通过综合生物信息学分析鉴定与乳腺癌发病机制和预后相关的候选生物标志物。

Identification of candidate biomarkers correlated with the pathogenesis and prognosis of breast cancer via integrated bioinformatics analysis.

作者信息

Liu Shuyu, Liu Xinkui, Wu Jiarui, Zhou Wei, Ni Mengwei, Meng Ziqi, Jia Shanshan, Zhang Jingyuan, Guo Siyu, Lu Shan, Li Yingfei

机构信息

Department of Clinical Chinese Pharmacy, School of Chinese Materia Medica, Beijing University of Chinese Medicine, Chaoyang District.

Center for Drug Metabolism and Pharmacokinetics Research Research of Herbal Medicines, Institute of Chinese Materia Medica, China Academy of Chinese Medical Sciences, Dongzhimen, Dongcheng District, Beijing, China.

出版信息

Medicine (Baltimore). 2020 Dec 4;99(49):e23153. doi: 10.1097/MD.0000000000023153.

BACKGROUND

This study was carried out to identify potential key genes associated with the pathogenesis and prognosis of breast cancer (BC).

METHODS

Seven GEO datasets (GSE24124, GSE32641, GSE36295, GSE42568, GSE53752, GSE70947, GSE109169) were downloaded from the Gene Expression Omnibus (GEO) database. Differentially expressed genes (DEGs) between BC and normal breast tissue samples were screened by an integrated analysis of multiple gene expression profile datasets. Hub genes related to the pathogenesis and prognosis of BC were verified by employing protein-protein interaction (PPI) network.

RESULTS

Ten hub genes with high degree were identified, including CDK1, CDC20, CCNA2, CCNB1, CCNB2, BUB1, BUB1B, CDCA8, KIF11, and TOP2A. Lastly, the Kaplan-Meier plotter (KM plotter) online database demonstrated that higher expression levels of these genes were related to lower overall survival. Experimental validation showed that all 10 hub genes had the same expression trend as predicted.

CONCLUSION

The findings of this research would provide some directive significance for further investigating the diagnostic and prognostic biomarkers to facilitate the molecular targeting therapy of BC, which could be used as a new biomarker for diagnosis and to guide the combination medicine of BC.

背景

本研究旨在鉴定与乳腺癌（BC）发病机制和预后相关的潜在关键基因。

方法

从基因表达综合数据库（GEO）下载了7个GEO数据集（GSE24124、GSE32641、GSE36295、GSE42568、GSE53752、GSE70947、GSE109169）。通过对多个基因表达谱数据集的综合分析，筛选出BC与正常乳腺组织样本之间的差异表达基因（DEGs）。利用蛋白质-蛋白质相互作用（PPI）网络验证与BC发病机制和预后相关的枢纽基因。

结果

鉴定出10个高连接度的枢纽基因，包括细胞周期蛋白依赖性激酶1（CDK1）、细胞分裂周期蛋白20（CDC20）、细胞周期蛋白A2（CCNA2）、细胞周期蛋白B1（CCNB1）、细胞周期蛋白B2（CCNB2）、纺锤体组装检查点蛋白1（BUB1）、BUB1有丝分裂后期阻滞蛋白（BUB1B）、细胞分裂周期相关蛋白8（CDCA8）、驱动蛋白家族成员11（KIF11）和拓扑异构酶Ⅱα（TOP2A）。最后，在线数据库Kaplan-Meier Plotter（KM Plotter）表明这些基因的较高表达水平与较低的总生存率相关。实验验证表明，所有10个枢纽基因的表达趋势与预测一致。