A novel method for identifying potential disease-related miRNAs via a disease-miRNA-target heterogeneous network.

MicroRNAs (miRNAs), as a kind of important small endogenous single-stranded non-coding RNA, play critical roles in a large number of human diseases. However, the currently known experimental verifications of the disease-miRNA associations are still rare and experimental identification is time-consuming and labor-intensive. Accordingly, identifying potential disease-related miRNAs to help people understand the pathogenesis of complex diseases has become a hot topic. In this study, we take advantage of known disease-miRNA associations combined with a large number of experimentally validated miRNA-target associations, and further develop a novel disease-miRNA-target heterogeneous network for identifying disease-related miRNAs. The leave-one-out cross validation experiment and several statistical measures demonstrate that our method can effectively identify potential disease-related miRNAs. Furthermore, the good predictive performance of 15 common diseases and the manually confirmed analyses of the top 30 candidates of hepatocellular carcinoma, ovarian neoplasms and breast neoplasms further provide convincing evidence of the practical ability of our method. The source code implemented by our method is freely available at: .

[1]  Xing Chen,et al.  Semi-supervised learning for potential human microRNA-disease associations inference , 2014, Scientific Reports.

[2]  Yadong Wang,et al.  miR2Disease: a manually curated database for microRNA deregulation in human disease , 2008, Nucleic Acids Res..

[3]  F. Slack,et al.  Oncomirs — microRNAs with a role in cancer , 2006, Nature Reviews Cancer.

[4]  Ao Li,et al.  Predicting Long Noncoding RNA and Protein Interactions Using Heterogeneous Network Model , 2015, BioMed research international.

[5]  Yang Li,et al.  HMDD v2.0: a database for experimentally supported human microRNA and disease associations , 2013, Nucleic Acids Res..

[6]  W. Ritchie,et al.  Predicting microRNA targets and functions: traps for the unwary , 2009, Nature Methods.

[7]  Fabian J Theis,et al.  PhenomiR: a knowledgebase for microRNA expression in diseases and biological processes , 2010, Genome Biology.

[8]  D. Bartel.  MicroRNAs: Target Recognition and Regulatory Functions , 2009, Cell.

[9]  Wei Wu,et al.  miR-150 Promotes Human Breast Cancer Growth and Malignant Behavior by Targeting the Pro-Apoptotic Purinergic P2X7 Receptor , 2013, PloS one.

[10]  Michael Kertesz,et al.  The role of site accessibility in microRNA target recognition , 2007, Nature Genetics.

[11]  E. Miska,et al.  How microRNAs control cell division, differentiation and death. , 2005, Current opinion in genetics & development.

[12]  Q. Cui,et al.  An Analysis of Human MicroRNA and Disease Associations , 2008, PloS one.

[13]  J. Koenderink.  Q… , 2014, Les noms officiels des communes de Wallonie, de Bruxelles-Capitale et de la communaute germanophone.

[14]  Qionghai Dai,et al.  WBSMDA: Within and Between Score for MiRNA-Disease Association prediction , 2016, Scientific Reports.

[15]  Hsien-Da Huang,et al.  miRTarBase 2016: updates to the experimentally validated miRNA-target interactions database , 2015, Nucleic Acids Res..

[16]  C. Burge,et al.  Prediction of Mammalian MicroRNA Targets , 2003, Cell.

[17]  이화영.  X , 1960, Chinese Plants Names Index 2000-2009.

[18]  Jian-wei Tian,et al.  MicroRNA-130b promotes lung cancer progression via PPARγ/VEGF-A/BCL-2-mediated suppression of apoptosis , 2016, Journal of Experimental & Clinical Cancer Research.

[19]  Xinghua Shi,et al.  A Network Based Method for Analysis of lncRNA-Disease Associations and Prediction of lncRNAs Implicated in Diseases , 2014, PloS one.

[20]  장윤희,et al.  Y. , 2003, Industrial and Labor Relations Terms.

[21]  Di Wu,et al.  miRCancer: a microRNA-cancer association database constructed by text mining on literature , 2013, Bioinform..

[22]  Xing Chen,et al.  PBMDA: A novel and effective path-based computational model for miRNA-disease association prediction , 2017, PLoS Comput. Biol..

[23]  Aaas News,et al.  Book Reviews , 1893, Buffalo Medical and Surgical Journal.

[24]  Zhu-Hong You,et al.  A novel computational model based on super-disease and miRNA for potential miRNA-disease association prediction. , 2017, Molecular bioSystems.

[25]  R. Stephenson.  A and V , 1962, The British journal of ophthalmology.

[26]  Hui Zhou,et al.  starBase v2.0: decoding miRNA-ceRNA, miRNA-ncRNA and protein–RNA interaction networks from large-scale CLIP-Seq data , 2013, Nucleic Acids Res..

[27]  C. Croce,et al.  MicroRNA gene expression deregulation in human breast cancer. , 2005, Cancer research.

[28]  Ujjwal Maulik,et al.  Development of the human cancer microRNA network , 2010 .

[29]  T. Tuller,et al.  A code for transcription elongation speed , 2018, RNA biology.

[30]  C. Bracken,et al.  Experimental strategies for microRNA target identification , 2011, Nucleic acids research.

[31]  M. Latronico,et al.  Emerging role of microRNAs in cardiovascular biology. , 2007, Circulation research.

[32]  Yadong Wang,et al.  Prioritization of disease microRNAs through a human phenome-microRNAome network , 2010, BMC Systems Biology.

[33]  Neil Genzlinger.  A. and Q , 2006 .

[34]  Huanqing Feng,et al.  NTSMDA: prediction of miRNA-disease associations by integrating network topological similarity. , 2016, Molecular bioSystems.

[35]  Wei Tang,et al.  dbDEMC 2.0: updated database of differentially expressed miRNAs in human cancers , 2016, Nucleic Acids Res..

[36]  Peizhang Xu,et al.  MicroRNAs and the regulation of cell death. , 2004, Trends in genetics : TIG.

[37]  Ping Yang,et al.  MicroRNA‐200 promotes lung cancer cell growth through FOG2‐independent AKT activation , 2015, IUBMB life.

[38]  G. G. Stokes.  "J." , 1890, The New Yale Book of Quotations.

[39]  Ping Zhang,et al.  Personalized recommendation based on unbiased consistence , 2015 .

[40]  Yi-Cheng Zhang,et al.  Bipartite network projection and personal recommendation. , 2007, Physical review. E, Statistical, nonlinear, and soft matter physics.

[41]  W. Marsden.  I and J , 2012 .

[42]  M. Byrom,et al.  Antisense inhibition of human miRNAs and indications for an involvement of miRNA in cell growth and apoptosis , 2005, Nucleic acids research.

[43]  C. Mathers,et al.  Estimates of worldwide burden of cancer in 2008: GLOBOCAN 2008 , 2010, International journal of cancer.

[44]  Shangwei Ning,et al.  Prioritizing human cancer microRNAs based on genes’ functional consistency between microRNA and cancer , 2011, Nucleic acids research.

[45]  Zhi-Ping Liu,et al.  Prediction of protein-RNA binding sites by a random forest method with combined features , 2010, Bioinform..

[46]  Taylor Murray,et al.  Cancer statistics, 2000 , 2000, CA: a cancer journal for clinicians.

[47]  Hailin Chen,et al.  Prediction of Associations between OMIM Diseases and MicroRNAs by Random Walk on OMIM Disease Similarity Network , 2013, TheScientificWorldJournal.

[48]  Björn Kemper,et al.  microRNA miR-142-3p Inhibits Breast Cancer Cell Invasiveness by Synchronous Targeting of WASL, Integrin Alpha V, and Additional Cytoskeletal Elements , 2015, PloS one.

[49]  Yun Xiao,et al.  Prioritizing Candidate Disease miRNAs by Topological Features in the miRNA Target–Dysregulated Network: Case Study of Prostate Cancer , 2011, Molecular Cancer Therapeutics.

[50]  D. Bartel.  MicroRNAs Genomics, Biogenesis, Mechanism, and Function , 2004, Cell.

[51]  Xing Chen,et al.  RWRMDA: predicting novel human microRNA-disease associations. , 2012, Molecular bioSystems.

[52]  Ted M. Dawson,et al.  MicroRNAs in Parkinson's disease , 2011, Journal of Chemical Neuroanatomy.

[53]  Xantha Karp,et al.  Encountering MicroRNAs in Cell Fate Signaling , 2005, Science.

[54]  Tsung-Cheng Chang,et al.  microRNAs in vertebrate physiology and human disease. , 2007, Annual review of genomics and human genetics.

[55]  Xia Li,et al.  Walking the interactome to identify human miRNA-disease associations through the functional link between miRNA targets and disease genes , 2013, BMC Systems Biology.

[56]  Jiuyong Li,et al.  Identifying miRNAs, targets and functions , 2012, Briefings Bioinform..

[57]  Xing Chen,et al.  HGIMDA: Heterogeneous graph inference for miRNA-disease association prediction , 2016, Oncotarget.

[58]  Yadong Wang,et al.  Predicting human microRNA-disease associations based on support vector machine , 2013, Int. J. Data Min. Bioinform..

[59]  Ting Wang,et al.  OncomiRDB: a database for the experimentally verified oncogenic and tumor-suppressive microRNAs , 2014, Bioinform..

[60]  H. El‐Serag,et al.  Hepatocellular carcinoma: epidemiology and molecular carcinogenesis. , 2007, Gastroenterology.

[61]  Xiao-Ying Huang,et al.  MicroRNA-429 Modulates Hepatocellular Carcinoma Prognosis and Tumorigenesis , 2013, Gastroenterology research and practice.