文章：

基于加权基因共表达网络分析的乳腺癌发病机制鉴定

Identification of breast cancer mechanism based on weighted gene coexpression network analysis 

原文发布日期：2017-08-11 

英文摘要：

Our gene expression-profiling analysis aimed to explain the mechanism of breast cancer development by identifying key pathways and constructing networks of related transcription factors (TFs) and microRNAs (miRNAs) in breast cancer tissues. Gene expression profiles of normal and breast cancer tissues were downloaded to identify differentially expressed genes (DEGs). Coexpression modules were explored using weighted gene coexpression network analysis (WGCNA). Gene Ontology and Kyoto Encyclopedia of Genes and Genomes analyses were performed to discover the enriched functionally associated gene groups and define pathways in breast cancer, respectively. miRNAs-DEGs and TF-DEG regulatory networks were constructed using Cytoscape. CDK6(cyclin-dependent kinase), miR-124, EGF(epidermal growth factor) and NF-κB(nuclear factor of kappa light polypeptide gene enhancer in B-cells 1) expression was also analyzed using real-time quantitative PCR. Totally, 7713 DEGs were identified for WGCNA. The results revealed that 1388 upregulated DEGs were associated with protein transport, protein localization and organic substance transport, whereas 1819 downregulated DEGs were associated with cancer and Wnt signaling pathways. Five miRNAs (miR-760, miR-1276, miR-124, miR-124-3p and miR-506-3p) with a degree of ⩾15 and one important TF (NF-κB) were identified in miRNA and TF regulatory networks. CDK6 mRNA and miR-124 expression was significantly reduced and EGF mRNA expression was clearly enhanced in cancer tissues compared with those in normal breast tissues. The CDK6 gene could be regulated by miR-124, which is involved in Wnt signaling and cancer pathways. NF-κB might initiate the breast cancer pathway by targeting EGF in human breast cancer tissues. This putative information on regulatory networks in breast cancer will be beneficial for future researches on mechanisms underlying its development. 

摘要翻译：

我们的基因表达谱分析旨在通过识别乳腺癌组织中的关键通路，并构建相关转录因子（TFs）和微小RNA（miRNAs）调控网络，以阐释乳腺癌发展的机制。研究下载了正常与乳腺癌组织的基因表达谱数据以筛选差异表达基因（DEGs），采用加权基因共表达网络分析（WGCNA）探索共表达模块，通过基因本体论（GO）和京都基因与基因组百科全书（KEGG）分析分别识别功能相关的富集基因群和界定乳腺癌相关通路。利用Cytoscape构建了miRNA-DEG和TF-DEG调控网络，同时采用实时定量PCR技术分析了CDK6（细胞周期蛋白依赖性激酶）、miR-124、EGF（表皮生长因子）及NF-κB（核因子κB）的表达水平。研究共筛选出7713个DEGs用于WGCNA分析。结果显示：1388个上调DEGs与蛋白质转运、蛋白质定位和有机物质运输相关，而1819个下调DEGs与癌症及Wnt信号通路相关。在miRNA和TF调控网络中，识别出5个连接度≥15的miRNAs（miR-760、miR-1276、miR-124、miR-124-3p和miR-506-3p）及一个重要转录因子（NF-κB）。与正常乳腺组织相比，癌组织中CDK6 mRNA和miR-124表达显著降低，而EGF mRNA表达明显升高。CDK6基因可能受miR-124调控，该基因参与Wnt信号通路和癌症通路；NF-κB可能通过靶向EGF激活乳腺癌通路。这些关于乳腺癌调控网络的推定信息将为未来研究其发展机制提供重要参考。

原文链接：

Identification of breast cancer mechanism based on weighted gene coexpression network analysis