Generic placeholder image

Current Bioinformatics


ISSN (Print): 1574-8936
ISSN (Online): 2212-392X

Research Article

Exploring miRNA Sponge Networks of Breast Cancer by Combining miRNA-disease-lncRNA and miRNA-target Networks

Author(s): Lei Tian and Shu-Lin Wang*

Volume 16, Issue 3, 2021

Published on: 11 July, 2020

Page: [385 - 394] Pages: 10

DOI: 10.2174/1574893615999200711171530

Price: $65


Background: Recently, ample researches show that microRNAs (miRNAs) not only interact with coding genes but interact with a pool of different RNAs. Those RNAs are called miRNA sponges, including long non-coding RNAs (lncRNAs), circular RNA, pseudogenes and various messenger RNAs. Understanding regulatory networks of miRNA sponges can better help researchers to study the mechanisms of breast cancers.

Objective: We develop a new method to explore miRNA sponge networks of breast cancer by combining miRNA-disease-lncRNA and miRNA-target networks (MSNMDL).

Methods: Firstly, MSNMDL infers miRNA-lncRNA functional similarity networks from miRNAdisease- lncRNA networks. Secondly, MSNMDL forms lncRNA-target networks by using lncRNA to replace the role of matched miRNA in miRNA-target networks according to the lncRNA-miRNA pair of miRNA-lncRNA functional similarity networks. And MSNMDL only retains the genes of breast cancer in lncRNA-target networks to construct candidate miRNA sponge networks. Thirdly, MSNMDL merges these candidate miRNA sponge networks with other miRNA sponge interactions and then selects top-hub lncRNA and its interactions to construct miRNA sponge networks.

Result: MSNMDL is superior to other methods in terms of biological significance and its identified modules might act as module signatures for prognostication of breast cancer.

Conclusion: MiRNA sponge networks identified by MSNMDL are biologically significant and are closely associated with breast cancer, which makes MSNMDL a promising way for researchers to study the pathogenesis of breast cancer.

Keywords: miRNA sponge networks, miRNA sponge modules, breast cancer, biological enrichment, clustering algorithm, prognostication.

Graphical Abstract
Djebali S, Davis CA, Merkel A, et al.. Landscape of transcription in human cells Nature 2012; 489(7414): 101-8.
Bartel DP. MicroRNAs: target recognition and regulatory functions. Cell 2009; 136: 215-33.
Sardina DS, Alaimo S, Ferro A, Pulvirenti A, Giugno R. A novel computational method for inferring competing endogenous interactions. Brief Bioinform 2017; 18: 1071-81.
Chen X, Xie D, Zhao Q, You ZH. MicroRNAs and complex diseases: from experimental results to computational models. Brief Bioinform 2019; 20: 515-39.
Tay Y, Rinn J, Pandolfi PP. The multilayered complexity of ceRNA crosstalk and competition. Nature 2014; 505: 344-52.
Salmena L, Poliseno L, Tay Y, Kats L, Pandolfi PP. A ceRNA hypothesis: the rosetta stone of a hidden RNA language? Cell 2011.
Cesana M, Cacchiarelli D, Legnini I, et al. A long noncoding RNA controls muscle differentiation by functioning as a competing endogenous RNA. Cell 2011; 147: 358-69.
D’Errico I, Gadaleta G, Saccone C. Pseudogenes in metazoa: origin and features. Brief Funct Genomics Proteomics 2004; 3: 157-67.
Chen LL, Yang L. Regulation of circRNA biogenesis. RNA Biol 2015; 12(4): 381-8.
Hansen TB, Jensen TI, Clausen BH, et al. Natural RNA circles function as efficient microRNA sponges. Nature 2013; 495: 384-8.
Le TD, Zhang J, Liu L, Li J. Computational methods for identifying miRNA sponge interactions. Brief Bioinform 2017; 18: 577-90.
Ala U, Karreth FA, Bosia C, et al. Integrated transcriptional and competitive endogenous RNA networks are cross-regulated in permissive molecular environments. Proc Natl Acad Sci 2013; 110(18): 7154-9.
Zhang J, Le TD, Liu L, Li J. Identifying miRNA sponge modules using biclustering and regulatory scores. BMC Bioinformatics 2017; 18: 44.
Turner H, Bailey T, Krzanowski W. Improved biclustering of microarray data demonstrated through systematic performance tests. Comput Stat Data Anal 2005; 48: 235-54.
Paci P, Colombo T, Farina L. Computational analysis identifies a sponge interaction network between long non-coding RNAs and messenger RNAs in human breast cancer. BMC Syst Biol 2014; 8: 83.
Chiu HS, Llobet-Navas D, Yang X, et al. Cupid: Simultaneous reconstruction of micrornatarget and cerna networks. Genome Res 2015; 25: 257-67.
Olgun G, Sahin O, Tastan O. Discovering competing endogenous RNA interactions in breast cancer molecular subtypes. bioRxiv..
Zhang K, Peters J, Janzing D, Schölkopf B. Kernel-based conditional independence test and application in causal discovery. Proc 27th Conf Uncertain Artif Intell UAI 2011;. 804-13.
Figliuzzi M, Marinari E, De Martino A. MicroRNAs as a selective channel of communication between competing RNAs: a steady-state theory. Biophys J 2013; 104: 1203-13.
Tian L, Wang S-L. Exploring the potential MicroRNA sponge interactions of breast cancer based on some known interactions. J Bioinform Comput Biol 2019; 6: 1-8.
Cui T, Zhang L, Huang Y, et al. MNDR v2.0: an updated resource of ncRNA-disease associations in mammals. Nucleic Acids Res 2018; 46: D371-4.
Chou CH, Chang NW, Shrestha S, et al. miRTarBase 2016: Updates to the experimentally validated miRNA-target interactions database. Nucleic Acids Res 2016; 44: D239-47.
Vlachos IS, Paraskevopoulou MD, Karagkouni D, et al. DIANA-TarBase v7.0: Indexing more than half a million experimentally supported miRNA:mRNA interactions. Nucleic Acids Res 2015; 43: D153-9.
Dweep H, Gretz N. MiRWalk2.0: a comprehensive atlas of microRNA-target interactions. Nat Methods 2015; 12(8): 697.
Hao Y, Wu W, Li H, et al. NPInter v3.0: an upgraded database of noncoding RNA-associated interactions. Database 2016; 2016: baw057-baw057..
Paraskevopoulou MD, Vlachos IS, Karagkouni D, et al. DIANA-LncBase v2: indexing microRNA targets on non-coding transcripts. Nucleic Acids Res 2016; 2016: D231-8.
Agarwal V, Bell GW, Nam JW, Bartel DP. Predicting effective microRNA target sites in mammalian mRNAs. eLife 2015; 4e05005
Li JH, Liu S, Zhou H, Qu LH, Yang JH. StarBase v2.0: Decoding miRNA-ceRNA, miRNA-ncRNA and protein-RNA interaction networks from large-scale CLIP-Seq data. Nucleic Acids Res 2014; 42(Database issue): D92-7.
Zhang J, Le TD, Liu L, Li J. Inferring miRNA sponge co-regulation of protein-protein interactions in human breast cancer. BMC Bioinformatics 2017; 18: 235.
Wang P, Li X, Gao Y, et al. Lncactdb 2.0: An updated database of experimentally supported cerna interactions curated from low- and high-throughput experiments. Nucleic Acids Res 2019; 47: D121-7.
Pian C, Zhang G, Tu T, Ma X, Li F. LncCeRBase: a database of experimentally validated human competing endogenous long non-coding RNAs. Database 2018; 2018: 1-4.
Cheng L, Wang P, Tian R, et al. Lncrna2target v2.0: a comprehensive database for target genes of lncrnas in human and mouse. Nucleic Acids Res 2019; 47: D140-4.
Xu J, Feng L, Han Z, et al. Extensive ceRNA-ceRNA interaction networks mediated by miRNAs regulate development in multiple rhesus tissues. Nucleic Acids Res 2016; 44: 9438-51.
Xu J, Li Y, Lu J, et al. The mRNA related ceRNA-ceRNA landscape and significance across 20 major cancer types. Nucleic Acids Res 2015; 43: 8169-82.
Ogata H, Goto S, Sato K, Fujibuchi W, Bono H, Kanehisa M. KEGG: Kyoto encyclopedia of genes and genomes. Nucleic Acids Res 1999; 27-30
Zhang J, Liu L, Xu T, et al. MiRspongeR: An R/Bioconductor package for the identification and analysis of miRNA sponge interaction networks and modules. BMC Bioinformatics 2019; 20: 1-12.
Viteri G, Matthews L, Varusai T, et al. Reactome and ORCID-fine-grained credit attribution for community curation. Database (Oxford) 2019; 2019: 1-5.
Howe LR, Brown AMC. Wht signaling and breast cancer. Cancer Biol Ther 2004; 3: 36-41.
Mittal S, Subramanyam D, Dey D, Kumar RV, Rangarajan A. Cooperation of Notch and Ras/MAPK signaling pathways in human breast carcinogenesis. Mol Cancer 2009; 8: 1-12.
Yu Z, Baserga R, Chen L, Wang C, Lisanti MP, Pestell RG. MicroRNA, cell cycle, and human breast cancer. Am J Pathol 2010; 176(3): 1058-64.
Tokunaga E, Kimura Y, Mashino K, et al. Activation of PI3K/Akt signaling and hormone resistance in breast cancer. Breast Cancer 2006; 13: 137-44.
Zhang J, Yao S, Hu Q, et al. Genetic variations in the Hippo signaling pathway and breast cancer risk in African American women in the AMBER Consortium. Carcinogenesis 2016; 37: 951-6.
Tay Y, Kats L, Salmena L, et al. Coding-independent regulation of the tumor suppressor PTEN by competing endogenous mRNAs. Cell 2011; 147: 344-57.

Rights & Permissions Print Export Cite as
© 2022 Bentham Science Publishers | Privacy Policy