Identification of competing endogenous RNAs of the tumor suppressor gene PTEN: A probabilistic approach

• Published in: Scientific reports 2017-08, Vol.7 (1), p.7755-12, Article 7755
• Main Authors: Zarringhalam, Kourosh, Tay, Yvonne, Kulkarni, Prajna, Bester, Assaf C., Pandolfi, Pier Paolo, Kulkarni, Rahul V.
• Format: Article
• Language: English
• Subjects: 3' Untranslated Regions; 38/109; 38/77; 38/91; 631/114/2114; 631/114/2397; Binding sites; Cell Line, Tumor; Gene Expression Regulation, Neoplastic; Humanities and Social Sciences; Humans; Mathematical models; MicroRNAs - genetics; MicroRNAs - metabolism; miRNA; Models, Theoretical; multidisciplinary; Probability; Prostate cancer; PTEN Phosphohydrolase - genetics; PTEN Phosphohydrolase - metabolism; PTEN protein; RNA, Messenger - genetics; RNA, Messenger - metabolism; Science; Science (multidisciplinary); Spatial distribution; Target recognition; Transcription; Tumor cell lines; Tumor suppressor genes
• ISSN: 2045-2322; 2045-2322
• DOI: 10.1038/s41598-017-08209-1

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.