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The Cancer Genome Atlas (TCGA) (http://cancergenome.nih.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://bioinf1.indstate.edu/MMiRNA-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 established 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 exhibited correlations in opposite direction between the tumor and normal samples based on the customized 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.[1]
