上海交通大学学报（医学版）

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2021 , Vol. 41 >Issue 6: 749 - 755

DOI: https://doi.org/10.3969/j.issn.1674-8115.2021.06.007

论著 · 基础研究

基于GEO数据库筛选狼疮性肾炎的关键基因和信号通路

  • 刘音 ,
  • 杨涛 ,
  • 谢裕赛 ,
  • 王玉柱
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  • 1.北京市海淀医院肾内科，北京 100080
    2.中国医科大学基础医学院病理学教研室，沈阳 110122
刘 音（1980—），女，主治医师，学士；电子信箱: 790144859@qq.com。

网络出版日期: 2021-06-29

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Screening of key genes and pathways involved in lupus nephritis based on GEO database

  • Yin LIU ,
  • Tao YANG ,
  • Yu-sai XIE ,
  • Yu-zhu WANG
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  • 1.Department of Nephrology, Beijing Haidian Hospital, Beijing 100080, China
    2.Department of Pathology, College of Basic Medical Sciences, China Medical University, Shenyang 110112, China

Online published: 2021-06-29

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摘要

目的·利用生物信息学分析方法筛选狼疮性肾炎相关差异表达基因及相关信号通路。方法·从GEO公共数据库中下载GSE32591数据集矩阵数据，应用R软件limma包进行标准化以及筛选差异表达基因，应用ggpubr和pheatmap包对差异基因绘制火山图及热图。应用DAVID在线数据库对差异表达基因进行GO（Gene Ontology）分析和KEGG （Kyoto Encyclopedia of Genes and Genomes）通路分析，使用R语言ggplot包绘制柱状图及气泡图。运用STRING和Cytoscape软件构建差异表达基因的蛋白质-蛋白质相互作用网络，应用MCODE及cytohubba插件筛选出参与狼疮性肾炎的最显著模块及枢纽基因。采用GSE99339数据集验证枢纽基因的差异表达。结果·通过limma包分析GSE32591数据集，获得了367个差异表达基因，包括253个上调基因及114个下调基因。GO分析和KEGG 通路富集分析表明，差异表达基因在病毒防御反应、质膜外侧、甲型流感、结核病、EB病毒感染、补体途径等方面显著富集。应用STRING和Cytoscape构建了差异表达基因的蛋白互作网络，并鉴定出与狼疮性肾炎相关的10个枢纽基因，在验证数据集GSE99339中有显著的差异表达。结论·获得的367个差异表达基因和10个枢纽基因可能是狼疮性肾炎潜在的生物标志物。

关键词： GEO数据库; 狼疮性肾炎; 系统性红斑狼疮; 生物信息学; 差异表达基因

本文引用格式

刘音 , 杨涛 , 谢裕赛 , 王玉柱 . 基于GEO数据库筛选狼疮性肾炎的关键基因和信号通路[J]. 上海交通大学学报（医学版）, 2021 , 41(6) : 749 -755 . DOI: 10.3969/j.issn.1674-8115.2021.06.007

Abstract

Objective

·To identify the differentially expressed genes and pathways involved in lupus nephritis (LN) using bioinformatics analysis.

Methods

·The matrix data of GSE32591 dataset was downloaded from the GEO database, and the limma package of R software was applied to standardize and screen the differentially expressed genes. The volcano map and heatmap of the differentially expressed genes were drawn by ggpubr and pheatmap packages. Gene Ontology (GO) analysis and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analysis of differentially expressed genes were performed using DAVID online database, and bar plot and bubble chart were drawn using R software ggplot package. STRING database and Cytoscape software were used to construct protein interaction networks of differentially expressed genes, and MCODE and cytohubba plug-ins were used to screen out the most significant modules and key genes involved in lupus nephritis. GSE99339 dataset was used to verify the differential expression of hub genes.

Results

·The GSE32591 data set was analyzed through the limma package, and 367 differentially expressed genes were obtained, including 253 up-regulated genes and 114 down-regulated genes. GO analysis and KEGG pathway enrichment analysis showed that differentially expressed genes were significantly enriched in virus defense response, cytoplasmic matrix, influenza A, tuberculosis, EB virus infection, complement pathway, etc. A protein interaction network of differentially expressed genes was constructed by STRING and Cytoscape, and 10 pivotal genes related to LN were identified. The hub genes are significantly differentially expressed in the GSE99339 validation dataset.

Conclusion

·The 367 differentially expressed genes and 10 hub genes are potential biomarkers of LN.

Key words： GEO database; lupus nephritis; systemic lupus erythematosus; bioinformatics; differentially expressed genes

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