Dissecting the biological relationship between TCGA miRNA and mRNA sequencing data using MMiRNA-Viewer

文献类型： 外文期刊

第一作者： Bai, Yongsheng

作者： Bai, Yongsheng;Ding, Lizhong;Rath, Ethan;Stuart, Gary;Bai, Yongsheng;Stuart, Gary;Baker, Steve;Jiang, Feng;Bai, Jenny M.;Wu, Jianghong;Jiang, Hui

作者机构：

关键词： TCGA;miRNA;mRNA;Cancer;Correlation;Expression;Regulation;MMiRNA-Viewer;Visualization

期刊名称：BMC BIOINFORMATICS （ 影响因子：3.169； 五年影响因子：3.629 ）

ISSN： 1471-2105

年卷期： 2016 年 17 卷

页码：

收录情况： SCI

摘要： Background: MicroRNAs (miRNA) are short nucleotides that interact with their target genes through 3 ' untranslated regions (UTRs). The Cancer Genome Atlas (TCGA) harbors an increasing amount of cancer genome data for both tumor and normal samples. However, there are few visualization tools focusing on concurrently displaying important relationships and attributes between miRNAs and mRNAs of both cancer tumor and normal samples. Moreover, a deep investigation of miRNA-mRNA target and biological relationships across multiple cancer types by integrating web-based analysis has not been thoroughly conducted. Results: We developed an interactive visualization tool called MMiRNA-Viewer that can concurrently present the co-relationships of expression between miRNA-mRNA pairs of both tumor and normal samples into a single graph. The input file of MMiRNA-Viewer contains the expression information including fold changes between normal and tumor samples for mRNAs and miRNAs, the correlation between mRNA and miRNA, and the predicted target relationship by a number of databases. Users can also load their own input data into MMiRNA-Viewer and visualize and compare detailed information about cancer-related gene expression changes, and also changes in the expression of transcription-regulating miRNAs. To validate the MMiRNA-Viewer, eight types of TCGA cancer datasets with both normal and control samples were selected in this study and three filter steps were applied subsequently. We performed Gene Ontology (GO) analysis for genes available in final selected 238 pairs and also for genes in the top 5 % (95 percentile) for each of eight cancer types to report a significant number of genes involved in various biological functions and pathways. We also calculated various centrality measurement matrices for the largest connected component(s) in each of eight cancers and reported top genes and miRNAs with high centrality measurements. Conclusions: With its user-friendly interface, dynamic visualization and advanced queries, we also believe MMiRNA-Viewer offers an intuitive approach for visualizing and elucidating co-relationships between miRNAs and mRNAs of both tumor and normal samples. We suggest that miRNA and mRNA pairs with opposite fold changes of their expression and with inverted correlation values between tumor and normal samples might be most relevant for explaining the decoupling of mRNAs and their targeting miRNAs in tumor samples for certain cancer types.

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