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MicroRNA Target Identification pp 183–209Cite as

Computational Methods to Investigate the Impact of miRNAs on Pathways

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Part of the book series: Methods in Molecular Biology ((MIMB,volume 1970))

Abstract

Pathway analysis is a wide class of methods allowing to determine the alteration of functional processes in complex diseases. However, biological pathways are still partial, and knowledge coming from posttranscriptional regulators has started to be considered in a systematic way only recently. Here we will give a global and updated view of the main pathway and subpathway analysis methodologies, focusing on the improvements obtained through the recent introduction of microRNAs as regulatory elements in these frameworks.

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References

  1. Aronson SJ, Rehm HL (2015) Building the foundation for genomics in precision medicine. Nature 526:336–342. https://doi.org/10.1038/nature15816

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  2. Lovat F, Fassan M, Gasparini P et al (2015) miR-15b/16-2 deletion promotes B-cell malignancies. Proc Natl Acad Sci U S A 112:11636–11641. https://doi.org/10.1073/pnas.1514954112

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  3. Balatti V, Pekarky Y, Rizzotto L, Croce CM (2013) miR deregulation in CLL. Adv Exp Med Biol 792:309–325. https://doi.org/10.1007/978-1-4614-8051-8_14

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  4. Balatti V, Nigita G, Veneziano D et al (2017) tsRNA signatures in cancer. Proc Natl Acad Sci U S A 114:8071–8076. https://doi.org/10.1073/pnas.1706908114

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  5. Balatti V, Pekarsky Y, Croce CM (2017) Role of the tRNA-derived small RNAs in cancer: new potential biomarkers and target for therapy. Adv Cancer Res 135:173–187. https://doi.org/10.1016/bs.acr.2017.06.007

    Article  PubMed  Google Scholar 

  6. Sethi S, Ali S, Sethi S, Sarkar FH (2014) MicroRNAs in personalized cancer therapy. Clin Genet 86:68–73. https://doi.org/10.1111/cge.12362

    Article  CAS  PubMed  Google Scholar 

  7. Saumet A, Mathelier A, Lecellier C-H (2014) The potential of microRNAs in personalized medicine against cancers. Biomed Res Int 2014:642916. https://doi.org/10.1155/2014/642916

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  8. Glazko GV, Emmert-Streib F (2009) Unite and conquer: univariate and multivariate approaches for finding differentially expressed gene sets. Bioinformatics 25:2348–2354. https://doi.org/10.1093/bioinformatics/btp406

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  9. Alaimo S, Giugno R, Acunzo M et al (2016) Post-transcriptional knowledge in pathway analysis increases the accuracy of phenotypes classification. Oncotarget 7:54572–54582. https://doi.org/10.18632/oncotarget.9788

    Article  PubMed  PubMed Central  Google Scholar 

  10. Jin L, Zuo X-Y, Su W-Y et al (2014) Pathway-based analysis tools for complex diseases: a review. Genomics Proteomics Bioinformatics 12:210–220. https://doi.org/10.1016/j.gpb.2014.10.002

    Article  PubMed  PubMed Central  Google Scholar 

  11. Kanehisa M (2000) KEGG: Kyoto encyclopedia of genes and genomes. Nucleic Acids Res 28:27–30. https://doi.org/10.1093/nar/28.1.27

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  12. Joshi-Tope G, Gillespie M, Vastrik I et al (2005) Reactome: a knowledgebase of biological pathways. Nucleic Acids Res 33:D428–D432. https://doi.org/10.1093/nar/gki072

    Article  CAS  PubMed  Google Scholar 

  13. Subramanian A, Tamayo P, Mootha VK et al (2005) Gene set enrichment analysis: a knowledge-based approach for interpreting genome-wide expression profiles. Proc Natl Acad Sci U S A 102:15545–15550. https://doi.org/10.1073/pnas.0506580102

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  14. Vlachos IS, Zagganas K, Paraskevopoulou MD et al (2015) DIANA-miRPath v3.0: deciphering microRNA function with experimental support. Nucleic Acids Res 43:W460–W466. https://doi.org/10.1093/nar/gkv403

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  15. Khatri P, Draghici S, Ostermeier GC, Krawetz SA (2002) Profiling gene expression using Onto-Express. Genomics 79:266–270. https://doi.org/10.1006/geno.2002.6698

    Article  CAS  PubMed  Google Scholar 

  16. Drǎghici S, Khatri P, Martins RP et al (2003) Global functional profiling of gene expression. Genomics 81:98–104. https://doi.org/10.1016/S0888-7543(02)00021-6

    Article  CAS  PubMed  Google Scholar 

  17. Berriz GF, King OD, Bryant B et al (2003) Characterizing gene sets with FuncAssociate. Bioinformatics 19:2502–2504. https://doi.org/10.1093/bioinformatics/btg363

    Article  CAS  PubMed  Google Scholar 

  18. Beissbarth T, Speed TP (2004) GOstat: find statistically overrepresented Gene Ontologies within a group of genes. Bioinformatics 20:1464–1465. https://doi.org/10.1093/bioinformatics/bth088

    Article  CAS  PubMed  Google Scholar 

  19. Castillo-Davis CI, Hartl DL (2003) GeneMerge–post-genomic analysis, data mining, and hypothesis testing. Bioinformatics 19:891–892. https://doi.org/10.1093/bioinformatics/btg114

    Article  CAS  PubMed  Google Scholar 

  20. Martin D, Brun C, Remy E et al (2004) GOToolBox: functional analysis of gene datasets based on Gene Ontology. Genome Biol 5:R101. https://doi.org/10.1186/gb-2004-5-12-r101

    Article  PubMed  PubMed Central  Google Scholar 

  21. Doniger SW, Salomonis N, Dahlquist KD et al (2003) MAPPFinder: using gene ontology and GenMAPP to create a global gene-expression profile from microarray data. Genome Biol 4:R7. https://doi.org/10.1186/gb-2003-4-1-r7

    Article  PubMed  PubMed Central  Google Scholar 

  22. Efron B, Tibshirani R (2007) On testing the significance of sets of genes. Ann Appl Stat 1:107–129. https://doi.org/10.1214/07-aoas101

    Article  Google Scholar 

  23. Hummel M, Meister R, Mansmann U (2008) GlobalANCOVA: exploration and assessment of gene group effects. Bioinformatics 24:78–85. https://doi.org/10.1093/bioinformatics/btm531

    Article  CAS  PubMed  Google Scholar 

  24. Rahnenführer J, Domingues FS, Maydt J, Lengauer T (2004) Calculating the statistical significance of changes in pathway activity from gene expression data. Stat Appl Genet Mol Biol 3:Article16. https://doi.org/10.2202/1544-6115.1055

    Article  PubMed  Google Scholar 

  25. Draghici S, Khatri P, Tarca AL et al (2007) A systems biology approach for pathway level analysis. Genome Res 17:1537–1545. https://doi.org/10.1101/gr.6202607

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  26. Tarca AL, Draghici S, Khatri P et al (2009) A novel signaling pathway impact analysis. Bioinformatics 25:75–82. https://doi.org/10.1093/bioinformatics/btn577

    Article  CAS  PubMed  Google Scholar 

  27. Shojaie A, Michailidis G (2009) Analysis of gene sets based on the underlying regulatory network. J Comput Biol 16:407–426. https://doi.org/10.1089/cmb.2008.0081

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  28. Vaske CJ, Benz SC, Sanborn JZ et al (2010) Inference of patient-specific pathway activities from multi-dimensional cancer genomics data using PARADIGM. Bioinformatics 26:i237–i245. https://doi.org/10.1093/bioinformatics/btq182

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  29. Sedgewick AJ, Benz SC, Rabizadeh S et al (2013) Learning subgroup-specific regulatory interactions and regulator independence with PARADIGM. Bioinformatics 29:i62–i70. https://doi.org/10.1093/bioinformatics/btt229

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  30. Ansari S, Voichita C, Donato M et al (2016) A novel pathway analysis approach based on the unexplained disregulation of genes. Proc IEEE 1–14. doi: https://doi.org/10.1109/jproc.2016.2531000

  31. Calura E, Martini P, Sales G et al (2014) Wiring miRNAs to pathways: a topological approach to integrate miRNA and mRNA expression profiles. Nucleic Acids Res 42:e96. https://doi.org/10.1093/nar/gku354

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  32. Kong SW, Pu WT, Park PJ (2006) A multivariate approach for integrating genome-wide expression data and biological knowledge. Bioinformatics 22:2373–2380. https://doi.org/10.1093/bioinformatics/btl401

    Article  CAS  PubMed  Google Scholar 

  33. Tian L, Greenberg SA, Kong SW et al (2005) Discovering statistically significant pathways in expression profiling studies. Proc Natl Acad Sci U S A 102:13544–13549. https://doi.org/10.1073/pnas.0506577102

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  34. Jiang Z, Gentleman R (2007) Extensions to gene set enrichment. Bioinformatics 23:306–313. https://doi.org/10.1093/bioinformatics/btl599

    Article  CAS  PubMed  Google Scholar 

  35. Lu Y, Liu P-Y, Xiao P, Deng H-W (2005) Hotelling’s T2 multivariate profiling for detecting differential expression in microarrays. Bioinformatics 21:3105–3113. https://doi.org/10.1093/bioinformatics/bti496

    Article  CAS  PubMed  Google Scholar 

  36. Xiong H (2006) Non-linear tests for identifying differentially expressed genes or genetic networks. Bioinformatics 22:919–923. https://doi.org/10.1093/bioinformatics/btl034

    Article  CAS  PubMed  Google Scholar 

  37. Mitrea C, Taghavi Z, Bokanizad B et al (2013) Methods and approaches in the topology-based analysis of biological pathways. Front Physiol 4:278. https://doi.org/10.3389/fphys.2013.00278

    Article  PubMed  PubMed Central  Google Scholar 

  38. Hsu S-D, Lin F-M, Wu W-Y et al (2011) miRTarBase: a database curates experimentally validated microRNA-target interactions. Nucleic Acids Res 39:D163–D169. https://doi.org/10.1093/nar/gkq1107

    Article  CAS  PubMed  Google Scholar 

  39. Xiao F, Zuo Z, Cai G et al (2009) miRecords: an integrated resource for microRNA-target interactions. Nucleic Acids Res 37:D105–D110. https://doi.org/10.1093/nar/gkn851

    Article  CAS  PubMed  Google Scholar 

  40. Li C, Han J, Yao Q et al (2013) Subpathway-GM: identification of metabolic subpathways via joint power of interesting genes and metabolites and their topologies within pathways. Nucleic Acids Res 41:e101. https://doi.org/10.1093/nar/gkt161

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  41. Judeh T, Johnson C, Kumar A, Zhu D (2013) TEAK: topology enrichment analysis framework for detecting activated biological subpathways. Nucleic Acids Res 41:1425–1437. https://doi.org/10.1093/nar/gks1299

    Article  CAS  PubMed  Google Scholar 

  42. Martini P, Sales G, Massa MS et al (2013) Along signal paths: an empirical gene set approach exploiting pathway topology. Nucleic Acids Res 41:e19. https://doi.org/10.1093/nar/gks866

    Article  CAS  PubMed  Google Scholar 

  43. Nam S, Chang HR, Kim K-T et al (2014) PATHOME: an algorithm for accurately detecting differentially expressed subpathways. Oncogene 33:4941–4951. https://doi.org/10.1038/onc.2014.80

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  44. Vrahatis AG, Balomenos P, Tsakalidis AK, Bezerianos A (2016) DEsubs: an R package for flexible identification of differentially expressed subpathways using RNA-seq experiments. Bioinformatics 32:3844–3846. https://doi.org/10.1093/bioinformatics/btw544

    Article  CAS  PubMed  Google Scholar 

  45. Feng L, Xu Y, Zhang Y et al (2015) Subpathway-GMir: identifying miRNA-mediated metabolic subpathways by integrating condition-specific genes, microRNAs, and pathway topologies. Oncotarget 6:39151–39164. https://doi.org/10.18632/oncotarget.5341

    Article  PubMed  PubMed Central  Google Scholar 

  46. Vrahatis AG, Dimitrakopoulou K, Balomenos P et al (2016) CHRONOS: a time-varying method for microRNA-mediated subpathway enrichment analysis. Bioinformatics 32:884–892. https://doi.org/10.1093/bioinformatics/btv673

    Article  CAS  PubMed  Google Scholar 

  47. Alaimo S, Marceca GP, Ferro A, Pulvirenti A (2017) Detecting disease specific pathway substructures through an integrated systems biology approach. Noncoding RNA 3:E20. https://doi.org/10.3390/ncrna3020020

    Article  CAS  PubMed  Google Scholar 

  48. Cormen TH (2009) Introduction to algorithms. MIT Press, Cambridge

    Google Scholar 

  49. Ashburner M, Ball CA, Blake JA et al (2000) Gene ontology: tool for the unification of biology. The Gene Ontology Consortium. Nat Genet 25:25–29. https://doi.org/10.1038/75556

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  50. McKusick VA (2000) OMIM(TM): online mendelian inheritance in man. Baltimore, Johns Hopkins University

    Google Scholar 

  51. Campbell N (2004) Genetic association database. Nat Rev Genet 5:87–87. https://doi.org/10.1038/nrg1288

    Article  CAS  Google Scholar 

  52. Chou C-H, Shrestha S, Yang C-D et al (2018) miRTarBase update 2018: a resource for experimentally validated microRNA-target interactions. Nucleic Acids Res 46:D296–D302. https://doi.org/10.1093/nar/gkx1067

    Article  CAS  PubMed  Google Scholar 

  53. Inui M, Martello G, Piccolo S (2010) MicroRNA control of signal transduction. Nat Rev Mol Cell Biol 11:252–263. https://doi.org/10.1038/nrm2868

    Article  CAS  PubMed  Google Scholar 

  54. Jiang Q, Wang Y, Hao Y et al (2009) miR2Disease: a manually curated database for microRNA deregulation in human disease. Nucleic Acids Res 37:D98–D104. https://doi.org/10.1093/nar/gkn714

    Article  CAS  PubMed  Google Scholar 

  55. Vergoulis T, Vlachos IS, Alexiou P et al (2011) TarBase 6.0: capturing the exponential growth of miRNA targets with experimental support. Nucleic Acids Res 40:D222–D229. https://doi.org/10.1093/nar/gkr1161

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  56. Poole W, Gibbs DL, Shmulevich I et al (2015) Combining dependent P-values with an empirical adaptation of Brown’s method. Bioinformatics 32(17):i430–i436

    Article  Google Scholar 

  57. Brown MB (1975) 400: a method for combining non-independent, one-sided tests of significance. Biometrics 31:987. https://doi.org/10.2307/2529826

    Article  Google Scholar 

  58. Friedman AA, Letai A, Fisher DE, Flaherty KT (2015) Precision medicine for cancer with next-generation functional diagnostics. Nat Rev Cancer 15:747–756. https://doi.org/10.1038/nrc4015

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  59. Fodor A, Karnieli E (2016) Challenges of implementing personalized (precision) medicine: a focus on diabetes. Per Med 13:485–497. https://doi.org/10.2217/pme-2016-0022

    Article  CAS  PubMed  Google Scholar 

  60. Caskey T (2018) Precision medicine: functional advancements. Annu Rev Med 69:1–18. https://doi.org/10.1146/annurev-med-041316-090905

    Article  CAS  PubMed  Google Scholar 

  61. Sager M, Yeat NC, Pajaro-Van der Stadt S et al (2015) Transcriptomics in cancer diagnostics: developments in technology, clinical research and commercialization. Expert Rev Mol Diagn 15:1589–1603. https://doi.org/10.1586/14737159.2015.1105133

    Article  CAS  PubMed  Google Scholar 

  62. Zhang X, Marjani SL, Hu Z et al (2016) Single-cell sequencing for precise cancer research: progress and prospects. Cancer Res 76:1305–1312. https://doi.org/10.1158/0008-5472.CAN-15-1907

    Article  CAS  PubMed  Google Scholar 

  63. Melnekoff D, Lagana A, Hamou W et al (2017) Single-cell RNA sequencing reveals distinct transcriptomic profiles of multiple myeloma with implications for personalized medicine. Blood 130:62–62

    Google Scholar 

  64. Avci CB, Baran Y (2014) Use of microRNAs in personalized medicine. Methods Mol Biol 1107:311–325. https://doi.org/10.1007/978-1-62703-748-8_19

    Article  CAS  PubMed  Google Scholar 

  65. Laganà A, Acunzo M, Romano G et al (2014) miR-Synth: a computational resource for the design of multi-site multi-target synthetic miRNAs. Nucleic Acids Res 42:5416–5425. https://doi.org/10.1093/nar/gku202

    Article  CAS  PubMed  PubMed Central  Google Scholar 

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Acknowledgments

This work has been done within the research project “Marcatori molecolari e clinico-strumentali precoci, nelle patologie metaboliche e cronico-degenerative,” funded by the Department of Clinical and Experimental of University of Catania.

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Authors and Affiliations

  1. Department of Clinical and Experimental Medicine, University of Catania, Catania, Italy

    Salvatore Alaimo, Giovanni Micale, Alfredo Ferro & Alfredo Pulvirenti

  2. Department of Physics and Astronomy, University of Catania, Catania, Italy

    Alessandro La Ferlita

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  1. Salvatore Alaimo
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  2. Giovanni Micale
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  3. Alessandro La Ferlita
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  4. Alfredo Ferro
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  5. Alfredo Pulvirenti
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Corresponding author

Correspondence to Alfredo Pulvirenti .

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Editors and Affiliations

  1. Department of Genetics and Genomic Sciences, Mount Sinai School of Medicine, New York, NY, USA

    Alessandro Laganà

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Alaimo, S., Micale, G., La Ferlita, A., Ferro, A., Pulvirenti, A. (2019). Computational Methods to Investigate the Impact of miRNAs on Pathways. In: Laganà, A. (eds) MicroRNA Target Identification. Methods in Molecular Biology, vol 1970. Humana Press, New York, NY. https://doi.org/10.1007/978-1-4939-9207-2_11

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  • DOI: https://doi.org/10.1007/978-1-4939-9207-2_11

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