Identification of key pathways and hub genes in basal-like breast cancer using bioinformatics analysis
Identification of key pathways and hub genes in basal-like breast cancer using bioinformatics analysis
复制标题
利用生物信息学分析鉴定基底样乳腺癌的关键通路和枢纽基因
DOI：
10.2147/ott.s158619
复制
发表时间：
2019-01-01
影响因子：
4
通讯作者：
Luo, Mao
中科院分区：
文献类型：
--
作者：
Yang, Kaidi;Gao, Jian;Luo, Mao
基底样乳腺癌（BLBC）是最具侵袭性的乳腺癌（BC）亚型，与预后不良有关。由于BLBC的分子机制尚未完全被发现，因此确定该疾病的关键通路和枢纽基因是为探索BLBC的发生和发展机制提供新见解的重要途径。目的通过生物信息学分析，寻找BLBC发生发展的潜在基因特征。方法和结果在GSE25066和GSE21422微阵列中鉴定出40个上调和21个下调的差异表达基因（deg），这些差异表达基因在BLBC的进展中具有显著的致癌或抑制作用。此外，通过基因集富集分析（Gene Set Enrichment Analysis, GSE25066）对基础型和非基础型乳腺癌之间的DEGs进行KEGG通路和GSEA富集分析。这些deg富集于细胞周期、细胞因子-细胞因子受体相互作用、趋化因子信号通路、中心碳代谢信号通路和TNF信号通路等通路。利用Cytoscape软件构建了蛋白-蛋白相互作用（PPI）网络，并利用MCODE对推测的模块进行了生物学意义验证。发现模块1与有丝分裂调控项密切相关，在细胞周期通路中富集，从而证实了BLBC高有丝分裂指数的病理特征。利用Oncomine和Kaplan-Meier绘图仪对CCNB2、BUB1、NDC80、CENPE、KIF2C、TOP2A、MELK、TPX2、CKS2和KIF20A等前10个枢纽基因的预测值进行验证。我们的研究结果表明，PPI网络中的枢纽基因和模块可能有助于深入了解BLBC的分子机制，为更准确地发现BLBC患者的潜在治疗靶点铺平道路。
Background Basal-like breast cancer (BLBC) is the most aggressive subtype of breast cancer (BC) and links to poor outcomes. As the molecular mechanism of BLBC has not yet been completely discovered, identification of key pathways and hub genes of this disease is an important way for providing new insights into exploring the mechanisms of BLBC initiation and progression. Objective The aim of this study was to identify potential gene signatures of the development and progression of the BLBC via bioinformatics analysis. Methods and results The differential expressed genes (DEGs) including 40 up-regulated and 21 down-regulated DEGs were identified between GSE25066 and GSE21422 microarrays, and these DEGs were significantly enriched in the terms related to oncogenic or suppressive roles in BLBC progression. In addition, KEGG pathway and GSEA (Gene Set Enrichment Analysis) enrichment analyses were performed for DEGs between the basal type and non-basal-type breast cancer from GSE25066 microarray. These DEGs were enriched in pathways such as cell cycle, cytokine-cytokine receptor interaction, chemokine signaling pathway, central carbon metabolism signaling and TNF signaling pathway. Moreover, the protein-protein interaction (PPI) network was constructed with those 61 DEGs using the Cytoscape software, and the biological significance of putative modules was established using MCODE. The module 1 was found to be closely related with a term of mitosis regulation and enriched in cell cycle pathway, and thus confirmed the pathological characteristic of BLBC with a high mitotic index. Furthermore, prediction values of the top 10 hub genes such as CCNB2, BUB1, NDC80, CENPE, KIF2C, TOP2A, MELK, TPX2, CKS2 and KIF20A were validated using Oncomine and Kaplan-Meier plotter. Conclusion Our results suggest the intriguing possibility that the hub genes and modules in the PPI network contributed to in-depth knowledge about the molecular mechanism of BLBC, paving a way for more accurate discovery of potential treatment targets for BLBC patients.