果共筛选出1360个DEGs和富半胱氨酸和甘氨酸的蛋白质1(cysteine and glycine rich protein 1,CSRP1)、胶质纤维酸性蛋白(glial fibrillary acidic protein,GFAP)、白细胞介素4受体(interleukin 4 receptor,IL4R)、生长抑素(somatostatin,SST)、人核因子-κB抑制蛋白α(nuclear factor-kappa B inhibitor alpha,NFKBIA)5个生物标志物。GO分析显示AD与神经系统发育和细胞发育的正向调节高度相关;KEGG和GSEA富集结果显示AD与B细胞受体信号�Objective To analyze the immune-related biomarkers,pathogenesis,levels of immune infiltration and potential homology herbs in Alzheimer’s disease(AD).Methods The expression profiles of GSE5281 and GSE132903 were downloaded from GEO database to obtain AD differentially expressed genes(DEGs).The relevant module genes of AD were identified using a weighted coexpression algorithm,and the immune-related genes were subsequently obtained from the ImmPortal database.The intersection of the above mentioned genes was used to obtained to obtain the set of essential immune differential genes.The analysis was then performed using least absolute shrinkage and selection operator(LASSO)and support vector machine-recursive feature elimination(SVM-RFE)methods to screen for common immune biomarkers of AD.Biological pathways were explored through gene ontology(GO)and Kyoto encyclopedia of genes and genomes(KEGG),and gene set enrichment analysis(GSEA).The receiver operator characteristic(ROC)curve was used to evaluate the diagnostic capability of these candidate markers,and validated them in GSE122063 dataset.Besides,we established clinical nomograms and curves for clinical application evaluation.The cell-type identification by estimating relative subsets of RNA transcripts(Cibersort)and single-sample gene set enrichment analysis(ssGSEA)were used to analyze immune cell infiltration in the samples.Finally,Coremine Medical and Herb databases were used to analyze traditional Chinese medicines(TCMs)and components,and molecular docking and kinetic simulation were carried out.Results A total of 1360 differential genes and five biomarkers including cysteine and glycine rich protein 1(CSRP1),glial fibrillary acidic protein(GFAP),interleukin 4 receptor(IL4R),somatostatin(SST),and nuclear factor-kappa B inhibitor alpha(NFKBIA)were screened.GO analysis shows that AD is strongly correlated with positive regulation of neural development and cell development.The KEGG and GSEA enrichment results indicated that AD is most closely related to the B-

关 键 词：阿尔茨海默病 加权共表达分析 机器学习 免疫浸润 生物标志物 人参 当归 厚朴花 20(S)-原人参二醇 

分 类 号：R285[医药卫生—中药学]