Prediction of microRNA-disease associations based on distance correlation set

BackgroundRecently, numerous laboratory studies have indicated that many microRNAs (miRNAs) are involved in and associated with human diseases and can serve as potential biomarkers and drug targets. Therefore, developing effective computational models for the prediction of novel associations between diseases and miRNAs could be beneficial for achieving an understanding of disease mechanisms at the miRNA level and the interactions between diseases and miRNAs at the disease level. Thus far, only a few miRNA-disease association pairs are known, and models analyzing miRNA-disease associations based on lncRNA are limited.ResultsIn this study, a new computational method based on a distance correlation set is developed to predict miRNA-disease associations (DCSMDA) by integrating known lncRNA-disease associations, known miRNA-lncRNA associations, disease semantic similarity, and various lncRNA and disease similarity measures. The novelty of DCSMDA is due to the construction of a miRNA-lncRNA-disease network, which reveals that DCSMDA can be applied to predict potential lncRNA-disease associations without requiring any known miRNA-disease associations. Although the implementation of DCSMDA does not require known disease-miRNA associations, the area under curve is 0.8155 in the leave-one-out cross validation. Furthermore, DCSMDA was implemented in case studies of prostatic neoplasms, lung neoplasms and leukaemia, and of the top 10 predicted associations, 10, 9 and 9 associations, respectively, were separately verified in other independent studies and biological experimental studies. In addition, 10 of the 10 (100%) associations predicted by DCSMDA were supported by recent bioinformatical studies.ConclusionsAccording to the simulation results, DCSMDA can be a great addition to the biomedical research field.

[1]  Yi Zheng,et al.  Cross disease analysis of co-functional microRNA pairs on a reconstructed network of disease-gene-microRNA tripartite , 2017, BMC Bioinformatics.

[2]  S. Brenner,et al.  General Nature of the Genetic Code for Proteins , 1961, Nature.

[3]  Y-h. Taguchi,et al.  Inference of Target Gene Regulation via miRNAs during Cell Senescence by Using the MiRaGE Server , 2012, ICIC.

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

[5]  K Han,et al.  Prediction of disease-related microRNAs by incorporating functional similarity and common association information. , 2014, Genetics and molecular research : GMR.

[6]  Xia Li,et al.  Prediction of potential disease-associated microRNAs based on random walk , 2015, Bioinform..

[7]  Michel J. Weber.  New human and mouse microRNA genes found by homology search , 2004, The FEBS journal.

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

[9]  Bernhard Kuster,et al.  Lapatinib Resistance in Breast Cancer Cells Is Accompanied by Phosphorylation-Mediated Reprogramming of Glycolysis. , 2017, Cancer research.

[10]  Y. Wang,et al.  Mammalian ncRNA-disease repository: a global view of ncRNA-mediated disease network , 2013, Cell Death and Disease.

[11]  Roded Sharan,et al.  Associating Genes and Protein Complexes with Disease via Network Propagation , 2010, PLoS Comput. Biol..

[12]  Wei-De Zhong,et al.  miR-195 Inhibits Tumor Progression by Targeting RPS6KB1 in Human Prostate Cancer , 2015, Clinical Cancer Research.

[13]  M. Gerstein,et al.  RNA-Seq: a revolutionary tool for transcriptomics , 2009, Nature Reviews Genetics.

[14]  Hongmin Li,et al.  miR-424对非小细胞肺癌A549细胞生长和侵袭的影响及分子机制 , 2016, Zhongguo fei ai za zhi = Chinese journal of lung cancer.

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

[16]  Zhu-Hong You,et al.  PRMDA: personalized recommendation-based MiRNA-disease association prediction , 2017, Oncotarget.

[17]  Francis Crick,et al.  The Genetic Code for Proteins , 1963 .

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

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

[20]  Sek Won Kong,et al.  Altered microRNA expression in human heart disease. , 2007, Physiological genomics.

[21]  Vildan Bozok Çetintaş,et al.  miR-15a enhances the anticancer effects of cisplatin in the resistant non-small cell lung cancer cells , 2016, Tumor Biology.

[22]  V. Medina-Villaamil,et al.  MicroARN circulantes en sangre de pacientes con cáncer de próstata , 2014 .

[23]  V. Ambros.  The functions of animal microRNAs , 2004, Nature.

[24]  J. Mattick,et al.  Non-coding RNA. , 2006, Human molecular genetics.

[25]  W. Rottbauer,et al.  MicroRNA-21 contributes to myocardial disease by stimulating MAP kinase signalling in fibroblasts , 2008, Nature.

[26]  C. Runsheng,et al.  MicroRNA and lncRNA in Neurodegenerative Diseases , 2010 .

[27]  J. Rinn,et al.  Discovery and annotation of long noncoding RNAs , 2015, Nature Structural &Molecular Biology.

[28]  Q. Zou,et al.  Prediction of MicroRNA-Disease Associations Based on Social Network Analysis Methods , 2015, BioMed research international.

[29]  Dong Wang,et al.  Inferring the human microRNA functional similarity and functional network based on microRNA-associated diseases , 2010, Bioinform..

[30]  A. Giuliano,et al.  Pathophysiological basis of human papillomavirus in penile cancer: Key to prevention and delivery of more effective therapies , 2016, CA: a cancer journal for clinicians.

[31]  Jan A Staessen,et al.  Circulating MicroRNA-208b and MicroRNA-499 Reflect Myocardial Damage in Cardiovascular Disease , 2010, Circulation. Cardiovascular genetics.

[32]  C. Ji,et al.  MiR-424 and miR-27a increase TRAIL sensitivity of acute myeloid leukemia by targeting PLAG1 , 2016, Oncotarget.

[33]  C. Croce,et al.  miR-15 and miR-16 induce apoptosis by targeting BCL2. , 2005, Proceedings of the National Academy of Sciences of the United States of America.

[34]  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.

[35]  General , 1970 .

[36]  Peilin Jia,et al.  Key regulators in prostate cancer identified by co-expression module analysis , 2014, BMC Genomics.

[37]  Yu Wang,et al.  MiR-195 suppresses non-small cell lung cancer by targeting CHEK1 , 2015, Oncotarget.

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

[39]  Hong Jin,et al.  Expression profiles analysis reveals an integrated miRNA-lncRNA signature to predict survival in ovarian cancer patients with wild-type BRCA1/2 , 2017, Oncotarget.

[40]  Xing Chen,et al.  Predicting lncRNA-disease associations and constructing lncRNA functional similarity network based on the information of miRNA , 2015, Scientific Reports.

[41]  Wei Zhang,et al.  LncRNA H19 regulates ID2 expression through competitive binding to hsa-miR-19a/b in acute myelocytic leukemia. , 2017, Molecular medicine reports.

[42]  Xing Chen,et al.  LncRNADisease: a database for long-non-coding RNA-associated diseases , 2012, Nucleic Acids Res..

[43]  M. Esteller.  Non-coding RNAs in human disease , 2011, Nature Reviews Genetics.

[44]  M. Quindós-Varela,et al.  Circulating MicroRNAs in blood of patients with prostate cancer. , 2014, Actas urologicas espanolas.

[45]  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.

[46]  Xi Chen,et al.  mAPC-GibbsOS: an integrated approach for robust identification of gene regulatory networks , 2013, BMC Systems Biology.

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

[48]  Yufei Huang,et al.  Prediction of microRNAs Associated with Human Diseases Based on Weighted k Most Similar Neighbors , 2013, PloS one.

[49]  Mary Kay Barton,et al.  Local consolidative therapy may be beneficial in patients with oligometastatic non‐small cell lung cancer , 2017, CA: a cancer journal for clinicians.

[50]  A. Roses,et al.  Identification of miRNA Changes in Alzheimer's Disease Brain and CSF Yields Putative Biomarkers and Insights into Disease Pathways , 2008 .