【摘要】 目的 通过生物信息学方法对乳腺癌中的关键基因进行筛选，并分析其功能，为乳腺癌的发病机制、诊疗及预后提供新思路。方法 从基因表达数据库(GEO)中获取乳腺癌基因表达数据集，通过R软件筛选差异表达基因，利用DAVID数据库进行功能富集分析，通过STRING数据库对筛选出的关键基因进行蛋白相互作用分析，Cytoscape软件对得到的蛋白互作网络(PPI)进行可视化，CytoNCA插件用于计算根据betweenness(介性中间性，BC)排名前20的基因。使用GEPIA2分析基因在乳腺癌中的表达水平。使用Kaplan Meier plotter对筛选出的关键基因进行预后生存分析。结果 从基因表达数据库(GEO)中得到GSE42568数据集，使用R筛选获取差异表达基因，得到与乳腺癌存在相互关系作用的1770个基因，其中包括698个上调基因和1072个下调基因。通过DAVID在线数据库进行功能富集分析，富集分析结果显示关键基因主要参与信号转导，ERK1和ERK2级联的正向调控，和对细胞增殖的负调控作用；主要集中在细胞外外泌体、胞外区、质膜及细胞质；发挥蛋白结合，细胞外基质结构成分，钙离子结合等作用；主要富集于PI3K-Akt信号通路、癌症的途径、PPAR信号通路等。STRING数据库用于构建蛋白互作网络，根据betweenness(介性中间性，BC)排名前20的基因有EGFR、FN1、LPAR2、IGF1、FGF7、SPP1、COL1A1、CREB5、ERBB2、COL1A2等。GEPIA2分析并验证前十个基因在乳腺癌中的表达水平，分别为EGFR,FN1,LPAR2、IGF1、FGF7、SPP1、COL1A1、CREB5、ERBB2、COL1A2,其中COL1A2在乳腺癌中呈高表达，IGF1在乳腺癌中呈低表达。预后生存分析结果显示COL1A2和IGF1基因的表达水平与乳腺癌预后生存具有相关性。结论 本研究利用生物信息学方法筛选出乳腺癌中的差异基因，为寻找乳腺癌新的治疗靶点提供思路。更多还原

【Abstract】 Objective To screen key genes in breast cancer and analyze their functions by bioinformatics methods, so as to provide new ideas for the pathogenesis, diagnosis and treatment and prognosis of breast cancer.Methods The breast cancer gene expression data set was obtained from the gene expression database(GEO),the differentially expressed genes were screened by R software, the functional enrichment analysis was performed by the DAVID database, the protein interaction analysis of the key genes screened was performed through the STRING database, the results of protein-protein interaction network(PPI) was visualized by Cytoscape software, and the CytoNCA plug-in was used to calculate the top 20 genes according to betweenness(mesocentrality, BC).GEPIA2 was used to analyze gene expression levels in breast cancer.Kaplan Meier plotter was used to analyze the prognosis and survival of the key genes screened.Results A GSE42568 data set was obtained from the Gene Expression Database(GEO) and 1,770 genes were obtained by R screening, including 698 up-regulated genes and 1,072 down-regulated genes.The results of enrichment analysis showed that the key genes were mainly involved in signal transduction, positive regulation of ERK1 and ERK2 cascades, and negative regulation of cell proliferation; mainly concentrated in extracellular exosomes, extracellular regions, plasma membrane and cytoplasm; played role in protein binding, extracellular matrix structural components, calcium ion binding, etc; mainly enriched in PI3K-Akt signaling pathway, cancer pathway, PPAR signaling pathway, etc.The STRING database was used to construct protein-protein interaction networks, and the top 20 genes according to betweenness(mesocentrality, BC) were EGFR,FN1,LPAR2,IGF1,FGF7,SPP1,COL1A1,CREB5,ERBB2,COL1A2,etc.GEPIA2 was analyzed and verified to be the expression levels of the first ten genes in breast cancer, which were EGFR,FN1,LPAR2,IGF1,FGF7,SPP1,COL1A1,CREB5,ERBB2,and COL1A2,among which COL1A2 was highly expressed in breast cancer and IGF1 was low in breast cancer.The results of prognostic survival analysis showed that the expression levels of COL1A2 and IGF1 genes were correlated with the prognostic survival of breast cancer.Conclusions In this study,bioinformatics methods are used to screen out differential genes in breast cancer,which provides ideas for finding new therapeutic targets for breast cancer．更多还原