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运用整合生物信息学分析鉴定雌激素受体阳性/人表皮生长因子受体2阴性乳腺癌与雌激素受体阴性/人表皮生长因子受体2阴性乳腺癌之间的关键差异表达基因。

Identification of key differentially expressed genes between ER-positive/HER2-negative breast cancer and ER-negative/HER2-negative breast cancer using integrated bioinformatics analysis.

作者信息

Gan Siyuan, Dai Haixia, Li Rujia, Liu Wang, Ye Ruifang, Ha Yanping, Di Xiaoqing, Hu Wenhua, Zhang Zhi, Sun Yanqin

机构信息

Department of Pathology, Guangdong Medical University, Zhanjiang 524023, China.

Department of Ultrasound, The Affiliated Hospital of Guangdong Medical University, Zhanjiang 524023, China.

出版信息

Gland Surg. 2020 Jun;9(3):661-675. doi: 10.21037/gs.2020.03.40.

Abstract

BACKGROUND

Treatment strategies for various subtypes of breast cancer (BC) are different based on their distinct molecular characteristics. Therefore, it is very important to identify key differentially expressed genes (DEGs) between ER-positive/HER2-negative BC and ER-negative/HER2-negative BC.

METHODS

Gene expression profiles of GSE22093 and GSE23988 were obtained from the Gene Expression Omnibus database. There were 74 ER-positive/HER2-negative BC tissues and 85 ER-negative/HER2-negative BC tissues in the two profile datasets. DEGs between ER-positive/HER2-negative tissues and ER-negative/HER2-negative BC tissues were identified by the GEO2R tool. The common DEGs among the two datasets were detected with Venn software online. Next, we made use of the Database for Annotation, Visualization and Integrated Discovery to analyze enriched Kyoto Encyclopedia of Gene and Genome (KEGG) pathways and gene ontology terms. Then, the protein-protein interactions (PPIs) of these DEGs were visualized by Cytoscape with the Search Tool for the Retrieval of Interacting Genes. Of the proteins in the PPI network, Molecular Complex Detection plug-in analysis identified nine core upregulated genes and one core downregulated gene. UALCAN and Gene Expression Profiling Interactive Analysis were applied to determine the expression of these 10 genes in BC. Furthermore, for the analysis of overall survival among those genes, the Kaplan-Meier method was implemented.

RESULTS

Ninety-three common DEGs (63 upregulated and 30 downregulated) were identified. KEGG pathway enrichment analysis showed that upregulated DEGs were particularly enriched in the progesterone-mediated oocyte maturation pathway. In addition, PGR might be a prognostic biomarker for ER-positive/HER2-negative BC. CCND1 is a poor prognostic biomarker for ER-positive/HER2-negative BC and ER-negative/HER2-negative BC. Moreover, TFF1, AGR2 and EGFR might be predictive biomarkers of node metastasis in ER-positive/HER2-negative BC and ER-negative/HER2-negative BC.

CONCLUSIONS

CCND1, AGR2, PGR, TFF1 and EGFR are the key DEGs between ER-positive/HER2-negative BC and ER-negative/HER2-negative BC. Further studies are required to confirm the functions of the identified genes.

摘要

背景

基于不同的分子特征,乳腺癌(BC)各亚型的治疗策略有所不同。因此,识别雌激素受体(ER)阳性/人表皮生长因子受体2(HER2)阴性乳腺癌与ER阴性/HER2阴性乳腺癌之间的关键差异表达基因(DEGs)非常重要。

方法

从基因表达综合数据库获取GSE22093和GSE23988的基因表达谱。这两个谱数据集包含74个ER阳性/HER2阴性乳腺癌组织和85个ER阴性/HER2阴性乳腺癌组织。通过GEO2R工具识别ER阳性/HER2阴性组织与ER阴性/HER2阴性乳腺癌组织之间的DEGs。使用在线Venn软件检测两个数据集之间的共同DEGs。接下来,我们利用注释、可视化和综合发现数据库分析富集的京都基因与基因组百科全书(KEGG)通路和基因本体学术语。然后,通过Cytoscape与检索相互作用基因的搜索工具对这些DEGs的蛋白质-蛋白质相互作用(PPIs)进行可视化。在PPI网络中的蛋白质中,分子复合物检测插件分析确定了9个核心上调基因和1个核心下调基因。应用UALCAN和基因表达谱交互式分析来确定这10个基因在乳腺癌中的表达。此外,为了分析这些基因的总生存期,采用了Kaplan-Meier方法。

结果

鉴定出93个共同的DEGs(63个上调和30个下调)。KEGG通路富集分析表明,上调的DEGs特别富集于孕酮介导的卵母细胞成熟通路。此外,孕激素受体(PGR)可能是ER阳性/HER2阴性乳腺癌的预后生物标志物。细胞周期蛋白D1(CCND1)是ER阳性/HER2阴性乳腺癌和ER阴性/HER2阴性乳腺癌的不良预后生物标志物。此外,三叶因子1(TFF1)、富含半胱氨酸的酸性分泌蛋白2(AGR2)和表皮生长因子受体(EGFR)可能是ER阳性/HER2阴性乳腺癌和ER阴性/HER2阴性乳腺癌中淋巴结转移的预测生物标志物。

结论

CCND1、AGR2、PGR、TFF1和EGFR是ER阳性/HER2阴性乳腺癌与ER阴性/HER2阴性乳腺癌之间的关键DEGs。需要进一步研究来证实所鉴定基因的功能。

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