Zarringhalam, KouroshTay, YvonneKulkarni, PrajnaBester, AssafPandolfi, Pier PaoloKulkarni, Rahul V.2017-11-212017Zarringhalam, 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.http://nrs.harvard.edu/urn-3:HUL.InstRepos:34375048Regulation 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.en-USJournal Article2017-11-2110.1038/s41598-017-08209-1