Department of Experimental Management Center, Henan Institute of Science and Technology, Xinxiang, China.
Department of Pharmacy, Xinxiang Central Hospital, Xinxiang, China.
J Cell Biochem. 2019 Jun;120(6):9522-9531. doi: 10.1002/jcb.28228. Epub 2018 Dec 3.
Breast cancer with metastasis especially brain metastasis represents a significant cause of morbidity and mortality in patients. In this study, we aimed to investigate the hub genes and potential molecular mechanism in brain metastasis breast cancer. Expression profiles of the genes were extracted from the Gene Expression Omnibus (GEO) database. GO and KEGG pathway enrichment analyses were conducted at Database for Annotation, Visualization, and Integrated Discovery. Protein-protein interaction (PPI) network was established by STRING database constructed by Cytoscape software. Hub genes were identified by the molecular complex detection (MCODE) plugin and the CytoHubba plugin. The transcription factor (TF) that regulates the expression of hub genes was analyzed using the NetworkAnalyst algorithm. Kaplan-Meier curve was used to analyze the effects of hub genes on overall survival. Two GEO databases (GSE100534 and GSE52604) were downloaded from GEO databases. A total of 102 overlapped genes were identified, and the top five KEGG pathways enriched were pathways in cancer, HTLV-I infection, focal adhesion, ECM-receptor interaction, and protein digestion and absorption. By combing the results of MCODE and CytoHubba, a total of 10 hub genes were selected. Kaplan-Meier curve showed that ANLN, BUB1, TTK, and SKA3 were closely associated with the overall survival of breast cancer patients. TF analysis results showed that E2F4, KDM5B, and MYC were crucial regulators for these four hub genes. The current study based on the GEO database provided novel understanding regarding the mechanism of breast cancer metastasis to brain and may provide novel therapeutic targets.
乳腺癌伴转移，尤其是脑转移，是导致患者发病率和死亡率升高的重要原因。本研究旨在探讨脑转移乳腺癌中的关键基因和潜在分子机制。从基因表达综合数据库（GEO）中提取基因表达谱。采用数据库注释、可视化和综合发现（DAVID）数据库进行 GO 和 KEGG 通路富集分析。通过 Cytoscape 软件构建的 STRING 数据库构建蛋白质-蛋白质相互作用（PPI）网络。利用 MCODE 插件和 CytoHubba 插件确定关键基因。利用 NetworkAnalyst 算法分析调节关键基因表达的转录因子（TF）。采用 Kaplan-Meier 曲线分析关键基因对总生存期的影响。从 GEO 数据库中下载了两个 GEO 数据集（GSE100534 和 GSE52604）。共鉴定出 102 个重叠基因，前 5 个富集的 KEGG 通路为癌症通路、HTLV-I 感染、黏着斑、ECM-受体相互作用和蛋白消化吸收。结合 MCODE 和 CytoHubba 的结果，共选择了 10 个关键基因。Kaplan-Meier 曲线表明，ANLN、BUB1、TTK 和 SKA3 与乳腺癌患者的总生存期密切相关。TF 分析结果表明，E2F4、KDM5B 和 MYC 是这 4 个关键基因的关键调节因子。本研究基于 GEO 数据库，为乳腺癌脑转移的机制提供了新的认识，可能为新的治疗靶点提供依据。
