ArticleScientific reports2017
Identification of competing endogenous RNAs of the tumor suppressor gene PTEN: A probabilistic approach.
Article in Scientific reports, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 15 papers.
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Who cites it
15 citing papers in PubMed, 25 citations in OpenAlex.
- Coding, or non-coding, that is the question.Cell research · 2024Review
- In silico analysis of prognostic and diagnostic significance of target genes from prostate cancer cell lines derived exomicroRNAs.Cancer cell international · 2023Article
- Review
- Precise Identification of Recurrent Somatic Mutations in Oral Cancer Through Whole-Exome Sequencing Using Multiple Mutation Calling Pipelines.Frontiers in oncology · 2021Article
- Modulation of Vascular Smooth Muscle Cell Multiplication, Apoptosis, and Inflammatory Damage by miR-21 in Coronary Heart Disease.Computational and mathematical methods in medicine · 2021Article
- Replication Study: Coding-independent regulation of the tumor suppressor PTEN by competing endogenous mRNAs.eLife · 2020Article
- EMT signaling: potential contribution of CRISPR/Cas gene editing.Cellular and molecular life sciences : CMLS · 2020Review
- The Complex Landscape ofCold Spring Harbor perspectives in medicine · 2020Review
- Identification and validation of mRNA 3'untranslated regions of DNMT3B and TET3 as novel competing endogenous RNAs of the tumor suppressor PTEN.International journal of oncology · 2020Article
- miRNA-based biomarkers, therapies, and resistance in Cancer.International journal of biological sciences · 2020Review
- Article
- Pan-cancer characterisation of microRNA across cancer hallmarks reveals microRNA-mediated downregulation of tumour suppressors.Nature communications · 2018Article
- Arena-Idb: a platform to build human non-coding RNA interaction networks.BMC bioinformatics · 2018Article
- The functions and regulation of the PTEN tumour suppressor: new modes and prospects.Nature reviews. Molecular cell biology · 2018Review
- LncCeRBase: a database of experimentally validated human competing endogenous long non-coding RNAs.Database : the journal of biological databases and curation · 2018Article
Corrections and comments
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Authors and funding
6 authors at 3 institutions in 2 countries.
Funding
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.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.