上海交通大学学报(医学版) ›› 2020, Vol. 40 ›› Issue (3): 294-.doi: 10.3969/j.issn.1674-8115.2020.03.004

• 论著·基础研究 • 上一篇    下一篇

基于生存分析的生物信息学方法筛选乳腺癌枢纽基因和关键通路

陈 思*,刘春良*,赵 倩,孙海鹏,刘云霞   

  1. 上海交通大学基础医学院病理生理学系,细胞分化与凋亡教育部重点实验室,上海200025
  • 出版日期:2020-03-28 发布日期:2020-04-09
  • 通讯作者: 刘云霞,电子信箱:jxsdsklyx@126.com。
  • 作者简介:陈 思(1994—),女,硕士生;电子信箱:leoway122@163.com。刘春良(1993—),男,硕士生;电子信箱:liuchunliang@sjtu.edu.cn。*为共同第一作者。
  • 基金资助:
    国家自然科学基金(81570717)。

Identification of hub genes and key pathways in breast cancersurvival-based bioinformatics analysis

CHEN Si, LIU Chun-liang, ZHAO Qian, SUN Hai-peng, LIU Yun-xia   

  1. Key Laboratory of Cell Differentiation and Apoptosis of National Ministry of Education, Department of Pathophysiology, Shanghai Jiao Tong University College of Basic Medical Sciences, Shanghai 200025, China
  • Online:2020-03-28 Published:2020-04-09
  • Supported by:
    National Natural Science Foundation of China (81570717).

摘要: 目的·利用生物信息学分析,将基因表达数据与临床生存分析相结合,以筛选乳腺癌的枢纽基因和关键通路。方法·从GEO数据库(Gene Expression Omnibus)下载了3个乳腺癌基因表达数据集,筛选出乳腺癌中的差异表达基因。Kaplan-Meier plotter数据库进一步筛选出与总体生存期相关的差异表达基因,并对这些基因进行GO(Gene Ontology)功能分析和KEGG(Kyoto Encyclopedia of Genes and Genomes)通路分析。通过构建蛋白 - 蛋白相互作用网络筛选乳腺癌枢纽基因。利用Oncomine数据库和人类蛋白质图谱数据库来验证枢纽基因的表达。实时荧光定量PCR检测人乳腺癌细胞MDA-MB-231和人正常乳腺上皮细胞MCF-10A中枢纽基因的表达情况。结果·筛选到262个差异表达基因与乳腺癌患者的总体生存期显著相关。GO功能分析和KEGG通路分析结果显示,这些基因与细胞核分裂、细胞分裂和染色体分离相关,并且主要富集在细胞周期、FoxO信号通路和卵母细胞减数分裂等通路上。蛋白质相互作用网络构建确定了10个枢纽基因。经数据库验证,它们在乳腺癌中均高表达;实时荧光定量PCR结果显示,10个枢纽基因中有8个在乳腺癌细胞中高表达。结论·通过基于生存分析的生物信息学方法筛选出了参与乳腺癌发生发展的关键基因和通路,这些基因和通路主要与细胞周期调控和细胞分裂相关。

关键词: 乳腺癌, 生物信息学, 枢纽基因, 生存分析, 生物学标志物

Abstract: Objective · To identify hub genes and key pathways in breast cancerbioinformatics analysis that integrated gene data with clinical survival analysis. Methods · Three gene profilings downloaded Gene Expression Omnibus (GEO) were used to identify differentially expressed genes (DEGs) in breast cancer. Kaplan-Meier plotter was used to identify the DEGs that were significantly associated with overall survival in breast cancer. Gene Ontology (GO) function analysis and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analysis were performed. Next, hub genes were identified the protein-protein interaction (PPI) network. Oncomine and the Human Protein Atlas (HPA) database were used to validate the of the hub genes. The s of hub genes in MDA-MB-231 cells and MCF-10A cells were detectedquantitative real-time PCR (qPCR). Results · Among the DEGs, 262 genes were significantly correlated with overall survival of breast cancer patients. The results of GO functional analysis and KEGG pathway analysis showed that these genes were associated with nuclear division, cell division and chromosome segregation, and were mainly enriched on the pathways such as cell cycle, FoxO signaling pathway and oocyte meiosis. PPI network construction identified ten hub genes. They were all highly expressed in breast cancer, which were validatedthe databases. The results of qPCR showed that 8 out of 10 hub genes were highly expressed in breast cancer cells. Conclusion · The hub genes and key pathways involved in the development of breast cancer are identifiedsurvival-based bioinformatics analysis, which are mainly associated with cell cycle regulation and cell division.

Key words: breast cancer, bioinformatics, hub gene, survival analysis, biomarker