MicroTar: predicting microRNA targets from RNA duplexes.
BMC Bioinformatics 01/2006; 7.
Article: MicroRNA miR-107 is overexpressed in pituitary adenomas and inhibits the expression of aryl hydrocarbon receptor-interacting protein in vitro.[show abstract] [hide abstract]
ABSTRACT: Abnormal microRNA (miRNA) expression profiles have recently been associated with sporadic pituitary adenomas, suggesting that miRNAs can contribute to tumor formation; miRNAs are small noncoding RNAs that inhibit posttranscriptional expression of target mRNAs by binding to target sequences usually located in the 3'-UTR. In this study, we investigated the role played by miR-107, a miRNA associated with different human cancers, in sporadic pituitary adenomas and its interaction with the pituitary tumor suppressor gene aryl hydrocarbon receptor-interacting protein (AIP). miR-107 expression was evaluated in pituitary adenoma and normal pituitary samples using microRNA screen TLDA (TaqMan Low-Density Array) and RT-qPCR assays. We show that miR-107 expression was significantly upregulated in GH-secreting and nonfunctioning pituitary adenomas. We found that human AIP-3'-UTR is a target of miR-107 since miR-107 inhibited in vitro AIP expression to 53.9 ± 2% of the miRNA control in a luciferase assay and reduced endogenous AIP mRNA expression to 53 ± 22% of the miRNA control in human cells. However, we did not observe a negative correlation between AIP and miR-107 expression in the human tumor samples. Furthermore, we show that miR-107 overexpression inhibited cell proliferation in human neuroblastoma and rat pituitary adenoma cells. In conclusion, miR-107 is overexpressed in pituitary adenomas and may act as a tumor suppressor. We have identified and confirmed AIP as a miR-107 target gene. Expression data in human samples suggest that the expression of AIP and miR-107 could be influenced by a combination of tumorigenic factors as well as compensatory mechanisms stimulated by the tumorigenic process.AJP Endocrinology and Metabolism 07/2012; 303(6):E708-19. · 4.75 Impact Factor
[show abstract] [hide abstract]
ABSTRACT: MiRNAs play an essential role in the networks of gene regulation by inhibiting the translation of target mRNAs. Several computational approaches have been proposed for the prediction of miRNA target-genes. Reports reveal a large fraction of under-predicted or falsely predicted target genes. Thus, there is an imperative need to develop a computational method by which the target mRNAs of existing miRNAs can be correctly identified. In this study, combined pattern recognition neural network (PRNN) and principle component analysis (PCA) architecture has been proposed in order to model the complicated relationship between miRNAs and their target mRNAs in humans. The results of several types of intelligent classifiers and our proposed model were compared, showing that our algorithm outperformed them with higher sensitivity and specificity. Using the recent release of the mirBase database to find potential targets of miRNAs, this model incorporated twelve structural, thermodynamic and positional features of miRNA:mRNA binding sites to select target candidates.Genomics 11/2012; · 3.02 Impact Factor
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