IDEAS home Printed from https://ideas.repec.org/a/plo/pone00/0148521.html
   My bibliography  Save this article

Integration of Multiple Genomic and Phenotype Data to Infer Novel miRNA-Disease Associations

Author

Listed:
  • Hongbo Shi
  • Guangde Zhang
  • Meng Zhou
  • Liang Cheng
  • Haixiu Yang
  • Jing Wang
  • Jie Sun
  • Zhenzhen Wang

Abstract

MicroRNAs (miRNAs) play an important role in the development and progression of human diseases. The identification of disease-associated miRNAs will be helpful for understanding the molecular mechanisms of diseases at the post-transcriptional level. Based on different types of genomic data sources, computational methods for miRNA-disease association prediction have been proposed. However, individual source of genomic data tends to be incomplete and noisy; therefore, the integration of various types of genomic data for inferring reliable miRNA-disease associations is urgently needed. In this study, we present a computational framework, CHNmiRD, for identifying miRNA-disease associations by integrating multiple genomic and phenotype data, including protein-protein interaction data, gene ontology data, experimentally verified miRNA-target relationships, disease phenotype information and known miRNA-disease connections. The performance of CHNmiRD was evaluated by experimentally verified miRNA-disease associations, which achieved an area under the ROC curve (AUC) of 0.834 for 5-fold cross-validation. In particular, CHNmiRD displayed excellent performance for diseases without any known related miRNAs. The results of case studies for three human diseases (glioblastoma, myocardial infarction and type 1 diabetes) showed that all of the top 10 ranked miRNAs having no known associations with these three diseases in existing miRNA-disease databases were directly or indirectly confirmed by our latest literature mining. All these results demonstrated the reliability and efficiency of CHNmiRD, and it is anticipated that CHNmiRD will serve as a powerful bioinformatics method for mining novel disease-related miRNAs and providing a new perspective into molecular mechanisms underlying human diseases at the post-transcriptional level. CHNmiRD is freely available at http://www.bio-bigdata.com/CHNmiRD.

Suggested Citation

  • Hongbo Shi & Guangde Zhang & Meng Zhou & Liang Cheng & Haixiu Yang & Jing Wang & Jie Sun & Zhenzhen Wang, 2016. "Integration of Multiple Genomic and Phenotype Data to Infer Novel miRNA-Disease Associations," PLOS ONE, Public Library of Science, vol. 11(2), pages 1-15, February.
    • Handle: RePEc:plo:pone00:0148521
    DOI: 10.1371/journal.pone.0148521
    as

    Download full text from publisher

    File URL: https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0148521
    Download Restriction: no
    File URL: https://journals.plos.org/plosone/article/file?id=10.1371/journal.pone.0148521&type=printable
    Download Restriction: no
    File URL: https://libkey.io/10.1371/journal.pone.0148521?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    References listed on IDEAS

    as
    1. Eric M. Small & Eric N. Olson, 2011. "Pervasive roles of microRNAs in cardiovascular biology," Nature, Nature, vol. 469(7330), pages 336-342, January.
    2. Harriet E. Gee & Carme Camps & Francesca M. Buffa & Stefano Colella & Helen Sheldon & Jonathan M. Gleadle & Jiannis Ragoussis & Adrian L. Harris, 2008. "MicroRNA-10b and breast cancer metastasis," Nature, Nature, vol. 455(7216), pages 8-9, October.
    3. Victor Ambros, 2004. "The functions of animal microRNAs," Nature, Nature, vol. 431(7006), pages 350-355, September.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. José María Galván-Román & Ángel Lancho-Sánchez & Sergio Luquero-Bueno & Lorena Vega-Piris & Jose Curbelo & Marcos Manzaneque-Pradales & Manuel Gómez & Hortensia de la Fuente & Mara Ortega-Gómez & Javi, 2020. "Usefulness of circulating microRNAs miR-146a and miR-16-5p as prognostic biomarkers in community-acquired pneumonia," PLOS ONE, Public Library of Science, vol. 15(10), pages 1-13, October.
    2. Kshitij Srivastava & Anvesha Srivastava, 2012. "Comprehensive Review of Genetic Association Studies and Meta-Analyses on miRNA Polymorphisms and Cancer Risk," PLOS ONE, Public Library of Science, vol. 7(11), pages 1-1, November.
    3. Xing Chen & Jun Yin & Jia Qu & Li Huang, 2018. "MDHGI: Matrix Decomposition and Heterogeneous Graph Inference for miRNA-disease association prediction," PLOS Computational Biology, Public Library of Science, vol. 14(8), pages 1-24, August.
    4. Yanyan Wang & Yujie Zhang & Chi Pan & Feixia Ma & Suzhan Zhang, 2015. "Prediction of Poor Prognosis in Breast Cancer Patients Based on MicroRNA-21 Expression: A Meta-Analysis," PLOS ONE, Public Library of Science, vol. 10(2), pages 1-13, February.
    5. Thierry Chekouo & Francesco C. Stingo & James D. Doecke & Kim-Anh Do, 2015. "miRNA–target gene regulatory networks: A Bayesian integrative approach to biomarker selection with application to kidney cancer," Biometrics, The International Biometric Society, vol. 71(2), pages 428-438, June.
    6. Xing Chen & Li Huang, 2017. "LRSSLMDA: Laplacian Regularized Sparse Subspace Learning for MiRNA-Disease Association prediction," PLOS Computational Biology, Public Library of Science, vol. 13(12), pages 1-28, December.
    7. Ang Li & Yingwei Deng & Yan Tan & Min Chen, 2021. "A novel miRNA-disease association prediction model using dual random walk with restart and space projection federated method," PLOS ONE, Public Library of Science, vol. 16(6), pages 1-17, June.
    8. Charlotte Glinge & Sebastian Clauss & Kim Boddum & Reza Jabbari & Javad Jabbari & Bjarke Risgaard & Philipp Tomsits & Bianca Hildebrand & Stefan Kääb & Reza Wakili & Thomas Jespersen & Jacob Tfelt-Han, 2017. "Stability of Circulating Blood-Based MicroRNAs – Pre-Analytic Methodological Considerations," PLOS ONE, Public Library of Science, vol. 12(2), pages 1-16, February.
    9. Andreas B Gevaert & Isabel Witvrouwen & Christiaan J Vrints & Hein Heidbuchel & Emeline M Van Craenenbroeck & Steven J Van Laere & Amaryllis H Van Craenenbroeck, 2018. "MicroRNA profiling in plasma samples using qPCR arrays: Recommendations for correct analysis and interpretation," PLOS ONE, Public Library of Science, vol. 13(2), pages 1-13, February.
    10. Alexander Link & Verena Becker & Ajay Goel & Thomas Wex & Peter Malfertheiner, 2012. "Feasibility of Fecal MicroRNAs as Novel Biomarkers for Pancreatic Cancer," PLOS ONE, Public Library of Science, vol. 7(8), pages 1-9, August.
    11. Hossain Ahmed & Beyene Joseph, 2013. "Estimation of weighted log partial area under the ROC curve and its application to MicroRNA expression data," Statistical Applications in Genetics and Molecular Biology, De Gruyter, vol. 12(6), pages 743-755, December.
    12. Hai Lian & Lei Wang & Jingmin Zhang, 2012. "Increased Risk of Breast Cancer Associated with CC Genotype of Has-miR-146a Rs2910164 Polymorphism in Europeans," PLOS ONE, Public Library of Science, vol. 7(2), pages 1-7, February.
    13. Fabricio F Costa & Jared M Bischof & Elio F Vanin & Rishi R Lulla & Min Wang & Simone T Sredni & Veena Rajaram & Maria de Fátima Bonaldo & Deli Wang & Stewart Goldman & Tadanori Tomita & Marcelo B Soa, 2011. "Identification of MicroRNAs as Potential Prognostic Markers in Ependymoma," PLOS ONE, Public Library of Science, vol. 6(10), pages 1-10, October.
    14. Le Thi Truc Linh, 2018. "The Microrna 29 family and its regulation," HO CHI MINH CITY OPEN UNIVERSITY JOURNAL OF SCIENCE - ENGINEERING AND TECHNOLOGY, HO CHI MINH CITY OPEN UNIVERSITY JOURNAL OF SCIENCE, HO CHI MINH CITY OPEN UNIVERSITY, vol. 8(1), pages 18-27.
    15. Seyedehsadaf Asfa & Halil Ibrahim Toy & Reza Arshinchi Bonab & George P. Chrousos & Athanasia Pavlopoulou & Styliani A. Geronikolou, 2023. "Soft Tissue Ewing Sarcoma Cell Drug Resistance Revisited: A Systems Biology Approach," IJERPH, MDPI, vol. 20(13), pages 1-17, July.
    16. Man-Tang Qiu & Jing-Wen Hu & Xiang-Xiang Ding & Xin Yang & Zhi Zhang & Rong Yin & Lin Xu, 2012. "Hsa-miR-499 rs3746444 Polymorphism Contributes to Cancer Risk: A Meta-Analysis of 12 Studies," PLOS ONE, Public Library of Science, vol. 7(12), pages 1-7, December.
    17. Li Li & Yunjian Sheng & Lin Lv & Jian Gao, 2013. "The Association between Two MicroRNA Variants (miR-499, miR-149) and Gastrointestinal Cancer Risk: A Meta-Analysis," PLOS ONE, Public Library of Science, vol. 8(11), pages 1-1, November.
    18. Julie Massart & Rasmus J. O. Sjögren & Brendan Egan & Christian Garde & Magnus Lindgren & Weifeng Gu & Duarte M. S. Ferreira & Mutsumi Katayama & Jorge L. Ruas & Romain Barrès & Donal J. O’Gorman & Ju, 2021. "Endurance exercise training-responsive miR-19b-3p improves skeletal muscle glucose metabolism," Nature Communications, Nature, vol. 12(1), pages 1-13, December.
    19. Blanca Elena Castro-Magdonel & Manuela Orjuela & Diana E Alvarez-Suarez & Javier Camacho & Lourdes Cabrera-Muñoz & Stanislaw Sadowinski-Pine & Aurora Medina-Sanson & Citlali Lara-Molina & Daphne Garcí, 2020. "Circulating miRNome detection analysis reveals 537 miRNAS in plasma, 625 in extracellular vesicles and a discriminant plasma signature of 19 miRNAs in children with retinoblastoma from which 14 are al," PLOS ONE, Public Library of Science, vol. 15(4), pages 1-19, April.
    20. Adam Emmer, 2019. "The careers behind and the impact of solo author articles in Nature and Science," Scientometrics, Springer;Akadémiai Kiadó, vol. 120(2), pages 825-840, August.

    More about this item

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:plo:pone00:0148521. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: plosone (email available below). General contact details of provider: https://journals.plos.org/plosone/ .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.