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骨质疏松症关键基因的筛选及生物信息学分析
作者姓名:许航  崔宇韬  任广凯  刘贺  王雁冰  彭传刚  吴丹凯
作者单位:130000 长春,吉林大学第二医院骨科医学中心
基金项目:吉林省科技发展计划项目(20200404140YY)
摘    要:目的通过生物信息学的方式筛选骨质疏松症关键基因,并进一步分析其与骨质疏松症的联系及作用机制。 方法从公共基因表达数据库下载基因表达谱数据集,通过R软件筛选差异表达基因并进行功能与通路分析。使用在线工具String构建蛋白质互作网络,导入cytoscape软件筛选关键基因并构建集簇模块。 结果共筛选出1 334个差异表达基因,其中上调基因722个,下调基因612个。GO分析显示功能主要富集在细胞外基质结构成分,信号受体激活剂活性,跨膜转运蛋白结合及细胞因子结合等方面。KEGG通路富集中显示差异基因主要参与PI3K-Akt信号通路、MAPK信号通路、Rap1信号通路以及Ras信号通路等通路。根据蛋白质互作网络筛选出AKT1、EGF、VEGFA、PROM1、TP53、NES、CD21、SNAI1、FGF13、LIF共十个关键基因,以及一个集簇模块。 结论筛选并分析了关键基因与集簇模块的功能、作用及其与骨质疏松可能存在的联系,为揭示骨质疏松症潜在的的分子机制和药物靶点提供新的思路。

关 键 词:骨质疏松症  差异基因表达  关键基因筛选  生物信息学  
收稿时间:2022-06-27

Screening and Bioinformatics Analysis of Core Genes of Osteoporosis
Authors:Hang Xu  Yutao Cui  Guangkai Ren  He Liu  Yanbing Wang  Chuangang Peng  Dankai Wu
Institution:Orthopaedic Medical Center, The Second Hospital of Jilin University, ChangChun 130000, China
Abstract:ObjectiveIn this study, we screened the hub genes of osteoporosis by bioinformatics, and further analyzed their relationship with osteoporosis and their mechanism of action. MethodsFirstly, the gene expression profile data set was downloaded from the public gene expression database, and the differentially expressed genes were screened by R software, and their functions and pathways were analyzed. Then, the online tool String was used to construct the protein interaction network, and the software cytoscape was imported to screen the core genes and construct the clustering module. ResultsA total of 1334 differentially expressed genes were screened, including 722 up-regulated genes and 612down-regulated genes. GO analysis shows that the functions are mainly concentrated in extracellular matrix structural constituent, signal receptor activator activity, transmembrane transporter binding and cytokine binding. The enrichment of KEGG pathway shows that the differential genes are mainly involved in PI3K-Akt signaling pathway, MAPK signaling pathway, Rap1 signaling pathway and Ras signaling pathway. Ten key genes, namely AKT1, EGF, VEGFA, PROM1, TP53, NES, CD21, SNAL1, FGF13, LIF, and one clustering module were selected according to protein interaction network. ConclusionThe functions and roles of key genes and clustering modules and their possible relationship with osteoporosis were screened and analyzed, which provided new ideas for revealing the potential molecular mechanism and drug targets of osteoporosis.
Keywords:Osteoporosis  Differential gene expression  Core gene screening  Bioinformatics  
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