通过生物信息学分析鉴定雌激素受体阴性/人表皮生长因子受体2阴性乳腺癌中的关键候选基因、信号通路及相关预后价值。

Identification of key candidate genes, pathways and related prognostic values in ER-negative/HER2-negative breast cancer by bioinformatics analysis.

作者信息

Shao Nan, Yuan Kaitao, Zhang Yunjian, Yun Cheang Tuck, Li Jie, Lin Ying

机构信息

Breast Disease Center, The First Affiliated Hospital, Sun Yat-Sen University, Guangzhou, 510080, China.

出版信息

J BUON. 2018 Jul-Aug;23(4):891-901.

PMID:30358191

Abstract

PURPOSE

Breast cancer possesses different molecular expressions and biological behaviors. The purpose of this study was to identify the key genes, pathways, and related prognostic values in estrogen receptor (ER)-negative/human epidermal growth factor 2 (HER2)-negative breast cancer by bioinformatics analysis.

METHODS

The mRNA expression profiles of GSE20194 and GSE23988 were obtained from the Gene Expression Omnibus (GEO) database. Differently expressed genes (DEGs) were analyzed by GEO2R. A functional and pathway enrichment analysis of DEGs was conducted using DAVID. A protein-protein interaction (PPI) network was constructed using STRING and a module analysis of the PPI network was conducted using Cytoscape software. Survival analysis of hub genes was analyzed using the Kaplan-Meier plotter online tool.

RESULTS

108 ER-negative/HER2-negative and 172 ER-positive/HER2-negative breast cancer samples were collected from the datasets GSE20194 and GSE23988. A total of 355 DEGs were identified in the ER-negative/HER2-negative samples, including 140 up-regulated and 215 down-regulated genes. The PPI network of DEGs consisted of 265 nodes and 648 edges. A significant module (12 nodes and 56 edges) was acquired from the PPI network of DEGs. Geneontology (GO) and pathway enrichment analysis demonstrated that this module was mainly related with transcription, cell proliferation, binding, and pathways in the PI3K-Akt signaling pathway. The high expression of CCNE1, KRT16, and MYBL2 was associated with worse relapse-free survival (RFS) and overall survival (OS) in ER-negative/HER2-negative breast cancer.

CONCLUSIONS

An integrated bioinformatics analysis was utilized to discover key candidate genes and pathways in ER-negative/HER2-negative breast cancer. This can improve the understanding of molecular mechanisms and provide potential candidate genes for diagnosis, prognosis, and individualized therapy.

摘要

目的

乳腺癌具有不同的分子表达和生物学行为。本研究旨在通过生物信息学分析确定雌激素受体（ER）阴性/人表皮生长因子2（HER2）阴性乳腺癌中的关键基因、信号通路及相关预后价值。

方法

从基因表达综合数据库（GEO）获取GSE20194和GSE23988的mRNA表达谱。通过GEO2R分析差异表达基因（DEG）。使用DAVID对DEG进行功能和通路富集分析。利用STRING构建蛋白质-蛋白质相互作用（PPI）网络，并使用Cytoscape软件对PPI网络进行模块分析。使用在线工具Kaplan-Meier plotter对枢纽基因进行生存分析。

结果

从数据集GSE20194和GSE23988中收集了108例ER阴性/HER2阴性和172例ER阳性/HER2阴性乳腺癌样本。在ER阴性/HER2阴性样本中总共鉴定出355个DEG，包括140个上调基因和215个下调基因。DEG的PPI网络由265个节点和648条边组成。从DEG的PPI网络中获得一个显著模块（12个节点和56条边）。基因本体论（GO）和通路富集分析表明，该模块主要与转录、细胞增殖、结合以及PI3K-Akt信号通路中的通路相关。CCNEl、KRT16和MYBL2的高表达与ER阴性/HER2阴性乳腺癌患者较差的无复发生存期（RFS）和总生存期（OS）相关。