基于基因芯片的肾母细胞瘤生物信息学分析
| 作者: |
1瞿根义,
1王佳威,
1徐勇,
1阳光,
1聂海波,
1黄文琳,
1汤乘
1 中南大学湘雅医学院附属株洲医院泌尿外科，湖南 株洲 412007 |
| 通讯: |
徐勇
Email: tigerhnll@126.com |
| DOI: | 10.3978/j.issn.2095-6959.2021.01.003 |
| 基金: | 湖南省株洲市科技计划项目(2019-001)。 |
摘要
目的：整合并利用生物信息学分析肾母细胞瘤基因表达谱芯片，挖掘肾母细胞瘤发生的关键基因。方法：利用GEO2R在线分析工具对GEO数据库肾母细胞瘤基因芯片数据GSE2712进行差异表达基因筛选，使用R软件对差异表达基因进行火山图绘制，进一步结合DAVID和STRING在线生物信息学工具对差异表达基因进行调控网络分析并构建蛋白质-蛋白质相互作用(protein-protein interaction，PPI)网络，使用Cytoscape软件进行Hub基因筛选。结果：共筛选出肾母细胞瘤差异表达基因955个，其中表达上调基因157个，表达下调基因798个。对差异表达基因进行GO富集分析和KEGG通路富集分析，利用在线生物信息学工具构建PPI网络，使用Cytoscape软件获取PPI网络中前10位Hub基因，分别是FN1，ALB，VEGFA，KDR，IGF1，PECAM1，FLT1，TEK，FGF1和ANGPT1。结论：应用生物信息学能有效分析基因芯片数据，获取肾母细胞瘤发生的关键基因，为肾母细胞瘤的治疗提供新的思路。
关键词：
肾母细胞瘤；生物信息学；差异表达基因；关键基因
Bioinformatics analysis of gene chip related to Wilms tumor
CorrespondingAuthor: XU Yong Email: tigerhnll@126.com
DOI: 10.3978/j.issn.2095-6959.2021.01.003
Foundation: This work was supported by the Science and Technology Guiding Program of Zhuzhou City, Hunan Province, China (2019-001).
Abstract
Objective: To integrate and use bioinformatics to analyze the gene expression profiling chip of nephroblastoma, and to dig out the key genes of nephroblastoma. Methods: The GEO2R online analysis tool was used to screen the differentially expressed genes in the GEO database for renal cell tumor gene chip data GSE2712. The R software was used to map the differentially expressed genes. The volcano map was used to further analyze the regulatory networks and construct the protein-protein interation (PPI) networks in combination with DAVID and STRING online bioinformatics tools. The Cytoscape software was used to screen the HUB genes. Results: A total of 955 differentially expressed genes were screened out, including 157 up-regulated genes and 798 down-regulated genes. GO enrichment analysis and KEGG pathway enrichment analysis of differentially expressed genes were performed using online bioinformatics tools to construct a PPI network. Cytoscape software was used to obtain the top 10 Hub genes in the PPI network, including FN1, ALB, VEGFA, KDR, IGF1, PECAM1, FLT1, TEK, FGF1 and ANGPT1. Conclusion: The application of bioinformatics can effectively analyze the gene chip data, obtain the key genes of nephroblastoma, and provide new ideas for the treatment of nephroblastoma.
Keywords：
nephroblastoma; bioinformatics; differentially expressed genes; key genes