Skip to main content
SpringerLink
Log in
Book cover

MicroRNA Cancer Regulation pp 291–315Cite as

Elucidating the Role of microRNAs in Cancer Through Data Mining Techniques

  • Chapter
  • First Online:

Part of the book series: Advances in Experimental Medicine and Biology ((AEMB,volume 774))

Abstract

microRNAs (miRNAs) have been shown to play a crucial role in the most important biological processes and their dysregulation has been connected to a variety of diseases, including cancer. The number of computational tools for the analysis of miRNA related data is continuously increasing. They range from simple look-up resources to more sophisticated tools for functional analysis of miRNAs. These systems may help to investigate the role of miRNAs in key biological processes and their involvement in diseases. The ultimate goal is to allow the development of regulatory models describing complex processes and the effects of their dysregulation.

Here we review the most important and recent methods for the analysis of miRNA expression profiles and the tools available on the web for target prediction and functional analysis of miRNAs.

Particular emphasis is given to the integration of heterogeneous data, including target predictions and expression profiles, which can be used to infer miRNA/phenotype associations and for the generation of network models of miRNA function.

This is a preview of subscription content, log in via an institution.

Chapter
USD   29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD   129.00
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD   169.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info
Hardcover Book
USD   169.99
Price excludes VAT (USA)
  • Durable hardcover edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Learn about institutional subscriptions

References

  1. Jovanovic M, Hengartner MO (2006) miRNAs and apoptosis: RNAs to die for. Oncogene 25:6176–6187

    CAS  PubMed  Google Scholar 

  2. Wienholds E, Plasterk RHA (2005) MicroRNA function in animal development. FEBS Lett 579:5911–5922

    CAS  PubMed  Google Scholar 

  3. Xiao C, Rajewsky K (2009) MicroRNA control in the immune system: basic principles. Cell 136(1):26–36

    CAS  PubMed  Google Scholar 

  4. Small EM, Olson EN (2011) Pervasive roles of microRNAs in cardiovascular biology. Nature 469(7330):336–342

    CAS  PubMed Central  PubMed  Google Scholar 

  5. Lau P, de Strooper B (2010) Dysregulated microRNAs in neurodegenerative disorders. Semin Cell Dev Biol 21(7):768–773

    CAS  PubMed  Google Scholar 

  6. Iorio MV, Croce CM (2009) MicroRNAs in cancer: small molecules with a huge impact. J Clin Oncol 27(34):5848–5856

    CAS  PubMed  Google Scholar 

  7. Bartel DP (2009) MicroRNAs: target recognition and regulatory functions. Cell 136(2):215–233

    CAS  PubMed Central  PubMed  Google Scholar 

  8. Didiano D, Hobert O (2006) Perfect seed pairing is not a generally reliable predictor for miRNA-target interactions. Nat Struct Mol Biol 13(9):849–851

    CAS  PubMed  Google Scholar 

  9. Didiano D, Hobert O (2008) Molecular architecture of a miRNA-regulated 3′ UTR. RNA 14(7):1297–1317

    CAS  PubMed  Google Scholar 

  10. Yin JQ, Zhao RC, Morris KV (2008) Profiling microRNA expression with microarrays. Trends Biotechnol 26:70–76

    CAS  PubMed  Google Scholar 

  11. Chen C, Ridzon DA, Broomer AJ, Zhou Z, Lee DH, Nguyen JT, Barbisin M, Xu NL, Mahuvakar VR, Andersen MR et al (2005) Real-time quantification of microRNAs by stem–loop RT-PCR. Nucleic Acids Res 33:e179. doi:10.1093/nar/gni178

    PubMed Central  PubMed  Google Scholar 

  12. Shi R, Chiang VL (2005) Facile means for quantifying microRNA expression by real-time PCR. Biotechniques 39:519–525

    CAS  PubMed  Google Scholar 

  13. Hafner M, Landgraf P, Ludwig J, Rice A, Ojo T, Lin C, Holoch D, Lim C, Tuschl T (2008) Identification of microRNAs and other small regulatory RNAs using cDNA library sequencing. Methods 44:3–12

    CAS  PubMed Central  PubMed  Google Scholar 

  14. Krichevsky AM, King KS, Donahue CP, Khrapko K, Kosik KS (2003) A microRNA array reveals extensive regulation of microRNAs during brain development. RNA 9:1274–1281

    CAS  PubMed  Google Scholar 

  15. Liu CG, Calin GA, Meloon B, Gamliel N, Sevignani C, Ferracin M, Dumitru CD, Shimizu M, Zupo S, Dono M, Alder H, Bullrich F, Negrini M, Croce CM (2004) An oligonucleotide microchip for genome-wide microRNA profiling in human and mouse tissues. Proc Natl Acad Sci USA 101:9740–9744

    CAS  PubMed  Google Scholar 

  16. Zhao JJ, Hua YJ, Sun DG, Meng XX, Xiao HS, Ma X (2006) Genome-wide microRNA profiling in human fetal nervous tissues by oligonucleotide microarray. Childs Nerv Syst 22:1419–1425

    PubMed Central  PubMed  Google Scholar 

  17. Schmittgen TD, Jiang J, Liu Q, Yang L (2004) A high-throughput method to monitor the expression of microRNA precursors. Nucleic Acids Res 32:e43

    PubMed Central  PubMed  Google Scholar 

  18. Chen C, Ridzon DA, Broomer AJ, Zhou Z, Lee DH, Nguyen JT, Barbisin M, Xu NL, Mahuvakar VR, Andersen MR, Lao KQ, Livak KJ, Guegler KJ (2005) Real-time quantification of microRNAs by stem-loop RT-PCR. Nucleic Acids Res 33:e179

    PubMed Central  PubMed  Google Scholar 

  19. Schmittgen TD, Lee EJ, Jiang J, Sarkar A, Yang L, Elton TS, Chen C (2008) Real-time PCR quantification of precursor and mature microRNA. Methods 44:31–38

    CAS  PubMed Central  PubMed  Google Scholar 

  20. Bissels U, Wild S, Tomiuk S, Holste A, Hafner M, Tuschl T, Bosio A (2009) Absolute quantification of microRNAs by using a universal reference. RNA 15:2375–2384

    CAS  PubMed  Google Scholar 

  21. Hall N (2007) Advanced sequencing technologies and their wider impact in microbiology. J Exp Biol 210(Pt 9):1518–1525

    CAS  PubMed  Google Scholar 

  22. Church GM (2006) Genomes for all. Sci Am 294(1):46–54

    CAS  PubMed  Google Scholar 

  23. Zak DE, Aderem A (2009) A systems view of host defense. Nat Biotechnol 27(11):999–1001

    CAS  PubMed Central  PubMed  Google Scholar 

  24. Geiss GK et al (2008) Direct multiplexed measurement of gene expression with color-coded probe pairs. Nat Biotechnol 26:317–325

    CAS  PubMed  Google Scholar 

  25. Kulkarni MM (2011) Digital multiplexed gene expression analysis using the NanoString nCounter system. Curr Protoc Mol Biol Chapter 25:Unit25B.10

  26. Lopez-Romero P, Gonzalez MA, Callejas S, Dopazo A, Irizarry RA (2010) Processing of Agilent microRNA array data. BMC Res Notes 3:18

    PubMed Central  PubMed  Google Scholar 

  27. Hua YJ, Tu K, Tang ZY, Li YX, Xiao HS (2008) Comparison of normalization methods with microRNA microarray. Genomics 92:122–128

    CAS  PubMed  Google Scholar 

  28. Pradervand S, Weber J, Thomas J, Bueno M, Wirapati P, Lefort K, Dotto GP, Harshman K (2009) Impact of normalization on miRNA microarray expression profiling. RNA 15:493–501

    CAS  PubMed  Google Scholar 

  29. Risso D, Massa MS, Chiogna M, Romualdi C (2009) A modified LOESS normalization applied to microRNA arrays: a comparative evaluation. Bioinformatics 25:2685–2691

    CAS  PubMed  Google Scholar 

  30. Sarkar D, Parkin R, Wyman S, Bendoraite A, Sather C, Delrow J, Godwin AK, Drescher C, Huber W, Gentleman R, Tewari M (2009) Quality assessment and data analysis for microRNA expression arrays. Nucleic Acids Res 37:e17

    CAS  PubMed Central  PubMed  Google Scholar 

  31. Peltier HJ, Latham GJ (2008) Normalization of microRNA expression levels in quantitative RTPCR assays: identification of suitable reference RNA targets in normal and cancerous human solid tissues. RNA 14:844–852

    CAS  PubMed  Google Scholar 

  32. Chang KH, Mestdagh P, Vandesompele J, Kerin MJ, Miller N (2010) MicroRNA expression profiling to identify and validate reference genes for relative quantification in colorectal cancer. BMC Cancer 10:173

    CAS  PubMed Central  PubMed  Google Scholar 

  33. Sato F, Tsuchiya S, Terasawa K, Tsujimoto G (2009) Intra-platform repeatability and inter-platform comparability of microRNA microarray technology. PLoS One 4:e5540

    PubMed Central  PubMed  Google Scholar 

  34. Benes V, Castoldi M (2010) Expression profiling of microRNA using real-time quantitative PCR, how to use it and what is available. Methods 50:244–249

    CAS  PubMed  Google Scholar 

  35. Mestdagh P, Van VP, De WA, Muth D, Westermann F, Speleman F, Vandesompele J (2009) A novel and universal method for microRNA RT-qPCR data normalization. Genome Biol 10:R64

    PubMed Central  PubMed  Google Scholar 

  36. Vandesompele J, De PK, Pattyn F, Poppe B, Van RN, De PA, Speleman F (2003) Accurate normalization of real-time quantitative RT-PCR data by geometric averaging of multiple internal control genes. Genome Biol 3:RESEARCH0034

    Google Scholar 

  37. Git A, Dvinge H, Salmon-Divon M, Osborne M, Kutter C, Hadfield J, Bertone P, Caldas C (2010) Systematic comparison of microarray profiling, real-time PCR, and next-generation sequencing technologies for measuring differential microRNA expression. RNA 16:991–1006

    CAS  PubMed  Google Scholar 

  38. Pfaffl MW, Tichopad A, Prgomet C, Neuvians TP (2004) Determination of stable housekeeping genes, differentially regulated target genes and sample integrity: BestKeeper–excel-based tool using pair-wise correlations. Biotechnol Lett 26:509–515

    CAS  PubMed  Google Scholar 

  39. Andersen CL, Jensen JL, Orntoft TF (2004) Normalization of real-time quantitative reverse transcription-PCR data: a model-based variance estimation approach to identify genes suited for normalization, applied to bladder and colon cancer data sets. Cancer Res 64:5245–5250

    CAS  PubMed  Google Scholar 

  40. Wang B, Wang XF, Howell P, Qian X, Huang K, Riker AI, Ju J, Xi Y (2010) A personalized microRNA microarray normalization method using a logistic regression model. Bioinformatics 26:228–234

    PubMed  Google Scholar 

  41. Baskerville S, Bartel DP (2005) Microarray profiling of microRNAs reveals frequent coexpression with neighboring miRNAs and host genes. RNA 11:241–247

    CAS  PubMed  Google Scholar 

  42. Liang RQ, Li W, Li Y, Tan CY, Li JX, Jin YX, Ruan KC (2005) An oligonucleotide microarray for microRNA expression analysis based on labeling RNA with quantum dot and nanogold probe. Nucleic Acids Res 33:e17. doi:10.1093/nar/gni019

    PubMed Central  PubMed  Google Scholar 

  43. Wang H, Ach RA, Curry B (2007) Direct and sensitive miRNA profiling from low-input total RNA. RNA 13:151–159

    CAS  PubMed  Google Scholar 

  44. Rao Y, Lee Y, Jarjoura D, Ruppert AS, Liu CG, Hsu JC, Hagan JP (2008) A comparison of normalization techniques for microRNA microarray data. Stat Appl Genet Mol Biol 7: Article22

    Google Scholar 

  45. Chiogna M, Massa MS, Risso D, Romualdi C (2009) A comparison on effects of normalisations in the detection of differentially expressed genes. BMC Bioinform 10:61

    Google Scholar 

  46. Sun Y, Koo S, White N, Peralta E, Esau C, Dean NM, Perera RJ (2004) Development of a microarray to detect human and mouse microRNAs and characterization of expression in human organs. Nucleic Acids Res 32:e188. doi:10.1093/nar/gnh186

    PubMed Central  PubMed  Google Scholar 

  47. Castoldi M, Schmidt S, Benes V, Hentze MW, Muckenthaler MU (2008) miChip: an array-based method for microRNA expression profiling using locked nucleic acid capture probes. Nat Protoc 3:321–329

    CAS  PubMed  Google Scholar 

  48. Garzon R, Volinia S, Liu CG, Fernandez-Cymering C, Palumbo T, Pichiorri F, Fabbri M, Coombes K, Alder H, Nakamura T, Flomenberg N, Marcucci G, Calin GA, Kornblau SM, Kantarjian H, Bloomfield CD, Andreeff M, Croce CM (2008) MicroRNA signatures associated with cytogenetics and prognosis in acute myeloid leukemia. Blood 111(6):3183–3189

    CAS  PubMed  Google Scholar 

  49. Miska EA, Alvarez-Saavedra E, Townsend M, Yoshii A, Sestan N, Rakic P, Constantine-Paton M, Horvitz HR (2004) Microarray analysis of microRNA expression in the developing mammalian brain. Genome Biol 5:R68. doi:10.1186/gb-2004-5-9-r68

    PubMed Central  PubMed  Google Scholar 

  50. Tian Z, Greene AS, Pietrusz JL, Matus IR, Liang M (2008) MicroRNA-target pairs in the rat kidney identified by microRNA microarray, proteomic, and bioinformatic analysis. Genome Res 18:404–411

    CAS  PubMed  Google Scholar 

  51. Bolstad BM, Irizarry RA, Astrand M, Speed TP (2003) A comparison of normalization methods for high density oligonucleotide array data based on variance and bias. Bioinformatics 19:185–193

    CAS  PubMed  Google Scholar 

  52. Steinhoff C, Vingron M (2006) Normalization and quantification of differential expression in gene expression microarrays. Brief Bioinform 7:166–177

    CAS  PubMed  Google Scholar 

  53. Huber W, von Heydebreck A, Sultmann H, Poustka A, Vingron M (2002) Variance stabilization applied to microarray data calibration and to the quantification of differential expression. Bioinformatics 18(Suppl 1):S96–S104

    PubMed  Google Scholar 

  54. Xiong H, Zhang D, Martyniuk CJ, Trudeau VL, Xia X (2008) Using generalized procrustes analysis (GPA) for normalization of cDNA microarray data. BMC Bioinformatics 9:25

    PubMed Central  PubMed  Google Scholar 

  55. Bargaje R, Hariharan M, Scaria V, Pillai B (2010) Consensus miRNA expression profiles derived from interplatform normalization of microarray data. RNA 16:16–25

    CAS  PubMed  Google Scholar 

  56. Do JH, Choi DK (2006) Normalization of microarray data: single-labeled and dual-labeled arrays. Mol Cells 22:254–261

    CAS  PubMed  Google Scholar 

  57. Garzon R, Garofalo M, Martelli MP, Briesewitz R, Wang L, Fernandez-Cymering C, Volinia S, Liu CG, Schnittger S, Haferlach T, Liso A, Diverio D, Mancini M, Meloni G, Foa R, Martelli MF, Marcucci C, Croce CM, Falini B (2008) Distinctive microRNA signature of acute myeloid leukemia bearing cytoplasmic mutated nucleophosmin. Proc Natl Acad Sci USA 105(10):3945–3950

    CAS  PubMed  Google Scholar 

  58. Perkins DO, Jeffries CD, Jarskog LF, Thomson JM, Woods K, Newman MA, Parker JS, Jin J, Hammond SM (2007) MicroRNA expression in the prefrontal cortex of individuals with schizophrenia and schizoaffective disorder. Genome Biol 8:R27. doi:10.1186/gb-2007-8-2-r27

    PubMed Central  PubMed  Google Scholar 

  59. Chen Y, Dougherty E, Bittner ML (1997) Ratio-based decisions and the quantitative analysis of cDNA microarray images. J Biomed Opt 2:364–374

    CAS  PubMed  Google Scholar 

  60. Tusher VG, Tibshirani R, Chu G (2001) Significance analysis of microarrays applied to the ionizing radiation response. Proc Natl Acad Sci USA 98(9):5116–5121

    CAS  PubMed  Google Scholar 

  61. Mukherjee S, Roberts SJ, van der Laan M (2005) Data-adaptive test statistics for microarray data. In: The ninth annual international conference on research in computational molecular biology, Cambridge, MA, pp 237–238

    Google Scholar 

  62. Martin DE, Demougin P, Hall MN, Bellis M (2004) Rank Difference Analysis of Microarrays (RDAM), a novel approach to statistical analysis of microarray expression profiling data. BMC Bioinform 5(1):148

    Google Scholar 

  63. Breitling R, Armengaud P, Amtmann A, Herzyk P (2004) Rank products: a simple, yet powerful, new method to detect differentially regulated genes in replicated microarray experiments. FEBS Lett 573(1–3):83–92

    CAS  PubMed  Google Scholar 

  64. Smyth GK (2004) Linear models and empirical bayes methods for assessing differential expression in microarray experiments. Stat Appl Genet Mol Biol 3(1):Article 3

    Google Scholar 

  65. Kerr MK, Martin M, Churchill GA (2000) Analysis of variance for gene expression microarray data. J Comput Biol 7:819–837

    CAS  PubMed  Google Scholar 

  66. Lee ML, Lu W, Whitmore GA, Beier D (2002) Models for microarray gene expression data. J Biopharm Stat 12:1–19

    PubMed  Google Scholar 

  67. Li C, Wong WH (2001) Model-based analysis of oligonucleotide arrays: model validation, design issues and standard error application. Genome Biol 2:research0049.1–0049.12

    Google Scholar 

  68. Irizarry RA, Hobbs B, Collin F, Beazer-Barclay YD, Antonellis KJ, Scherf U, Speed TP (2003) Exploration, normalization, and summaries of high density oligonucleotide array probe level data. Biostatistics 4(2):249–264

    PubMed  Google Scholar 

  69. Tsai CA, Chen JJ (2004) Significance analysis of ROC indices for comparing diagnostic markers: applications to gene microarray data. J Biopharm Stat 14(4):985–1003

    PubMed  Google Scholar 

  70. http://bioconductor.org/packages/release/bioc/html/RankProd.html

  71. Dopazo J, Carazo JM (1997) Phylogenetic reconstruction using an unsupervised growing neural network that adopts the topology of a phylogenetic tree. J Mol Evol 44:226–233

    CAS  PubMed  Google Scholar 

  72. Kaufman L, Rousseeuw PJ (1990) Finding groups in data: an introduction to cluster analysis. Wiley, New York

    Google Scholar 

  73. Kohonen T (1995) Self-organizing maps, vol 30, Springer series in information sciences. Springer, Berlin

    Google Scholar 

  74. Fraley C, Raftery AE (1998) How many clusters? Which clustering methods? Answers via model-based cluster analysis. Comput J 41:578–588

    Google Scholar 

  75. Fraley C, Raftery AE (1999) MCLUST: software for model-based cluster analysis. J Classif 16:297–306

    Google Scholar 

  76. Fraley C, Raftery AE (2002) Model-based clustering, discriminant analysis, and density estimation. J Am Stat Assoc 97:611–631

    Google Scholar 

  77. McLachlan GJ, Bean RW, Ben-Tovim JL, Zhu JX (2005) Using mixture models to detect differentially expressed genes. Aust J Exp Agric 45:859–866

    CAS  Google Scholar 

  78. McLachlan GJ, Bean RW, Ben-Tovim JL (2006) A simple implementation of a normal mixture approach to differential gene expression in multiclass microarrays. Bioinformatics 22:1608–1615

    CAS  PubMed  Google Scholar 

  79. Ma L, Teruya-Feldstein J, Weinberg RA (2007) Tumour invasion and metastasis initiated by microRNA-10b in breast cancer. Nature 449:682–688

    CAS  PubMed  Google Scholar 

  80. Tavazoie SF, Alarcon C, Oskarsson T, Padua D, Wang Q, Bos PD, Gerald WL, Massague J (2007) Endogenous human microRNAs that suppress breast cancer metastasis. Nature 451:147–152

    Google Scholar 

  81. Rosenfeld N, Aharonov R, Meiri E, Rosenwald S, Spector Y, Zepeniuk M, Benjamin H, Shabes N, Tabak S, Levy A, Lebanony D, Goren Y, Silberschein E, Targan N, Ben Ari A, Gilad S, Sion-Vardy N, Tobar A, Feinmesser M, Kharenko O, Nativ O, Nass D, Perelman M, Yosepovich A, Shalmon B, Polak-Charcon S, Fridman E, Avniel A, Bentwich I, Bentwich Z, Cohen D, Chajut A, Barshack I (2008) MicroRNAs accurately identify cancer tissue origin. Nat Biotechnol 26:462–469

    CAS  PubMed  Google Scholar 

  82. Blenkiron C, Goldstein LD, Thorne NP, Spiteri I, Chin SF, Dunning MJ, Barbosa-Morais NL, Teschendorff AE, Green AR, Ellis IO et al (2007) MicroRNA expression profiling of human breast cancer identifies new markers of tumour subtype. Genome Biol 8:R214

    PubMed Central  PubMed  Google Scholar 

  83. Mitchell PS, Parkin RK, Kroh EM, Fritz BR, Wyman SK, Pogosova-Agadjanyan EL, Peterson A, Noteboom J, O’Briant KC, Allen A, Lin DW, Urban N, Drescher CW, Knudsen BS, Stirewalt DL, Gentleman R, Vessella RL, Nelson PS, Martin DB, Tewari M (2008) Circulating microRNAs as stable blood-based markers for cancer detection. Proc Natl Acad Sci USA 105:10513–10518

    CAS  PubMed  Google Scholar 

  84. Lange J (2010) microRNA profiling on automated biochip platform reveals biomarker signatures from blood samples. Nat Methods 7. doi:10.1038/nmeth.f.281

  85. Lu J, Getz G, Miska EA, Alvarez-Saavedra E, Lamb J, Peck D, Sweet-Cordero A, Ebert BL, Mak RH, Ferrando AA, Downing JR, Jacks T, Horvitz HR, Golub TR (2005) MicroRNA expression profiles classify human cancers. Nature 435:834–838

    CAS  PubMed  Google Scholar 

  86. Visone R, Veronese A, Rassenti LZ, Balatti V, Pearl DK, Acunzo M, Volinia S, Taccioli C, Kipps TJ, Croce CM (2011) miR-181b is a biomarker of disease progression in chronic lymphocytic leukemia. Blood 118(11):3072–3079

    CAS  PubMed  Google Scholar 

  87. Bartel DP (2004) MicroRNAs: genomics, biogenesis, mechanism, and function. Cell 116:281–297

    CAS  PubMed  Google Scholar 

  88. Sethupathy P et al (2006) TarBase: a comprehensive database of experimentally supported animal microRNA targets. RNA 12:192–197

    CAS  PubMed  Google Scholar 

  89. Xiao F et al (2009) miRecords: an integrated resource for microRNA-target interactions. Nucleic Acids Res 37(Database issue):D105–D110

    CAS  PubMed Central  PubMed  Google Scholar 

  90. Chi SW et al (2009) Argonaute HITS-CLIP decodes microRNA–mRNA interaction maps. Nature 460(7254):479–486

    CAS  PubMed Central  PubMed  Google Scholar 

  91. Lewis BP, Burge CB, Bartel DP (2005) Conserved seed pairing, often flanked by adenosines, indicates that thousands of human genes are microRNA targets. Cell 120(1):15–20

    CAS  PubMed  Google Scholar 

  92. Rehmsmeier M et al (2004) Fast and effective prediction of microRNA/target duplexes. RNA 10:1507–1517

    CAS  PubMed  Google Scholar 

  93. Hofacker IL (2007) How microRNAs choose their targets. Nat Genet 39(10):1191–1192

    CAS  PubMed  Google Scholar 

  94. Mathews DH (2006) Revolutions in RNA secondary structure prediction. J Mol Biol 359(3):526–532

    CAS  PubMed  Google Scholar 

  95. Grimson A et al (2007) MicroRNA targeting specificity in mammals: determinants beyond seed pairing. Mol Cell 27:91–105

    CAS  PubMed Central  PubMed  Google Scholar 

  96. Mazière P, Enright AJ (2007) Prediction of microRNA targets. Drug Discov Today 12(11–12):452–458

    PubMed  Google Scholar 

  97. John B et al (2004) Human MicroRNA targets. PLoS Biol 2(11):1862–1879

    CAS  Google Scholar 

  98. Betel D et al (2007) The microRNA.org resource: targets and expression. Nucleic Acids Res 36(Database issue):D149–D153

    PubMed Central  PubMed  Google Scholar 

  99. Krek A et al (2005) Combinatorial microRNA target predictions. Nat Genet 37(5):495–500

    CAS  PubMed  Google Scholar 

  100. Kiriakidou M et al (2004) A combined computational-experimental approach predicts human microRNA targets. Genes Dev 18(10):1165–1178

    CAS  PubMed  Google Scholar 

  101. Miranda KC et al (2006) A pattern-based method for the identification of MicroRNA binding sites and their corresponding heteroduplexes. Cell 126:1203–1217

    CAS  PubMed  Google Scholar 

  102. Long D et al (2007) Potent effect of target structure on microRNA function. Nat Struct Mol Biol 14(4):287–294

    CAS  PubMed  Google Scholar 

  103. Kertesz M et al (2007) The role of site accessibilità in microRNA target recognition. Nat Genet 39(10):1278–1284

    CAS  PubMed  Google Scholar 

  104. Jiang Q, Wang Y, Juan L et al (2009) miR2Disease: a manually curated database for microRNA deregulation in human disease. Nucleic Acids Res 37(Database issue):D98–D104

    CAS  PubMed Central  PubMed  Google Scholar 

  105. Lu M, Zhang Q, Deng M et al (2008) An analysis of human microRNA and disease associations. PLoS One 3(10):e3420

    PubMed Central  PubMed  Google Scholar 

  106. Barh D, Bhat D, Viero C (2010) miReg: a resource for microRNA regulation. J Integr Bioinform 7(1).

    Google Scholar 

  107. Laganà A, Forte S, Giudice A et al (2009) miRò: a miRNA knowledge base. Database (Oxford) 2009:bap008. doi:10.1093/database/bap008

    Google Scholar 

  108. 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

    CAS  PubMed Central  PubMed  Google Scholar 

  109. Landgraf P, Rusu M, Sheridan R et al (2007) A mammalian microRNA expression atlas based on small RNA library sequencing. Cell 129:1401–1414

    CAS  PubMed Central  PubMed  Google Scholar 

  110. Ulitsky I, Laurent LC, Shamir R (2010) Towards computational prediction of microRNA function and activity. Nucleic Acids Res 38(15):e160

    PubMed Central  PubMed  Google Scholar 

  111. Le Brigand K, Robbe-Sermesant K, Mari B et al (2010) MiRonTop: mining microRNAs targets across large scale gene expression studies. Bioinformatics 26(24):3131–3132

    PubMed  Google Scholar 

  112. Sales G, Coppe A, Bisognin A et al (2010) MAGIA, a web-based tool for miRNA and Genes Integrated Analysis. Nucleic Acids Res 38(Web Server issue):W352–W359

    CAS  PubMed Central  PubMed  Google Scholar 

  113. Alexiou P, Maragkakis M, Papadopoulos GL et al (2010) The DIANA-mirExTra web server: from gene expression data to microRNA function. PLoS One 5(2):e9171

    PubMed Central  PubMed  Google Scholar 

  114. Papadopoulos GL, Alexiou P, Maragkakis M et al (2009) DIANA-mirPath: integrating human and mouse microRNAs in pathways. Bioinformatics 25(15):1991–1993

    CAS  PubMed  Google Scholar 

  115. Maragkakis M, Alexiou P, Papadopoulos GL et al (2009) Accurate microRNA target prediction correlates with protein repression levels. BMC Bioinform 10:295

    Google Scholar 

  116. Le Béchec A, Portales-Casamar E, Vetter G et al (2011) MIR@NT@N: a framework integrating transcription factors, microRNAs and their targets to identify sub-network motifs in a meta-regulation network model. BMC Bioinform 12:67

    Google Scholar 

  117. Portales-Casamar E, Arenillas D, Lim J et al (2009) The PAZAR database of gene regulatory information coupled to the ORCA toolkit for the study of regulatory sequences. Nucleic Acids Res 37(Database issue):D54–D60

    CAS  PubMed Central  PubMed  Google Scholar 

  118. Portales-Casamar E, Thongjuea S, Kwon AT et al (2010) JASPAR 2010: the greatly expanded open-access database of transcription factor binding profiles. Nucleic Acids Res 38(Database issue):D105–D110

    CAS  PubMed Central  PubMed  Google Scholar 

  119. Ho Sui SJ, Mortimer JR, Arenillas DJ et al (2005) oPOSSUM: identification of over-represented transcription factor binding sites in co-expressed genes. Nucleic Acids Res 33(10):3154–3164

    PubMed Central  PubMed  Google Scholar 

  120. Kozomara A, Griffiths-Jones S (2011) miRBase: integrating microRNA annotation and deep-sequencing data. Nucleic Acids Res 39(Database Issue):D152–D157

    CAS  PubMed Central  PubMed  Google Scholar 

  121. Griffiths-Jones S, Saini HK, van Dongen S et al (2008) miRBase: tools for microRNA genomics. Nucleic Acids Res 36(Database Issue):D154–D158

    CAS  PubMed Central  PubMed  Google Scholar 

  122. Griffiths-Jones S, Grocock RJ, van Dongen S et al (2006) miRBase: microRNA sequences, targets and gene nomenclature. Nucleic Acids Res 34(Database Issue):D140–D144

    CAS  PubMed Central  PubMed  Google Scholar 

  123. Griffiths-Jones S (2004) The microRNA registry. Nucleic Acids Res 32(Database Issue):D109–D111

    CAS  PubMed Central  PubMed  Google Scholar 

  124. Chaurasia G, Malhotra S, Russ J et al (2009) UniHI 4: new tools for query, analysis and visualization of the human protein-protein interactome. Nucleic Acids Res 37(Database issue):D657–D660

    CAS  PubMed Central  PubMed  Google Scholar 

  125. Huang GT, Athanassiou C, Benos PV (2011) mirConnX: condition-specific mRNA-microRNA network integrator. Nucleic Acids Res 39(Web Server issue):W416–W423

    CAS  PubMed Central  PubMed  Google Scholar 

  126. Ferro A, Giugno R, Laganà A et al (2009) miRScape: a Cytoscape plugin to annotate biological networks with microRNAs. NETTAB 2009 conference, Catania, 10–12 June 2009

    Google Scholar 

  127. Smoot ME, Ono K, Ruscheinski J et al (2011) Cytoscape 2.8: new features for data integration and network visualization. Bioinformatics 27(3):431–432

    CAS  PubMed  Google Scholar 

  128. Cline MS, Smoot M, Cerami E et al (2007) Integration of biological networks and gene expression data using Cytoscape. Nat Protoc 2(10):2366–2382

    CAS  PubMed Central  PubMed  Google Scholar 

  129. Shannon P, Markiel A, Ozier O et al (2003) Cytoscape: a software environment for integrated models of biomolecular interaction networks. Genome Res 13(11):2498–2504

    CAS  PubMed  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Department of Clinical and Molecular Biomedicine, University of Catania, Catania, Italy

    Luciano Cascione, Alfredo Ferro, Rosalba Giugno, Alfredo Pulvirenti & Dario Veneziano

  2. Dipartimento di Matematica e Informatica, Università degli Studi di Catania, Città Universitaria – Viale A.Doria, 6, Catania, 95125, Italy

    Alfredo Ferro

  3. Department of Molecular Virology, Immunology and Medical Genetics, Comprehensive Cancer Center, The Ohio State University, Columbus, OH, USA

    Alessandro Laganà

  4. Research and Development, IGA Technology Services, Udine, Italy

    Giuseppe Pigola

Authors
  1. Luciano Cascione
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Alfredo Ferro
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Rosalba Giugno
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Alessandro Laganà
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Giuseppe Pigola
    View author publications

    You can also search for this author in PubMed Google Scholar

  6. Alfredo Pulvirenti
    View author publications

    You can also search for this author in PubMed Google Scholar

  7. Dario Veneziano
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Alfredo Ferro .

Editor information

Editors and Affiliations

  1. Systems Biology and Bioinformatics, University of Rostock, Rostock, Germany

    Ulf Schmitz

  2. Dept. of Systems Biology and Bioinformat, Universität Rostock Institut of Computer Science, Rostock, Germany

    Olaf Wolkenhauer

  3. Rostock, Germany

    Julio Vera

Rights and permissions

Reprints and permissions

Copyright information

© 2013 Springer Science+Business Media Dordrecht

About this chapter

Cite this chapter

Cascione, L. et al. (2013). Elucidating the Role of microRNAs in Cancer Through Data Mining Techniques. In: Schmitz, U., Wolkenhauer, O., Vera, J. (eds) MicroRNA Cancer Regulation. Advances in Experimental Medicine and Biology, vol 774. Springer, Dordrecht. https://doi.org/10.1007/978-94-007-5590-1_15

Download citation

Publish with us

Policies and ethics

Search

Navigation