Correlating Bladder Cancer Risk Genes with Their Targeting MicroRNAs Using MMiRNA-Tar

作     者：Yang Liu Steve Baker Hui Jiang Gary Stuart Yongsheng Bai 

作者机构：Department of Electrical and Computer EngineeringRose-Hulman Institute of Technology Department of Math and Computer ScienceIndiana State University Department of BiostatisticsUniversity of Michigan Department of BiologyIndiana State University The Center for Genomic AdvocacyIndiana State University 

出 版 物：《Genomics, Proteomics & Bioinformatics》 (基因组蛋白质组与生物信息学报（英文版）)

年 卷 期：2015年第13卷第3期

页      面：177-182页

核心收录：

学科分类：0710[理学-生物学] 1002[医学-临床医学] 1001[医学-基础医学(可授医学、理学学位)] 100214[医学-肿瘤学] 0714[理学-统计学（可授理学、经济学学位）] 0703[理学-化学] 0701[理学-数学] 10[医学] 0812[工学-计算机科学与技术（可授工学、理学学位）] 

基　　金：supported by the startup funds of Indiana State University USA to YB 

主　　题：The Cancer Genome Atlas Bladder cancer MicroRNA mRNA Correlation Target prediction 

摘      要：The Cancer Genome Arias （TCGA） （http：//cancergenome.***.gov） is a valuable data resource focused on an increasing number of well-characterized cancer genomes. In part, TCGA provides detailed information about cancer-dependent gene expression changes, including changes in the expression of transcription-regulating microRNAs. We developed a web interface tool MMiRNA-Tar （http：f/bioinfl.***.edufMMiRNA-Tar） that can calculate and plot the correlation of expression for mRNA-microRNA pairs across samples or over a time course for a list of pairs under different prediction confidence cutoff criteria. Prediction confidence was estab- lished by requiring that the proposed mRNA-microRNA pair appears in at least one of three target prediction databases： TargetProfiler, TargetScan, or miRanda. We have tested our MMiRNA-Tar tool through analyzing 53 tumor and 11 normal samples of bladder urothelial carcinoma （BLCA） datasets obtained from TCGA and identified 204 microRNAs. These microRNAs were correlated with the mRNAs of five previously-reported bladder cancer risk genes and these selected pairs exhib- ited correlations in opposite direction between the tumor and normal samples based on the cus- tomized cutoff criterion of prediction. Furthermore, we have identified additional 496 genes （830 pairs） potentially targeted by 79 significant microRNAs out of 204 using three cutoff criteria, i.e.,false discovery rate （FDR） 〈 0.1, opposite correlation coefficient between the tumor and normal samples, and predicted by at least one of three target prediction databases. Therefore, MMiRNA- Tar provides researchers a convenient tool to visualize the co-relationship between microRNAs and mRNAs and to predict their targeting relationship. We believe that correlating expression profiles for microRNAs and mRNAs offers a complementary approach for elucidating their interactions.