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Abstract
Regulation by microRNAs (miRNAs) and modulation of miRNA activity are critical components of diverse cellular processes. Recent research has shown that miRNA-based regulation of the tumor suppressor gene PTEN can be modulated by the expression of other miRNA targets acting as competing endogenous RNAs (ceRNAs). However, the key sequence-based features enabling a transcript to act as an effective ceRNA are not well understood and a quantitative model associating statistical significance to such features is currently lacking. To identify and assess features characterizing target recognition by PTEN-regulating miRNAs, we analyze multiple datasets from PAR-CLIP experiments in conjunction with RNA-Seq data. We consider a set of miRNAs known to regulate PTEN and identify high-confidence binding sites for these miRNAs on the 3′ UTR of protein coding genes. Based on the number and spatial distribution of these binding sites, we calculate a set of probabilistic features that are used to make predictions for novel ceRNAs of PTEN. Using a series of experiments in human prostate cancer cell lines, we validate the highest ranking prediction (TNRC6B) as a ceRNA of PTEN. The approach developed can be applied to map ceRNA networks of critical cellular regulators and to develop novel insights into crosstalk between different pathways involved in cancer.
Highlights
MicroRNAs are known to be critical components of tumor suppressive pathways and dysregulation of miRNAs is commonly observed in human cancers[1, 2]
We focus on transcripts for which the prediction that it can act as a competing endogenous RNAs (ceRNAs) goes both ways, i.e. if a transcript acts as an effective ceRNA of PTEN, the converse must be true, i.e., PTEN should act as an effective ceRNA of the transcript
We expect that our approach using PARCLIP only identifies a subset of PTEN ceRNAs which have multiple high-confidence miRNA response elements (MREs) sites that are identified by PAR-CLIP
Summary
MicroRNAs (miRNAs) are known to be critical components of tumor suppressive pathways and dysregulation of miRNAs is commonly observed in human cancers[1, 2]. Following the original discovery of ceRNA-based regulation of PTEN, several groups have developed methods for genome-wide prediction of PTEN ceRNAs5, 6, 9, 15, 16 These approaches have focused on identifying ceRNAs based on: a) sequence-based features derived from the locations and binding affinities of different miRNA binding sites in 3′ UTR regions and b) analysis of co-expression data across multiple samples and tissues. 25 demonstrates the ceRNA effect due to high levels of expression of the neuroblastoma master oncogene MYCN, which impacts regulation by the highly abundant let-7 miRNA family in MYCN-amplified neuroblastoma cells This discovery highlights the importance of identifying potential ceRNAs for a given target gene: transcripts which, provided they can be amplified to high levels (either naturally or by inducing them), can give rise to ceRNA-based regulation. While this restriction suggests that some bona fide ceRNAs will be missed by our approach, it is expected that the method will lead to high-confidence predictions
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