Yang Kaidi, Gao Jian, Luo Mao
Key Laboratory of Medical Electrophysiology of Ministry of Education, Collaborative Innovation Center for Prevention and Treatment of Cardiovascular Disease of Sichuan Province, Southwest Medical University, Luzhou, China,
Key Laboratory of Tumor Immunology, The First Affiliated Hospital, Army Medical University, Chongqing, China.
Onco Targets Ther. 2019 Feb 18;12:1319-1331. doi: 10.2147/OTT.S158619. eCollection 2019.
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.
The aim of this study was to identify potential gene signatures of the development and progression of the BLBC via bioinformatics analysis.
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 , , , , , , , , and were validated using Oncomine and Kaplan-Meier plotter.
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.
基底样乳腺癌（BLBC）是乳腺癌（BC）中最具侵袭性的亚型，与不良预后相关。由于BLBC的分子机制尚未完全阐明，鉴定该疾病的关键途径和核心基因是深入了解BLBC发生发展机制的重要途径。
本研究旨在通过生物信息学分析鉴定BLBC发生发展的潜在基因特征。
在GSE25066和GSE21422芯片之间鉴定出差异表达基因（DEGs），包括40个上调和21个下调的DEGs，这些DEGs在与BLBC进展中致癌或抑癌作用相关的术语中显著富集。此外，对来自GSE25066芯片的基底型和非基底型乳腺癌之间的DEGs进行了KEGG通路和基因集富集分析（GSEA）。这些DEGs在细胞周期、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信号通路等途径中富集。此外，使用Cytoscape软件构建了这61个DEGs的蛋白质-蛋白质相互作用（PPI）网络，并使用MCODE确定了假定模块的生物学意义。发现模块1与有丝分裂调控术语密切相关，并在细胞周期途径中富集，从而证实了BLBC有丝分裂指数高的病理特征。此外，使用Oncomine和Kaplan-Meier绘图仪验证了前10个核心基因如 、 、 、 、 、 、 、 、 和 的预测值。
我们的结果表明，PPI网络中的核心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了道路。
