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目的:利用生物信息的方法,建立一种对不同ER表达状态乳腺癌差异表达microRNAs生物学功能和信号通路系统分析的方法,探讨microRNAs在不同ER状态中可能发挥的调控作用。方法:通过文献挖掘,找出不同ER表达状态乳腺癌差异表达microRNAs,利用预测软件TargetScan、PicTar、miRanda和TarBase数据库得出microRNAs可能靶向的基因集,对靶基因集进行去除随机化的富集分析后,再利用DAVID数据库进行相关生物学功能和信号通路分析。结果:通过文献挖掘的方法找到了5个不同ER表达状态乳腺癌microRNAs差异表达数据集,分别包含了11、43、25、6及19个差异表达的microRNAs。经过靶基因预测及富集后,得到的不同microRNAs靶基因集分别包括1 553、1 750、1 905、1 250和1 826个靶基因。进一步功能及通路分析发现,这些靶基因集可能参与转录相关蛋白质定位及转移、RNA代谢、细胞周期、细胞凋亡和细胞分裂等多个生物学过程,并发现3条共同的BIOCARTA细胞信号通路可能与ER表达调节相关。结论:找到了不同ER表达状态乳腺癌差异表达的microRNAs,并利用生物信息学方法对这些mi-croRNAs进行系统分析,为进一步研究microRNAs在乳腺癌不同分子亚型分化中的调控机制奠定基础。
OBJECTIVE: To establish a systematic method for analyzing the biological functions and signaling pathways of differentially expressed microRNAs in breast cancer with different expression of ER, and to explore the possible regulatory role of microRNAs in different ER states. Methods: By means of literature mining, differentially expressed microRNAs in breast cancer with different expression of ER were identified. The possible target genes of microRNAs were obtained by using the software TargetScan, PicTar, miRanda and TarBase. The target gene sets were removed by randomized enrichment After analysis, we then use DAVID database to analyze related biological functions and signal pathways. Results: Five different ER expression breast cancer microRNAs differential expression datasets were found by literature mining methods, including 11, 43, 25, 6 and 19 differentially expressed microRNAs. After the target genes were predicted and enriched, the target gene sets of different microRNAs were obtained respectively including 1 553,1 750,1 905,1 250 and 1 826 target genes. Further functional and pathway analysis revealed that these target gene sets may be involved in a number of biological processes such as transcription-related protein localization and metastasis, RNA metabolism, cell cycle, apoptosis and cell division and found that three common BIOCARTA cell signaling pathways may Related to ER expression regulation. CONCLUSIONS: MicroRNAs differentially expressed in different ER-expressing breast cancers were found and bioinformatics methods were used to systematically analyze these mi-croRNAs, which laid the foundation for the further study on the regulatory mechanisms of microRNAs in different molecular subtypes of breast cancer.