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Molecular Biology

, Volume 52, Issue 5, pp 749–760 | Cite as

Inferring Disease–miRNA Associations by Self-Weighting with Multiple Data Source

  • X. Y. Yang
  • L. Gao
  • C. Liang
BIOINFORMATICS
  • 16 Downloads

Abstract

Increasing evidence has suggested that microRNAs (miRNAs) may function as positive regulators at the post-transcriptional level. A search for associations between miRNAs and diseases is crucial for understanding the pathogenesis. Various publicly available databases have been constructed to store meaningful information on a large number of miRNA molecules. In this study, to resolve the limitation that individual sources of miRNA target data tend to be incomplete and noisy, we propose a network-based computational method called self-weighting for integrating multiple data sources. A bipartite phenotype-miRNA network (BPMN) incorporates known disease–miRNA interactions as well as the similarities between disease phenotypes and functional similarities of miRNAs. Random walk with restart algorithm was deployed on the bipartite network to predict novel disease–miRNA associations. In leave-one-out cross-validation experiments, our technique achieves an AUC of 0.801 when evaluating against known disease-related miRNAs from HMDD. Systematic prioritization of miRNAs for 11 common diseases obtained an average AUC of 0.765. Additionally, a case study on colon cancer uncovered a number of potential miRNA candidates as biomarkers of this disease.

Keywords:

Bipartite network database disease-miRNA associations random walk self-weighting 

Notes

ACKNOWLEDGMENTS

This work was supported by the National Science Foundation of China (nos. 61602283, 31302283, 61672329, 61170145, 61373081), The Specialized Research Fund for the Doctoral Program of Higher Education of China (no. 20113704110001), The Natural Science Foundation of Shandong (nos. ZR2016FB10, ZR2010FM021), the Technology and Development Project of Shandong (no. 2013GGX10125) and the Taishan Scholar Project of Shandong, China.

REFERENCES

  1. 1.
    Hammond S.M. 2015. An overview of microRNAs. Adv. Drug. Deliv. Rev. 87, 3–14.CrossRefPubMedPubMedCentralGoogle Scholar
  2. 2.
    Meister G., Tuschl T. 2004. Mechanisms of gene silencing by double-stranded RNA. Nature. 431 (7006), 343–349.CrossRefPubMedGoogle Scholar
  3. 3.
    Rajasekaran S., Pattarayan D., Rajaguru P., Sudhakar Gandhi P.S., Thimmulappa R.K. 2016. MicroRNA regulation of acute lung injury and acute respiratory distress syndrome. J. Cell. Physiol. 231 (10), 2097.CrossRefPubMedGoogle Scholar
  4. 4.
    Vasudevan S., Tong Y., Steitz J.A. 2008. Switching from repression to activation: MicroRNAs can up-regulate translation. Science. 318 (5858), 1931–1934.CrossRefGoogle Scholar
  5. 5.
    Meola N., Gennarino V.A., Banfi S. 2009. MicroRNAs and genetic diseases. Pathogenetics. 2 (1), 7.CrossRefPubMedPubMedCentralGoogle Scholar
  6. 6.
    Esquela-Kerscher A., Slack F.J. 2006. Oncomirs— microRNAs with a role in cancer. Nat. Rev. Cancer. 6 (4), 259–269.CrossRefPubMedGoogle Scholar
  7. 7.
    Lee R.C., Feinbaum R.L., Ambros V. 1993. The C. elegans heterochronic gene lin-4 encodes small RNAs with antisense complementarity to lin-14. Cell. 75 (5), 843.CrossRefPubMedGoogle Scholar
  8. 8.
    Wightman B., Ha I., Ruvkun G. 1993. Posttranscriptional regulation of the heterochronic gene lin-14 by lin-4 mediates temporal pattern formation in C. elegans. Cell. 75 (5), 855.CrossRefPubMedGoogle Scholar
  9. 9.
    Jiang Q., Wang Y., Hao Y., Juan L., Teng M., Zhang X. Zhang X, Li M., Wang G., Liu Y. 2009. miR2Disease: A manually curated database for microRNA deregulation in human disease. Nucleic Acids Res. 37 (1), D98–104.CrossRefPubMedGoogle Scholar
  10. 10.
    Li Y., Qiu C., Tu J., Geng B., Yang J., Jiang T., Cui Q. 2014. HMDD v. 2.0: A database for experimentally supported human microRNA and disease associations. Nucleic Acids Res. 42 (Database issue), D1070.CrossRefPubMedGoogle Scholar
  11. 11.
    Yang Z., Ren F., Liu C., He S., Sun G., Gao Q. 2010. DBDEMC: A database of differentially expressed miRNAs in human cancers. BMC Genomics. 11 (4), S5.CrossRefPubMedPubMedCentralGoogle Scholar
  12. 12.
    Yang H., Dinney C.P., Ye Y., Zhu Y., Grossman H.B., Wu X. 2008. Evaluation of genetic variants in microRNA-related genes and risk of bladder cancer. Cancer. Res. 68 (7), 2530.CrossRefPubMedGoogle Scholar
  13. 13.
    Cheng A.M., Byrom M.W., Shelton J., Ford L.P. 2005. Antisense inhibition of human miRNAs and indications for an involvement of miRNA in cell growth and apoptosis. Nucleic Acids Res. 33 (4), 1290–1297.CrossRefPubMedPubMedCentralGoogle Scholar
  14. 14.
    Cui Q., Yu Z., Purisima E.O., Wang E. 2006. Principles of microRNA regulation of a human cellular signaling network. Mol. Syst. Biol. 2 (1), 46.CrossRefPubMedPubMedCentralGoogle Scholar
  15. 15.
    Karp X., Ambros V. 2005. Encountering microRNAs in cell fate signaling. Science. 310 (5752), 1288–1289.CrossRefPubMedGoogle Scholar
  16. 16.
    Miska E.A. 2005. How microRNAs control cell division differentiation and death. Curr. Opin. Genet. Dev. 15 (5), 563.CrossRefPubMedGoogle Scholar
  17. 17.
    Xu P., Guo M., Hay B.A. 2004. MicroRNAs and the regulation of cell death. Trends Genet. 20 (12), 617.CrossRefPubMedGoogle Scholar
  18. 18.
    Bandyopadhyay S., Mitra R., Maulik U., Zhang M.Q. 2010. Development of the human cancer microRNA network. Silence. 1 (1), 6.CrossRefPubMedPubMedCentralGoogle Scholar
  19. 19.
    Gutiérrez N.C., Sarasquete M.E., Misiewiczkrzeminska I., Delgado M., De L.R.J., Ticona F.V. 2010. Deregulation of microRNA expression in the different genetic subtypes of multiple myeloma and correlation with gene expression profiling. Leukemia. 24 (3), 629.CrossRefPubMedGoogle Scholar
  20. 20.
    Lu J., Getz G., Miska E.A., Alvarezsaavedra E., Lamb J., Peck D. 2005. MicroRNA expression profiles classify human cancers. Nature. 435 (7043), 834–838.CrossRefPubMedGoogle Scholar
  21. 21.
    Gaur A., Jewell D.A., Liang Y., Ridzon D., Moore J.H., Chen C. 2007. Characterization of microRNA expression levels and their biological correlates in human cancer cell lines. Cancer. Res. 67 (6), 2456.CrossRefPubMedGoogle Scholar
  22. 22.
    Várallyay E., Burgyán J., Havelda Z. 2008. MicroRNA detection by northern blotting using locked nucleic acid probes. Nat. Protoc. 3 (2), 190.CrossRefPubMedGoogle Scholar
  23. 23.
    Barad O., Meiri E., Avniel A., Aharonov R., Barzilai A., Bentwich I. 2004. MicroRNA expression detected by oligonucleotide microarrays: System establishment and expression profiling in human tissues. Genome Res. 14 (12), 2486–2494.CrossRefPubMedPubMedCentralGoogle Scholar
  24. 24.
    Chen Y., Gelfond J.A., Mcmanus L.M., Shireman P.K. 2009. Reproducibility of quantitative RT-PCR array in miRNA expression profiling and comparison with microarray analysis. BMC Genomics. 10 (1), 407.CrossRefPubMedPubMedCentralGoogle Scholar
  25. 25.
    Saba R., Booth S.A. 2006. Target labelling for the detection and profiling of microRNAs expressed in CNS tissue using microarrays. BMC Biotechnol. 6 (1), 47.CrossRefPubMedPubMedCentralGoogle Scholar
  26. 26.
    Jiang Q., Hao Y., Wang G. 2010. Prioritization of disease microRNAs through a human phenome-microRNAome network. BMC Syst. Biol. 4 (1), S2.CrossRefPubMedPubMedCentralGoogle Scholar
  27. 27.
    Shi H., Xu J., Zhang G., Xu L., Li C., Wang L. 2013. Walking the interactome to identify human miRNA-disease associations through the functional link between miRNA targets and disease genes. BMC Syst. Biol. 7 (1), 1–12.CrossRefGoogle Scholar
  28. 28.
    Xu C., Ping Y., Li X., Zhao H., Wang L., Fan H. 2014. Prioritizing candidate disease miRNAs by integrating phenotype associations of multiple diseases with matched miRNA and mRNA expression profiles. Mol. Biosyst. 10 (11), 2800–2809.CrossRefPubMedGoogle Scholar
  29. 29.
    Wu X., Jiang R., Zhang M.Q., Li S. 2008. Network-based global inference of human disease genes. Mol. Syst. Biol. 4 (1), 189.CrossRefPubMedPubMedCentralGoogle Scholar
  30. 30.
    Xu J., Li C.X., Lv J.Y., Li Y.S, .Xiao Y., Shao T.T. 2011. Prioritizing candidate disease miRNAs by topological features in the miRNA target-dysregulated network: Case study of prostate cancer. Mol. Cancer. Ther. 10 (10), 1857.CrossRefPubMedGoogle Scholar
  31. 31.
    Chen X., Liu M.X., Yan G.Y. 2012. RWRMDA: Predicting novel human microRNA–disease associations. Mol. Biosyst. 8 (10), 2792.CrossRefPubMedGoogle Scholar
  32. 32.
    Xuan P., Han K., Guo M., Guo Y., Li J., Ding J. 2013. Prediction of microRNAs associated with human diseases based on weighted k most similar neighbors. PLoS One. 8 (8), e70204.CrossRefPubMedPubMedCentralGoogle Scholar
  33. 33.
    Wang D., Wang J., Lu M. 2010. Inferring the human microRNA functional similarity and functional network based on microRNA-associated diseases. Bioinformatics. 26 (13), 1644–1650.CrossRefPubMedGoogle Scholar
  34. 34.
    Chen X., Yan G.Y. 2014. Semi-supervised learning for potential human microRNA–disease associations inference. Sci. Rep. 4, 5501.CrossRefPubMedPubMedCentralGoogle Scholar
  35. 35.
    Le D.H., Kwon Y.K. 2013. Neighbor-favoring weight reinforcement to improve random walk-based disease gene prioritization. Comput. Biol. Chem. 44 (2), 1.CrossRefPubMedGoogle Scholar
  36. 36.
    Chen X., Yan C.C., Zhang X., You Z.H., Deng L., Liu Y. 2016. WBSMDA: Within and between score for miRNA–disease association prediction. Sci. Rep. 6, 21 106.CrossRefGoogle Scholar
  37. 37.
    Chen X., Yan C.C., Zhang X., You Z.H., Huang Y.A., Yan G.Y. 2016. HGIMDA: Heterogeneous graph inference for miRNA-disease association prediction. Oncotarget. 7 (40), 65 257–65 269.Google Scholar
  38. 38.
    Sun D., Li A., Feng H., Wang M. 2016. NTSMDA: Prediction of miRNA–disease associations by integrating network topological similarity. Mol. Biosyst. 12 (7), 2224.CrossRefPubMedGoogle Scholar
  39. 39.
    Wang D., Wang J., Lu M., Song F., Cui Q. 2010. Inferring the human microRNA functional similarity and functional network based on microRNA-associated diseases. Bioinformatics. 26 (13), 1644–1650.CrossRefPubMedGoogle Scholar
  40. 40.
    Pesquita C., Faria D., Falcão A.O., Lord P., Couto F.M. 2009. Semantic similarity in biomedical ontologies. PLoS Comput. Biol. 5 (7), e1000443.CrossRefPubMedPubMedCentralGoogle Scholar
  41. 41.
    Xu T., Gu J., Zhou Y., Du L.F. 2009. Improving detection of differentially expressed gene sets by applying cluster enrichment analysis to gene ontology. BMC Bioinformatics. 10 (1), 240.CrossRefPubMedPubMedCentralGoogle Scholar
  42. 42.
    Driel M.A.V., Bruggeman J., Vriend G., Han G.B., Leunissen J.A.M. 2006. A text-mining analysis of the human phenome. Eur. J. Hum.Genet. 14 (5), 535.CrossRefPubMedGoogle Scholar
  43. 43.
    Lu M., Zhang Q., Deng M., Miao J., Guo Y., Gao W. 2008. An analysis of human microRNA and disease associations. PLoS One. 3 (10), e3420.CrossRefPubMedPubMedCentralGoogle Scholar
  44. 44.
    Keshava Prasad T.S., Goel R., Kandasamy K., Keerthikumar S., Kumar S., Mathivanan S., Telikicherla D., Raju R., Shafreen B., Venugopal A., Balakrishnan L., Marimuthu A., Banerjee S., Somanathan D.S., Sebastian A., et al. 2009. Human protein reference database – 2009 update. Nucleic Acids Res. 37 (Database issue), 767–772.CrossRefGoogle Scholar
  45. 45.
    John B., Enright A.J., Aravin A., Tuschl T., Sander C., Marks D.S. 2004. Human microRNA targets. PLoS Biol. 2 (11), e363.CrossRefPubMedPubMedCentralGoogle Scholar
  46. 46.
    Stoffel M. 2005. Combinatorial microRNA target predictions. Nat. Genet. 37 (5), 495–500.CrossRefPubMedGoogle Scholar
  47. 47.
    Griffiths-Jones S., Saini H.K., Van D.S., Enright A.J. 2008. Mirbase: Tools for microRNA genomics. Nucleic Acids Res. 36 (Database issue), D154.CrossRefPubMedGoogle Scholar
  48. 48.
    Krüger J., Rehmsmeier M. 2006. RNAhybrid: MicroRNA target prediction easy, fast and flexible. Nucleic Acids Res. 34 (Web Server issue), 451–454.Google Scholar
  49. 49.
    Maragkakis M., Reczko M., Simossis V.A., Alexiou P., Papadopoulos G.L., Dalamagas T. 2009. DIANA-microT web server: Elucidating microRNA functions through target prediction. Nucleic Acids Res. 37 (Web Server issue), 273–276.Google Scholar
  50. 50.
    Miranda K.C., Huynh T., Tay Y., Ang Y.S., Tam W.L., Thomson A.M. Lim B., Rigoutsos I. 2006. A pattern-based method for the identification of microRNA binding sites and their corresponding heteroduplexes. Cell. 126 (6), 1203–1217.CrossRefPubMedGoogle Scholar
  51. 51.
    Li X., Jiang W., Li W., Lian B., Wang S., Liao M. 2012. Dissection of human miRNA regulatory influence to subpathway. Brief. Bioinform. 13 (2), 175–186.CrossRefPubMedGoogle Scholar
  52. 52.
    Tang F., Barbacioru C., Bao S., Lee C., Nordman E., Wang X. 2010. Tracing the derivation of embryonic stem cells from the inner cell mass by single-cell RNA-seq analysis. Cell Stem Cell. 6 (5), 468.CrossRefPubMedPubMedCentralGoogle Scholar
  53. 53.
    Hamosh A., Scott A.F., Amberger J.S., Bocchini C.A., McKusick V.A. 2005. Online Mendelian Inheritance in Man (OMIM), a knowledge base of human genes and genetic disorders. Nucleic Acids Res. 33 (Suppl. 1), D514–D517.CrossRefPubMedGoogle Scholar
  54. 54.
    Li Y., Patra J.C. 2010. Integration of multiple data sources to prioritize candidate genes using discounted rating system. BMC Bioinformatics. 11 (S1), S20.CrossRefPubMedPubMedCentralGoogle Scholar
  55. 55.
    Li Y., Li J. 2012. Disease gene identification by random walk on multigraphs merging heterogeneous genomic and phenotype data. BMC Genomics. 13 (7), 1–12.CrossRefGoogle Scholar
  56. 56.
    Sun J., Zhou M., Yang H., Deng J., Wang L., Wang Q. 2013. Inferring potential microRNA–microRNA associations based on targeting propensity and connectivity in the context of protein interaction network. PLoS One. 8 (7), e69719.CrossRefPubMedPubMedCentralGoogle Scholar
  57. 57.
    Lv S., Li Y., Wang Q., Ning S., Huang T., Wang P. 2012. A novel method to quantify gene set functional association based on gene ontology. J. R. Soc. Interface. 9 (70), 1063.CrossRefPubMedGoogle Scholar
  58. 58.
    Köhler S., Bauer S., Horn D., Robinson P.N. 2008. Walking the interactome for prioritization of candidate disease genes. Am. J. Hum. Genet. 82 (4), 949–958.CrossRefPubMedPubMedCentralGoogle Scholar
  59. 59.
    Charles B., Saurabh S. 2016. Characterizing gene sets using discriminative random walks with restart on heterogeneous biological networks. Bioinformatics. 32 (14), 2167.CrossRefGoogle Scholar
  60. 60.
    Jiang R., Gan M., He P. 2011. Constructing a gene semantic similarity network for the inference of disease genes. BMC Syst. Biol. 5 (Suppl. 2), S2.CrossRefPubMedPubMedCentralGoogle Scholar
  61. 61.
    Macropol K., Can T., Singh A.K. 2009. RRW: Repeated random walks on genome-scale protein networks for local cluster discovery. BMC Bioinformatics. 10 (1), 283.CrossRefPubMedPubMedCentralGoogle Scholar
  62. 62.
    Mei Q., Zhang H., Liang C. 2016. A discriminative feature extraction approach for tumor classification using gene expression data. Curr. Bioinform. 11 (5), 561–570.CrossRefGoogle Scholar
  63. 63.
    Jemal A., Bray F., Center M.M., Ferlay J., Ward E., Forman D. 2011. Global cancer statistics. CA Cancer. J. Clin. 61 (2), 69.CrossRefPubMedGoogle Scholar
  64. 64.
    Drusco A., Nuovo G.J., Zanesi N., Di L.G., Pichiorri F., Volinia S. 2014. MicroRNA profiles discriminate among colon cancer metastasis. PLoS One. 9 (6), e96670.CrossRefPubMedPubMedCentralGoogle Scholar
  65. 65.
    Parkin D.M., Bray F., Ferlay J., Pisani P. 2005. Global cancer statistics 2002. CA Cancer. J. Clin. 55 (2), 74.CrossRefPubMedGoogle Scholar
  66. 66.
    Shi B., Sepplorenzino L., Prisco M., Linsley P., Deangelis T., Baserga R. 2007. MicroRNA 145 targets the insulin receptor substrate-1 and inhibits the growth of colon cancer cells. J. Biol. Chem. 282 (45), 32 582.CrossRefGoogle Scholar
  67. 67.
    Guo C., Sah F.J., Beard L., Willson J.K.V., Marko-witz S.D., Guda K. 2008. The non-coding RNAmir-126 suppresses the growth of neoplastic cells by targeting phosphatidylinositol 3-kinase signaling and is frequently lost in colon cancers. Genes Chromosomes Cancer. 47 (11), 939.CrossRefPubMedPubMedCentralGoogle Scholar
  68. 68.
    Volinia S., Calin G.A., Liu C.G., Ambs S., Cimmino A., Petrocca F., Visone R., Iorio M., Roldo C., Ferracin M., Prueitt R.L., Yanaihara N., Lanza G., Scarpa A., Vecchione A., et al. 2006. A microRNA expression signature of human solid tumors defines cancer gene targets. Proc. Natl. Acad. Sci. U. S. A. 103 (7), 2257–2261.CrossRefPubMedPubMedCentralGoogle Scholar
  69. 69.
    Tsang W.P., Kwok T.T. 2009. The mir-18a* microRNA functions as a potential tumor suppressor by targeting on K-Ras. Carcinogenesis. 30 (6), 953–959.CrossRefPubMedGoogle Scholar
  70. 70.
    Asangani I.A., Rasheed S.A.K., Nikolova D.A., Leupold J.H., Colburn N.H., Post S., Allgayer H. 2008. MicroRNA-21 (mir-21) post-transcriptionally downregulates tumor suppressor Pdcd4 and stimulates invasion, intravasation and metastasis in colorectal cancer. Oncogene. 27 (15), 2128–2136.CrossRefPubMedGoogle Scholar
  71. 71.
    Schetter A.J., Leung S.Y., Sohn J.J., Zanetti K.A., Bowman E.D., Yanaihara N., Yuen S.T., Chan T.L., Kwong D.L., Au G.K., Liu C.G., Calin G.A., Croce C.M., Harris C.C. 2008. MicroRNA expression profiles associated with prognosis and therapeutic outcome in colon adenocarcinoma. J. Am. Med. Assoc. 299 (4), 425–436.Google Scholar
  72. 72.
    Arndt G.M., Dossey L., Cullen L.M., Lai A., Druker R., Eisbacher M., Zhang C., Tran N., Fan H., Retzlaff K., Bittner A., Raponi M. 2009. Characterization of global microRNA expression reveals oncogenic potential of mir-145 in metastatic colorectal cancer. BMC Cancer. 9 (1), 374.CrossRefPubMedPubMedCentralGoogle Scholar

Copyright information

© Pleiades Publishing, Inc. 2018

Authors and Affiliations

  1. 1.School of Information Science and Engineering, Shandong Normal UniversityJinanChina

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