MKRMDA: multiple kernel learning-based Kronecker regularized least squares for MiRNA–disease association prediction

BackgroundRecently, as the research of microRNA (miRNA) continues, there are plenty of experimental evidences indicating that miRNA could be associated with various human complex diseases development and progression. Hence, it is necessary and urgent to pay more attentions to the relevant study of predicting diseases associated miRNAs, which may be helpful for effective prevention, diagnosis and treatment of human diseases. Especially, constructing computational methods to predict potential miRNA–disease associations is worthy of more studies because of the feasibility and effectivity.MethodsIn this work, we developed a novel computational model of multiple kernels learning-based Kronecker regularized least squares for MiRNA–disease association prediction (MKRMDA), which could reveal potential miRNA–disease associations by automatically optimizing the combination of multiple kernels for disease and miRNA.ResultsMKRMDA obtained AUCs of 0.9040 and 0.8446 in global and local leave-one-out cross validation, respectively. Meanwhile, MKRMDA achieved average AUCs of 0.8894 ± 0.0015 in fivefold cross validation. Furthermore, we conducted three different kinds of case studies on some important human cancers for further performance evaluation. In the case studies of colonic cancer, esophageal cancer and lymphoma based on known miRNA–disease associations in HMDDv2.0 database, 76, 94 and 88% of the corresponding top 50 predicted miRNAs were confirmed by experimental reports, respectively. In another two kinds of case studies for new diseases without any known associated miRNAs and diseases only with known associations in HMDDv1.0 database, the verified ratios of two different cancers were 88 and 94%, respectively.ConclusionsAll the results mentioned above adequately showed the reliable prediction ability of MKRMDA. We anticipated that MKRMDA could serve to facilitate further developments in the field and the follow-up investigations by biomedical researchers.

[1]  V. Ambros,et al.  The C. elegans heterochronic gene lin-4 encodes small RNAs with antisense complementarity to lin-14 , 1993, Cell.

[2]  Xing Chen,et al.  RKNNMDA: Ranking-based KNN for MiRNA-Disease Association prediction , 2017, RNA biology.

[3]  Peter D Siersema,et al.  Esophageal cancer. , 2008, Gastroenterology clinics of North America.

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

[5]  Tapio Pahikkala,et al.  Toward more realistic drug^target interaction predictions , 2014 .

[6]  Kishore Guda,et al.  The noncoding RNA, miR‐126, suppresses the growth of neoplastic cells by targeting phosphatidylinositol 3‐kinase signaling and is frequently lost in colon cancers , 2008, Genes, chromosomes & cancer.

[7]  Marie C. M. Lin,et al.  Proteomic identification of microRNA‐122a target proteins in hepatocellular carcinoma , 2010, Proteomics.

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

[9]  So Yeon Jeong,et al.  Comparison of inflammatory microRNA expression in healthy and periodontitis tissues. , 2011, Biocell : official journal of the Sociedades Latinoamericanas de Microscopia Electronica ... et. al.

[10]  X. Agirre,et al.  Identification by Real-time PCR of 13 mature microRNAs differentially expressed in colorectal cancer and non-tumoral tissues , 2006, Molecular Cancer.

[11]  Xing Chen,et al.  NLLSS: Predicting Synergistic Drug Combinations Based on Semi-supervised Learning , 2016, PLoS Comput. Biol..

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

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

[14]  Yongdong Zhang,et al.  Drug-target interaction prediction: databases, web servers and computational models , 2016, Briefings Bioinform..

[15]  Hangdi Xu,et al.  The effects of hepatitis C virus core protein on the expression of miR-122 in vitro , 2013, Virology Journal.

[16]  P. Sarnow,et al.  Modulation of Hepatitis C Virus RNA Abundance by a Liver-Specific MicroRNA , 2005, Science.

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

[18]  Xing Chen,et al.  Long non-coding RNAs and complex diseases: from experimental results to computational models , 2016, Briefings Bioinform..

[19]  C. Pasquier,et al.  Prediction of miRNA-disease associations with a vector space model , 2016, Scientific Reports.

[20]  M. Phipps,et al.  Screening for Colorectal Cancer: US Preventive Services Task Force Recommendation Statement. , 2016, JAMA.

[21]  Naoto Tsuchiya,et al.  Circulating Exosomal microRNAs as Biomarkers of Colon Cancer , 2014, PloS one.

[22]  B. Reinhart,et al.  The 21-nucleotide let-7 RNA regulates developmental timing in Caenorhabditis elegans , 2000, Nature.

[23]  M. Plummer,et al.  International agency for research on cancer. , 2020, Archives of pathology.

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

[25]  Xing Chen,et al.  Novel human lncRNA-disease association inference based on lncRNA expression profiles , 2013, Bioinform..

[26]  M. Feitelson,et al.  Hepatitis B virus integration, fragile sites, and hepatocarcinogenesis. , 2007, Cancer letters.

[27]  Yoshihiro Yamanishi,et al.  Chemogenomic approaches to infer drug-target interaction networks. , 2013, Methods in molecular biology.

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

[29]  Ivan G. Costa,et al.  A multiple kernel learning algorithm for drug-target interaction prediction , 2016, BMC Bioinformatics.

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

[31]  Z. Ling,et al.  Epidemiologic risk factors for esophageal cancer development. , 2011, Asian Pacific journal of cancer prevention : APJCP.

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

[33]  Xing Chen,et al.  MCMDA: Matrix completion for MiRNA-disease association prediction , 2017, Oncotarget.

[34]  M. DePamphilis,et al.  HUMAN DISEASE , 1957, The Ulster Medical Journal.

[35]  Ana Kozomara,et al.  miRBase: annotating high confidence microRNAs using deep sequencing data , 2013, Nucleic Acids Res..

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

[37]  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.

[38]  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.

[39]  S. McGuire.  World Cancer Report 2014. Geneva, Switzerland: World Health Organization, International Agency for Research on Cancer, WHO Press, 2015. , 2016, Advances in nutrition.

[40]  Jing Peng,et al.  SVM vs regularized least squares classification , 2004, ICPR 2004.

[41]  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.

[42]  Krister Wennerberg,et al.  Integrative and Personalized QSAR Analysis in Cancer by Kernelized Bayesian Matrix Factorization , 2014, J. Chem. Inf. Model..

[43]  Hong Wang,et al.  MiR-122 Inhibits Cell Proliferation and Tumorigenesis of Breast Cancer by Targeting IGF1R , 2012, PloS one.

[44]  V. Ambros.  microRNAs Tiny Regulators with Great Potential , 2001, Cell.

[45]  T. Sellers,et al.  Upregulation of miRNA-155 promotes tumour angiogenesis by targeting VHL and is associated with poor prognosis and triple-negative breast cancer , 2013, Oncogene.

[46]  Jan Gorodkin,et al.  Protein-driven inference of miRNA–disease associations , 2013, Bioinform..

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

[48]  Ethem Alpaydin,et al.  Multiple Kernel Learning Algorithms , 2011, J. Mach. Learn. Res..

[49]  P. Bork,et al.  Association of genes to genetically inherited diseases using data mining , 2002, Nature Genetics.

[50]  William Stafford Noble,et al.  Large-scale prediction of protein-protein interactions from structures , 2010, BMC Bioinformatics.

[51]  J. Steitz,et al.  Switching from Repression to Activation: MicroRNAs Can Up-Regulate Translation , 2007, Science.

[52]  M. Muckenthaler,et al.  Novel genomic amplification targeting the microRNA cluster at 19q13.42 in a pediatric embryonal tumor with abundant neuropil and true rosettes , 2008, Acta Neuropathologica.

[53]  James W Jacobson,et al.  MicroRNA: Potential for Cancer Detection, Diagnosis, and Prognosis. , 2007, Cancer research.

[54]  W. Tan,et al.  Circulating MicroRNAs, miR‐21, miR‐122, and miR‐223, in patients with hepatocellular carcinoma or chronic hepatitis , 2011, Molecular carcinogenesis.

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

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

[57]  J L Kelsey,et al.  Breast cancer: magnitude of the problem and descriptive epidemiology. , 1993, Epidemiologic reviews.

[58]  Xiaojun Chen,et al.  Large-scale prediction of microRNA-disease associations by combinatorial prioritization algorithm , 2017, Scientific Reports.

[59]  Mohammed Al-Shalalfa,et al.  Using context-specific effect of miRNAs to identify functional associations between miRNAs and gene signatures , 2013, BMC Bioinformatics.

[60]  Qionghai Dai,et al.  RBMMMDA: predicting multiple types of disease-microRNA associations , 2015, Scientific Reports.

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

[62]  Changning Liu,et al.  dbDEMC: a database of differentially expressed miRNAs in human cancers , 2010, BMC Genomics.

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

[64]  C. Croce,et al.  MicroRNA signatures in human cancers , 2006, Nature Reviews Cancer.

[65]  P. Bork,et al.  G2D: a tool for mining genes associated with disease , 2005, BMC Genetics.

[66]  G. Wahba,et al.  Some results on Tchebycheffian spline functions , 1971 .

[67]  T. Tuschl,et al.  Mechanisms of gene silencing by double-stranded RNA , 2004, Nature.

[68]  Stefano Volinia,et al.  MicroRNA Profiles Discriminate among Colon Cancer Metastasis , 2014, PloS one.

[69]  A. Barabasi,et al.  The human disease network , 2007, Proceedings of the National Academy of Sciences.

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

[71]  Michael H Handler,et al.  Survey of MicroRNA expression in pediatric brain tumors , 2011, Pediatric blood & cancer.

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

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

[74]  A. Pasquinelli,et al.  Control of developmental timing by micrornas and their targets. , 2002, Annual review of cell and developmental biology.

[75]  Elena Marchiori,et al.  Gaussian interaction profile kernels for predicting drug-target interaction , 2011, Bioinform..

[76]  Pei-ying Zhang,et al.  MicroRNA-10b targets E-cadherin and modulates breast cancer metastasis , 2012, Medical science monitor : international medical journal of experimental and clinical research.

[77]  A. Jemal,et al.  Global cancer statistics , 2011, CA: a cancer journal for clinicians.

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

[79]  W. Cho.  MicroRNAs: potential biomarkers for cancer diagnosis, prognosis and targets for therapy. , 2010, The international journal of biochemistry & cell biology.

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