|本期目录/Table of Contents|

[1]司蕊豪,刘羽茜,朱仲康,等.基于生信分析对阿尔茨海默病炎症相关关键基因的筛选及鉴定[J].天津医科大学学报,2023,29(05):486-493.
　SI Rui-hao,LIU Yu-xi,ZHU Zhong-kang,et al.Screening and identification of key genes associated with Alzheimer′s disease inflammation based on bioinformatics analysis[J].Journal of Tianjin Medical University,2023,29(05):486-493.
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基于生信分析对阿尔茨海默病炎症相关关键基因的筛选及鉴定(PDF)
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《天津医科大学学报》[ISSN:1006-8147/CN:12-1259/R]

卷:
29卷
期数:
2023年05期
页码:
486-493
栏目:
生物信息学
出版日期:
2023-09-20

文章信息/Info

Title:
Screening and identification of key genes associated with Alzheimer′s disease inflammation based on bioinformatics analysis
文章编号:
1006-8147（2023）05-0486-08
作者:
司蕊豪1; 刘羽茜1; 朱仲康2; 王旭3; 侯文晓1; 赵丹玉1
（辽宁中医药大学中西医结合学院1.生物化学教研室；2.生理学教研室；3.组织与胚胎学教研室，沈阳 110847）
Author(s):
SI Rui-hao1; LIU Yu-xi1; ZHU Zhong-kang2; WANG Xu3; HOU Wen-xiao1; ZHAO Dan-yu1
（1.Biochemistry Teaching and Research Section；2.Physiology Teaching and Research Office；3.Histology Embryology Teaching and Research Section，Department of College of Integrated Traditional Chinese and Western Medicine，Liaoning University of Traditional Chinese Medicine，Shenyang 110847，China）
关键词:
阿尔茨海默病; 炎症; 生信分析
Keywords:
Alzheimer′s disease; inflammation; bioinformatics analysis
分类号:
R742.8+9
DOI:
-
文献标志码:
A
摘要:
目的：筛选并鉴定阿尔茨海默病（AD）神经炎症相关关键基因。方法：用R语言limma包分别筛选GEO数据库中的数据集GSE144459（Healthy=16，AD=16）和GSE135999（Healthy=6，AD=7）中的差异基因（DEGs）；用在线韦恩图工具对上述获得的差异表达基因（DEG1、DEG2）与NCBI数据库获得（IRGs）取交集，以获得差异表达的IRGs；用R语言clusterProfiler包对其进行功能富集分析；通过STRING数据库（https：//string-db.org）和Cytoscape软件对差异表达IRGs进行PPI网络构建，并用cytoHubba插件来筛选PPI中的关键节点作为候选基因。后续用于LASSO回归分析进一步筛选特征基因并构建分类器，用数据集GSE122063（AD=56，Control=44）验证特征基因的表达并绘制箱线图。结果：本研究共获得106个差异表达的IRGs。富集分析与金黄色葡萄球菌感染、中性粒细胞外陷阱形成、Toll样受体、FcγR介导吞噬作用等功能及信号通路相关。经过PPI网络的构建、LASSO回归分析、分类器构建及受试者工作特征（ROC）曲线绘制以及特征基因的表达验证，最终得出Fcgr2b、Cd14、Fcgr1、Fcgr3、Ly86在AD中具有较强的预测诊断能力。结论：Fcgr2b、Cd14、Fcgr1、Fcgr3、Ly86 在AD中起着关键作用，可以作为AD潜在的诊断和治疗靶点。
Abstract:
Objective：To screen and identify key genes associated with neuroinflammation in Alzheimer′s disease（AD）. Methods： The limma package in R language was used to screen the differentially expressed genes（DEGs） in the datasets GSE144459（Healthy=16，AD=16） and GSE135999（Healthy=6，AD=7） in the GEO database，respectively. The online VennDiagram tool was used to intersect the above differentially expressed genes（DEG1，DEG2） with the inflammation-related genes（IRGs） in NCBI database to obtain the differentially expressed IRGs. The clusterProfiler package in R language was used to perform functional enrichment analysis. PPI networks were constructed for differentially expressed IRGs using the STRING database（https：//string-db.org） and Cytoscape software，and key nodes in the PPI were screened as candidate genes using the cytoHubba plugin. Subsequently，LASSO regression analysis was performed to further screen feature gene and classifier construction，and the expression of the feature gene was verified using the dataset GSE122063（AD=56，Control=44） and the box plot was drawn. Results： A total of 106 differentially expressed IRGs were obtained in this study. Enrichment analysis was associated with Staphylococcus aureus infection，neutrophil extracellular trap formation，Toll-like receptor signaling pathway，FcγR-mediated phagocytosis and other functions and signaling pathways. After the construction of PPT network，LASSO regression analysis，classifier construction，ROC curve plotting and expression verification of feature genes，it is concluded that the Fcgr2b，Cd14，Fcgr1，Fcgr3，and Ly86 had strong predictive and diagnostic capabilities in AD. Conclusion： Fcgr2b，Cd14，Fcgr1，Fcgr3，and Ly86 play key roles in AD and can be used as potential diagnostic and therapeutic targets for AD.

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备注/Memo

备注/Memo:
基金项目 国家自然科学基金资助项目（81803854）；辽宁省教育厅项目资助（LJKMZ20221317）
作者简介 司蕊豪（1993-），女，硕士在读，研究方向：中医药防治代谢疾病；通信作者：赵丹玉，E-mail：danyu1978@163.com。
更新日期/Last Update: 2023-09-25