Identifying miRNA sponge modules using biclustering and regulatory scores

BMC Bioinformatics

Volumn 18, Issue , 2017, Pages

  Zhang, Junpeng ^a   Le, Thuc D ^b   Liu, Lin ^b   Li, Jiuyong ^b  

^a DALI UNIVERSITY   (China)

^b UNIVERSITY OF SOUTH AUSTRALIA   (Australia)

Author keywords

Biclustering method; Breast cancer; CeRNA; MiRNA sponge; MiRNA sponge module; Regulatory score

Indexed keywords

DISEASES;

BI-CLUSTERING; BREAST CANCER; CERNA; MIRNA SPONGE; MIRNA SPONGE MODULE; REGULATORY SCORE;

RNA;

MESSENGER RNA; MICRORNA;

ANIMAL; BREAST TUMOR; FEMALE; GENE EXPRESSION PROFILING; GENE EXPRESSION REGULATION; GENE REGULATORY NETWORK; GENETICS; HUMAN; METABOLISM; MOLECULAR MODEL; REPRODUCIBILITY;

ANIMALS; BREAST NEOPLASMS; FEMALE; GENE EXPRESSION PROFILING; GENE EXPRESSION REGULATION, NEOPLASTIC; GENE REGULATORY NETWORKS; HUMANS; MICRORNAS; MODELS, MOLECULAR; REPRODUCIBILITY OF RESULTS; RNA, MESSENGER;

EID: 85015192978     PISSN: None     EISSN: 14712105     Source Type: Journal    
DOI: 10.1186/s12859-017-1467-5     Document Type: Article

References (43)

1. Bartel DP. MicroRNAs: target recognition and regulatory functions. Cell. 2009;136:215-33.
2. Poliseno L, Salmena L, Zhang J, et al. A coding-independent function of gene and pseudogene mRNAs regulates tumour biology. Nature. 2010;465:1033-8.
3. 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.
4. Hansen TB, Jensen TI, Clausen BH, et al. Natural RNA circles function as efficient microRNA sponges. Nature. 2013;495:384-8.
5. Memczak S, Jens M, Elefsinioti A, et al. Circular RNAs are a large class of animal RNAs with regulatory potency. Nature. 2013;495:333-8.
6. Tay Y, Rinn J, Pandolfi PP. The multilayered complexity of ceRNA crosstalk and competition. Nature. 2014;505:344-52.
7. Le TD, Zhang J, Liu L, Li J. Computational methods for identifying miRNA sponge interactions. Briefings Bioinf. 2016, doi: 10.1093/bib/bbw042.
8. Zhou X, Liu J, Wang W. Construction and investigation of breast-cancer-specific ceRNA network based on the mRNA and miRNA expression data. IET Syst Biol. 2014;8:96-103.
9. 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.
10. Shao T, Wu A, Chen J, et al. Identification of module biomarkers from the dysregulated ceRNA-ceRNA interaction network in lung adenocarcinoma. Mol Biosyst. 2015;11:3048-58.
11. Chiu YC, Hsiao TH, Chen Y, et al. Parameter optimization for constructing competing endogenous RNA regulatory network in glioblastoma multiforme and other cancers. BMC Genomics. 2015;16:S1.
12. 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.
13. Sumazin P, Yang X, Chiu HS, et al. An extensive microRNA-mediated network of RNA-RNA interactions regulates established oncogenic pathways in glioblastoma. Cell. 2011;147:370-81.
14. Chiu HS, Llobet-Navas D, Yang X, et al. Cupid: simultaneous reconstruction of microRNA-target and ceRNA networks. Genome Res. 2015;25:257-67.
15. 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.
16. Bosia C, Pagnani A, Zecchina R. Modelling competing endogenous RNA networks. PLoS One. 2013;8:e66609.
17. 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 U S A. 2013;110:7154-9.
18. Yuan Y, Liu B, Xie P, et al. Model-guided quantitative analysis of microRNA-mediated regulation on competing endogenous RNAs using a synthetic gene circuit. Proc Natl Acad Sci U S A. 2015;112:3158-63.
19. Agarwal V, Bell GW, Nam JW, et al. Predicting effective microRNA target sites in mammalian mRNAs. Elife. 2015;4:e05005.
20. Bovolenta LA, Acencio ML, Lemke N. HTRIdb: an open-access database for experimentally verified human transcriptional regulation interactions. BMC Genomics. 2012;13:405.
21. Keshava Prasad TS, Goel R, Kandasamy K, et al. Human protein reference database--2009 update. Nucleic Acids Res. 2009;37:D767-72.
22. Plaisier CL, Pan M, Baliga NS. A miRNA-regulatory network explains how dysregulated miRNAs perturb oncogenic processes across diverse cancers. Genome Res. 2012;22:2302-14.
23. Subramanian A, Tamayo P, Mootha VK, et al. Gene set enrichment analysis: a knowledge-based approach for interpreting genome-wide expression profiles. Proc Natl Acad Sci U S A. 2005;102:15545-50.
24. Futreal PA, Coin L, Marshall M, et al. A census of human cancer genes. Nat Rev Cancer. 2004;4:177-83.
25. Becker KG, Barnes KC, Bright TJ, et al. The genetic association database. Nat Genet. 2004;36:431-2.
26. Hamosh A, Scott AF, Amberger JS, et al. Online mendelian inheritance in Man (OMIM), a knowledgebase of human genes and genetic disorders. Nucleic Acids Res. 2005;33:D514-7.
27. Baasiri RA, Glasser SR, Steffen DL, et al. The breast cancer gene database: a collaborative information resource. Oncogene. 1999;18:7958-65.
28. Mosca E, Alfieri R, Merelli I, et al. A multilevel data integration resource for breast cancer study. BMC Syst Biol. 2010;4:76.
29. Lu M, Zhang Q, Deng M, et al. An analysis of human microRNA and disease associations. PLoS One. 2008;3:e3420.
30. Jiang Q, Wang Y, Hao Y, et al. miR2Disease: a manually curated database for microRNA deregulation in human disease. Nucleic Acids Res. 2009;37:D98-D104.
31. Xie B, Ding Q, Han H, et al. miRCancer: a microRNA-cancer association database constructed by text mining on literature. Bioinformatics. 2013;29:638-44.
32. Wang D, Gu J, Wang T, et al. OncomiRDB: a database for the experimentally verified oncogenic and tumor-suppressive microRNAs. Bioinformatics. 2014;30:2237-8.
33. Ruepp A, Kowarsch A, Schmidl D, et al. PhenomiR: a knowledgebase for microRNA expression in diseases and biological processes. Genome Biol. 2010;11:R6.
34. 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.
35. Wang P, Zhi H, Zhang Y, et al. miRSponge: a manually curated database for experimentally supported miRNA sponges and ceRNAs. Database: the journal of biological databases and curation. 2015; doi: 10.1093/database/bav098.
36. Turner H, Bailey T, Krzanowski W. Improved biclustering of microarray data demonstrated through systematic performance tests. Comput Stat Data Anal. 2005;48:235-54.
37. Lazzeroni L, Owen A. Plaid models for gene expression data. Stat Sin. 2002;12:61-86.
38. Ritchie ME, Phipson B, Wu D, et al. Limma powers differential expression analyses for RNA-sequencing and microarray studies. Nucleic Acids Res. 2015;43:e47.
39. Tabas-Madrid D, Nogales-Cadenas R, Pascual-Montano A. GeneCodis3: a non-redundant and modular enrichment analysis tool for functional genomics. Nucleic Acids Res. 2012;40:W478-83.
40. Ashburner M, Ball CA, Blake JA, et al. Gene ontology: tool for the unification of biology. Nat Genet. 2000;25:25-9.
41. Kanehisa M, Goto S. KEGG: kyoto encyclopedia of genes and genomes. Nucleic Acids Res. 2000;28:27-30.
42. Krauss G. Biochemistry of Signal Transduction and Regulation. 4th ed. Hoboken: Wiley-VCH; 2008.
43. Yu Z, Baserga R, Chen L, et al. microRNA, cell cycle, and human breast cancer. Am J Pathol. 2010;176:1058-64.