基于miRNA-mRNA联合分析的卵巢癌调控网络构建  

Construction of Ovarian Cancer Regulatory Network Based on miRNA-mRNA Combined Analysis

作　　者：孔薇[1] 王慧敏 王帅群 Kong Wei;Wang Huimin;Wang Shuaiqun(College of Information Engineering,Shanghai Maritime University,Shanghai,201306)

机构地区：[1]上海海事大学信息工程学院

出　　处：《基因组学与应用生物学》2018年第9期4227-4233,共7页Genomics and Applied Biology

基　　金：国家自然科学基金资助项目(NO.61271446);上海市科委自然科学基金项目(18ZR1417200)共同资助

摘　　要：研究表明微小RNA（microRNA,miRNA）通过影响转录后基因表达来调节机体功能,并与肿瘤的发生有密切关系。然而目前癌症致病过程的转录调控网络重构大多致力于转录层面的基因表达数据的处理和分析,如何整合转录及转录后不同类型的生物数据以挖掘它们的共调控机制是目前的研究热点之一。基于此,本研究利用联合非负矩阵分解算法融合卵巢癌miRNA数据和基因表达数据形成共模块,其次对特征模块中miRNA的靶基因进行预测分析,最后对mi RNA-mRNA共模块进行转录及转录后共调控网络构建。仿真结果及分子生物学分析表明,通过联合矩阵分析方法所提取的共模块显示出了与卵巢癌致病具有显著的生物相关性和潜在的联系,此外,GO生物过程分析也进一步的揭示了所提取的共模块中miRNA靶基因的生物学功能与卵巢癌致病密切相关。Researches have shown that microRNA（miRNA） regulate the function of the body by affecting posttranscriptional gene expression and are closely related to tumorigenesis. However, the reconstruction of cancer pathogenic transcriptional regulatory networks is mostly focused on the processing and analysis of single gene expression data. How to integrate different types of gene expression data with miRNA data to mine the common regulatory mechanisms has become a challenging problem. Furthermore, we used the Joint Non-negative Matrix Factorization Algorithm to merge miRNA data and gene expression data of ovarian cancer to form the common module, and then predicted and analyze the miRNA target genes in the characteristic module. Finally, constructing the regulatory network of the miRNA-mRNA common module. The results of the simulation and molecular biological analysis showed that the common module extracted by the Joint Matrix Analysis showed a significant biological relevance and potential connection with ovarian cancer. In addition, GO biological process analysis further revealed that the biological function of miRNA target genes in common modules is closely related to the pathogenesis of ovarian cancer.

关 键 词：卵巢癌 基因组数据 非负矩阵分解 生物过程分析 

分 类 号：R737.31[医药卫生—肿瘤]