基于生物信息学分析鉴定乳腺癌的核心基因及潜在分子机制
Identification of core genes and potential molecular mechanisms in breast cancer using bioinformatics analysis.
机构信息
Research Center, The Fourth Hospital of Hebei Medical University, Shijiazhuang, Hebei 050017, PR China.
Breast Center, The Fourth Hospital of Hebei Medical University, Shijiazhuang, Hebei 050017, PR China.
出版信息
Pathol Res Pract. 2019 Jul;215(7):152436. doi: 10.1016/j.prp.2019.152436. Epub 2019 May 4.
BACKGROUND
Breast cancer is the most frequently diagnosed cancer in women worldwide. This study aimed to elucidate the potential key candidate genes and pathways in breast cancer.
METHODS
The gene expression profile dataset GSE65212 was downloaded from GEO database. Differentially expressed genes (DEGs) were obtained by the R Bioconductor packages. The Gene ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analysis of DEGs were performed using DAVID database. The protein-protein interaction (PPI) network was then established by STRING and visualized by Cytoscape software. Module analysis of the PPI network was performed by the plug-in Molecular Complex Detection (MCODE). Then, the identified genes were verified by Kaplan-Meier plotter online database and quantitative real-time PCR (qPCR) in breast cancer tissue samples.
RESULTS
A total of 857 differential expressed genes were identified, of which, the upregulated genes were mainly enriched in the cell cycle, while the downregulated genes were mainly enriched in PPAR signaling pathway. Moreover, six hub genes with high degree were identified, including TOP2A, PCNA, CCNB1, CDC20, BIRC5 and CCNA2. Lastly, the Kaplan-Meier plotter online database confirmed that higher expression levels of these hub genes were related to lower overall survival. Experimental validation showed that all six hub genes had the same expression trend as predicted.
CONCLUSION
These results identified key genes, which could be used as a new biomarker for breast cancer diagnosis and treatment.
背景
乳腺癌是全球女性最常见的癌症。本研究旨在阐明乳腺癌潜在的关键候选基因和途径。
方法
从 GEO 数据库下载基因表达谱数据集 GSE65212。使用 R Bioconductor 包获得差异表达基因（DEGs）。使用 DAVID 数据库对 DEGs 进行基因本体论（GO）和京都基因与基因组百科全书（KEGG）通路富集分析。然后通过 STRING 建立蛋白质-蛋白质相互作用（PPI）网络，并通过 Cytoscape 软件可视化。通过插件分子复合物检测（MCODE）对 PPI 网络进行模块分析。然后，通过 Kaplan-Meier plotter 在线数据库和乳腺癌组织样本的定量实时 PCR（qPCR）验证鉴定的基因。
结果
共鉴定出 857 个差异表达基因，其中上调基因主要富集在细胞周期，而下调基因主要富集在 PPAR 信号通路。此外，还鉴定出了 6 个具有高度数的枢纽基因，包括 TOP2A、PCNA、CCNB1、CDC20、BIRC5 和 CCNA2。最后，Kaplan-Meier plotter 在线数据库证实，这些枢纽基因的高表达水平与总生存率降低有关。实验验证表明，这 6 个枢纽基因的表达趋势与预测的相同。
结论
这些结果确定了关键基因，可作为乳腺癌诊断和治疗的新生物标志物。
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