Two hub genes of bipolar disorder, a bioinformatics study based on the GEO database

IF 2 Q3 NEUROSCIENCES
Ping Sun , Shunkang Feng , Hui Yu , Xiaoxiao Wang , Yiru Fang
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Abstract

Bipolar disorder is a mood illness that affects many people. It has a high recurrence frequency and will cause significant damage to the patient's social function. At present, the pathogenesis of BD is not clear. The National Center for Biotechnology Information (NCBI) established and maintained the Gene Expression Omnibus (GEO) database, a gene expression database. For bioinformatics analysis, researchers can obtain expression data from the internet. At present, the samples of the dataset used in the research of BD are mostly from brain tissue, and the data containing blood samples are rarely used. GEO databases (GSE46416, GSE5388, and GSE5389) were used to retrieve public data, and utilizing the online tool GEO2R, differentially expressed genes (DEGs) were retrieved. The common DEGs between the samples of patients with BD and the samples of the normal population were screened by Venn diagrams. Gene ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) analyses were used to perform functional annotation and pathway enrichment analysis of DEGs. A protein-protein interaction network (PPI) was built to investigate hub genes on this basis. There were 117 up-regulated DEGs and 38 down-regulated DEGs discovered, with two hub genes [SRC, CDKN1A] among the up-regulated DEGs. These two hub genes were also highly enriched in the oxytocin signaling pathway, proteoglycans in cancer and bladder cancer, according to KEGG analysis. The results of the receiver operating characteristic curve (ROC) of SRC and CDKN1A in the three datasets strongly suggested that SRC and CDKN1A were potential diagnostic markers of BD. The results strongly suggest that SRC and CDKN1A are related to the pathogenesis of BD.

基于 GEO 数据库的生物信息学研究:双相情感障碍的两个中心基因
躁郁症是一种影响许多人的情绪病。它的复发率很高,会对患者的社会功能造成重大损害。目前,躁郁症的发病机制尚不明确。美国国家生物技术信息中心(NCBI)建立并维护了基因表达综合数据库(GEO)。为了进行生物信息学分析,研究人员可以从互联网上获取表达数据。目前,用于 BD 研究的数据集样本大多来自脑组织,含有血液样本的数据很少使用。研究人员利用 GEO 数据库(GSE46416、GSE5388 和 GSE5389)检索公共数据,并利用在线工具 GEO2R 检索差异表达基因(DEGs)。通过 Venn 图筛选出 BD 患者样本与正常人群样本之间的共同 DEGs。基因本体(GO)和京都基因组百科全书(KEGG)分析用于对 DEGs 进行功能注释和通路富集分析。在此基础上,建立了蛋白质-蛋白质相互作用网络(PPI)来研究枢纽基因。结果发现了117个上调的DEGs和38个下调的DEGs,其中两个枢纽基因[SRC, CDKN1A]属于上调的DEGs。根据 KEGG 分析,这两个枢纽基因在催产素信号通路、癌症中的蛋白多糖和膀胱癌中也高度富集。三个数据集中 SRC 和 CDKN1A 的接收操作特征曲线(ROC)结果强烈提示,SRC 和 CDKN1A 是潜在的 BD 诊断标志物。这些结果有力地表明,SRC和CDKN1A与BD的发病机制有关。
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来源期刊
IBRO Neuroscience Reports
IBRO Neuroscience Reports Neuroscience-Neuroscience (all)
CiteScore
2.80
自引率
0.00%
发文量
99
审稿时长
14 weeks
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