Literature DB >> 27273287

Computational methods for identifying miRNA sponge interactions.

Thuc Duy Le1, Junpeng Zhang2, Lin Liu1, Jiuyong Li1.   

Abstract

Recent findings show that coding genes are not the only targets that miRNAs interact with. In fact, there is a pool of different RNAs competing with each other to attract miRNAs for interactions, thus acting as competing endogenous RNAs (ceRNAs). The ceRNAs indirectly regulate each other via the titration mechanism, i.e. the increasing concentration of a ceRNA will decrease the number of miRNAs that are available for interacting with other targets. The cross-talks between ceRNAs, i.e. their interactions mediated by miRNAs, have been identified as the drivers in many disease conditions, including cancers. In recent years, some computational methods have emerged for identifying ceRNA-ceRNA interactions. However, there remain great challenges and opportunities for developing computational methods to provide new insights into ceRNA regulatory mechanisms.In this paper, we review the publically available databases of ceRNA-ceRNA interactions and the computational methods for identifying ceRNA-ceRNA interactions (also known as miRNA sponge interactions). We also conduct a comparison study of the methods with a breast cancer dataset. Our aim is to provide a current snapshot of the advances of the computational methods in identifying miRNA sponge interactions and to discuss the remaining challenges.
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Entities:  

Keywords:  cancer; ceRNA; ceRNA-ceRNA interaction; computational method; miRNA; miRNA sponge interaction

Mesh:

Substances:

Year:  2017        PMID: 27273287     DOI: 10.1093/bib/bbw042

Source DB:  PubMed          Journal:  Brief Bioinform        ISSN: 1467-5463            Impact factor:   11.622


  36 in total

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3.  Circular RNA profile in Graves' disease and potential function of hsa_circ_0090364.

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Journal:  Endocr Connect       Date:  2022-09-28       Impact factor: 3.221

4.  Hierarchical graph attention network for miRNA-disease association prediction.

Authors:  Zhengwei Li; Tangbo Zhong; Deshuang Huang; Zhu-Hong You; Ru Nie
Journal:  Mol Ther       Date:  2022-02-02       Impact factor: 12.910

5.  Analyzing the interactions of mRNAs, miRNAs, lncRNAs and circRNAs to predict competing endogenous RNA networks in glioblastoma.

Authors:  Yang Yuan; Li Jiaoming; Wang Xiang; Liu Yanhui; Jiang Shu; Gou Maling; Mao Qing
Journal:  J Neurooncol       Date:  2018-01-15       Impact factor: 4.130

6.  miRSM: an R package to infer and analyse miRNA sponge modules in heterogeneous data.

Authors:  Junpeng Zhang; Lin Liu; Taosheng Xu; Wu Zhang; Chunwen Zhao; Sijing Li; Jiuyong Li; Nini Rao; Thuc Duy Le
Journal:  RNA Biol       Date:  2021-04-06       Impact factor: 4.652

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

Authors:  Kourosh Zarringhalam; Yvonne Tay; Prajna Kulkarni; Assaf C Bester; Pier Paolo Pandolfi; Rahul V Kulkarni
Journal:  Sci Rep       Date:  2017-08-10       Impact factor: 4.379

8.  Integrated analysis of dosage effect lncRNAs in lung adenocarcinoma based on comprehensive network.

Authors:  Yunzhen Wei; Zichuang Yan; Cheng Wu; Qiang Zhang; Yinling Zhu; Kun Li; Yan Xu
Journal:  Oncotarget       Date:  2017-08-03

9.  Inferring miRNA sponge co-regulation of protein-protein interactions in human breast cancer.

Authors:  Junpeng Zhang; Thuc Duy Le; Lin Liu; Jiuyong Li
Journal:  BMC Bioinformatics       Date:  2017-05-08       Impact factor: 3.169

10.  Identification and Characterization of miRNA Transcriptome in Asiatic Cotton (Gossypium arboreum) Using High Throughput Sequencing.

Authors:  Muhammad Farooq; Shahid Mansoor; Hui Guo; Imran Amin; Peng W Chee; M Kamran Azim; Andrew H Paterson
Journal:  Front Plant Sci       Date:  2017-06-15       Impact factor: 5.753

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