Identification of competing endogenous RNAs of the tumor suppressor gene PTEN: A probabilistic approach
Pandolfi, Pier Paolo
Kulkarni, Rahul V.Note: Order does not necessarily reflect citation order of authors.
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CitationZarringhalam, Kourosh, Yvonne Tay, Prajna Kulkarni, Assaf C. Bester, Pier Paolo Pandolfi, and Rahul V. Kulkarni. 2017. “Identification of competing endogenous RNAs of the tumor suppressor gene PTEN: A probabilistic approach.” Scientific Reports 7 (1): 7755. doi:10.1038/s41598-017-08209-1. http://dx.doi.org/10.1038/s41598-017-08209-1.
AbstractRegulation 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.
Citable link to this pagehttp://nrs.harvard.edu/urn-3:HUL.InstRepos:34375048
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