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Gliomas are the most common and aggressive primary tumors in the central nervous system. Recently, Max interactor-1 (MXI1), an antagonist of c-Myc that is involved in brain tumor progression, has been reported to be deregulated in a variety of tumors including glioma. However, the mechanism of MXI1 deregulation in gliomas remains unclear. In this study, we show that the relative expression level of MXI1 is markedly down-regulated in glioma cell lines. Using integrated bioinformatic analysis and experimental confirmation, we identified several miRNAs by screening a panel of predicted miRNAs that may regulate the MXI1 3'UTR. The strongest inhibitory miRNA, miR-155, can attenuate the activity of a luciferase reporter gene that is fused with the MXI1 3'UTR and decrease the expression levels of MXI1 mRNA and protein in U87 glioma cells. The potential role of miR-155 in promoting glioma cell proliferation by targeting MXI1 was confirmed in various glioma cell lines by rescue experiments using MTT assays, EdU incorporation assay, and cell counting experiments. In addition, we determined that the level of MXI1 mRNA was inversely correlated with the expression of miR-155 in 18 sets of glioblastoma multiforme specimens. These findings reveal for the first time that the targeting of MXI1 by miR-155 may result in a reduction in MXI1 expression and promote glioma cell proliferation; this result suggests a novel function of miR-155 in targeting MXI1 in glioma-genesis.

Animal microRNA (miRNA) target prediction is still a challenge, although many prediction programs have been exploited. MiRNAs exert their function through partially binding the messenger RNAs (mRNAs; likely at 3' untranslated regions [3'UTRs]), which makes it possible to detect the miRNA-mRNA interactions in vitro by co-transfection of miRNA and a luciferase reporter gene containing the target mRNA fragment into mammalian cells under a dual-luciferase assay system. Here, we constructed a human miRNA expression library and used a dual-luciferase assay system to perform large-scale screens of interactions between miRNAs and the 3'UTRs of seven genes, which included more than 3,000 interactions with triplicate experiments for each interaction. The screening results showed that the 3'UTR of one gene can be targeted by multiple miRNAs. Among the prediction algorithms, a Bayesian phylogenetic miRNA target identification algorithm and a support vector machine (SVM) presented a relatively better performance (27% for EIMMo and 24.7% for miRDB) against the average precision (17.3%) of the nine prediction programs used here. Additionally, we noticed that a relatively high conservation level was shown at the miRNA 3' end targeted regions, as well as the 5' end (seed region) binding sites., published_or_final_version