Skip to main content

RNA Systems Biology for Cancer: From Diagnosis to Therapy

  • Protocol
Systems Medicine

Part of the book series: Methods in Molecular Biology ((MIMB,volume 1386))

Abstract

It is due to the advances in high-throughput omics data generation that RNA species have re-entered the focus of biomedical research. International collaborate efforts, like the ENCODE and GENCODE projects, have spawned thousands of previously unknown functional non-coding RNAs (ncRNAs) with various but primarily regulatory roles. Many of these are linked to the emergence and progression of human diseases. In particular, interdisciplinary studies integrating bioinformatics, systems biology, and biotechnological approaches have successfully characterized the role of ncRNAs in different human cancers. These efforts led to the identification of a new tool-kit for cancer diagnosis, monitoring, and treatment, which is now starting to enter and impact on clinical practice. This chapter is to elaborate on the state of the art in RNA systems biology, including a review and perspective on clinical applications toward an integrative RNA systems medicine approach. The focus is on the role of ncRNAs in cancer.

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

Access this chapter

Protocol
USD 49.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 84.99
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 139.00
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info
Hardcover Book
USD 109.99
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

Institutional subscriptions

References

  1. Gardner PP, Giegerich R (2004) A comprehensive comparison of comparative RNA structure prediction approaches. BMC Bioinformatics 5:140

    Article  PubMed Central  PubMed  CAS  Google Scholar 

  2. Gilbert W (1986) Origin of life: the RNA world. Nature 319(6055):618

    Article  Google Scholar 

  3. Jeffares DC, Poole AM, Penny D (1998) Relics from the RNA world. J Mol Evol 46(1):18–36

    Article  CAS  PubMed  Google Scholar 

  4. Poole AM, Jeffares DC, Penny D (1998) The path from the RNA world. J Mol Evol 46(1):1–17

    Article  CAS  PubMed  Google Scholar 

  5. Lau NC et al (2001) An abundant class of tiny RNAs with probable regulatory roles in Caenorhabditis elegans. Science 294(5543):858–862

    Article  CAS  PubMed  Google Scholar 

  6. Soifer HS, Rossi JJ, Sætrom P (2007) MicroRNAs in disease and potential therapeutic applications. Mol Ther 15(12):2070–2079

    Article  CAS  PubMed  Google Scholar 

  7. Calin GA et al (2004) Human microRNA genes are frequently located at fragile sites and genomic regions involved in cancers. Proc Natl Acad Sci U S A 101(9):2999–3004

    Article  PubMed Central  CAS  PubMed  Google Scholar 

  8. Calin GA et al (2004) MicroRNA profiling reveals distinct signatures in B cell chronic lymphocytic leukemias. Proc Natl Acad Sci U S A 101(32):11755–11760

    Article  PubMed Central  CAS  PubMed  Google Scholar 

  9. Amirkhah R, Schmitz U, Linnebacher M, Wolkenhauer O, Farazmand A (2015) MicroRNA-mRNA interactions in colorectal cancer and their role in tumor progression: miRNA targets in colorectal cancer. Genes, Chromosome Canc 54(3):129–141

    Google Scholar 

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

    Article  PubMed Central  CAS  PubMed  Google Scholar 

  11. Landgraf P et al (2007) A mammalian microRNA expression atlas based on small RNA library sequencing. Cell 129(7):1401–1414

    Article  PubMed Central  CAS  PubMed  Google Scholar 

  12. Selbach M et al (2008) Widespread changes in protein synthesis induced by microRNAs. Nature 455(7209):58–63

    Article  CAS  PubMed  Google Scholar 

  13. Garzon R, Calin GA, Croce CM (2009) MicroRNAs in cancer. Annu Rev Med 60:167–179

    Article  CAS  PubMed  Google Scholar 

  14. Yuan JH, Yang F, Wang F, Ma JZ, Guo YJ, Tao QF, Liu F, Pan W, Wang TT, Zhou CC, Wang SB, Wang YZ, Yang Y, Yang N, Zhou WP, Yang GS et al (2014) A long noncoding RNA activated by TGF-beta promotes the invasion-metastasis cascade in hepatocellular carcinoma. Cancer Cell 25(5):666–681

    Article  CAS  PubMed  Google Scholar 

  15. McCann KL, Baserga SJ (2012) Long noncoding RNAs as sinks in Prader-Willi syndrome. Mol Cell 48(2):155–157

    Article  PubMed Central  CAS  PubMed  Google Scholar 

  16. Hung T et al (2011) Extensive and coordinated transcription of noncoding RNAs within cell-cycle promoters. Nat Genet 43(7):621–629

    Article  PubMed Central  CAS  PubMed  Google Scholar 

  17. Feng J et al (2006) The Evf-2 noncoding RNA is transcribed from the Dlx-5/6 ultraconserved region and functions as a Dlx-2 transcriptional coactivator. Genes Dev 20(11):1470–1484

    Article  PubMed Central  CAS  PubMed  Google Scholar 

  18. Poliseno L et al (2010) A coding-independent function of gene and pseudogene mRNAs regulates tumour biology. Nature 465(7301):1033–1038

    Article  PubMed Central  CAS  PubMed  Google Scholar 

  19. Salmena L et al (2011) A ceRNA hypothesis: the Rosetta Stone of a hidden RNA language? Cell 146(3):353–358

    Article  PubMed Central  CAS  PubMed  Google Scholar 

  20. Leucci E et al (2013) microRNA-9 targets the long non-coding RNA MALAT1 for degradation in the nucleus. Sci Rep 3:2535

    Article  PubMed Central  PubMed  Google Scholar 

  21. Li CH, Chen Y (2013) Targeting long non-coding RNAs in cancers: progress and prospects. Int J Biochem Cell Biol 45:1895–1910

    Article  CAS  PubMed  Google Scholar 

  22. Kemena C et al (2013) Using tertiary structure for the computation of highly accurate multiple RNA alignments with the SARA-Coffee package. Bioinformatics 29(9):1112–1119

    Article  CAS  PubMed  Google Scholar 

  23. Gibb EA, Brown CJ, Lam WL (2011) The functional role of long non-coding RNA in human carcinomas. Mol Cancer 10

    Google Scholar 

  24. Yang QQ, Deng YF (2014) Long non-coding RNAs as novel biomarkers and therapeutic targets in head and neck cancers. Int J Clin Exp Pathol 7(4):1286–1292

    PubMed Central  PubMed  Google Scholar 

  25. Shen Z et al (2014) Long non-coding RNA profiling in laryngeal squamous cell carcinoma and its clinical significance: potential biomarkers for LSCC. PLoS One 9(9):e108237

    Article  PubMed Central  PubMed  CAS  Google Scholar 

  26. Zheng HT et al (2014) High expression of lncRNA MALAT1 suggests a biomarker of poor prognosis in colorectal cancer. Int J Clin Exp Pathol 7(6):3174–3181

    PubMed Central  CAS  PubMed  Google Scholar 

  27. Gupta RA et al (2010) Long non-coding RNA HOTAIR reprograms chromatin state to promote cancer metastasis. Nature 464(7291):1071–1076

    Article  PubMed Central  CAS  PubMed  Google Scholar 

  28. Li R, Zhang L, Jia L, Duan Y, Li Y, Bao L et al (2014) Long non-coding RNA BANCR promotes proliferation in malignant melanoma by regulating MAPK pathway activation. PLoS One 9(6):e100893. doi:10.1371/journal.pone.0100893

    Google Scholar 

  29. Tang L et al (2013) Long noncoding RNA HOTAIR is associated with motility, invasion, and metastatic potential of metastatic melanoma. Biomed Res Int 2013

    Google Scholar 

  30. Khaitan D et al (2011) The melanoma-upregulated long noncoding RNA SPRY4-IT1 modulates apoptosis and invasion. Cancer Res 71(11):3852–3862

    Article  CAS  PubMed  Google Scholar 

  31. Garzon R et al (2006) MicroRNA expression and function in cancer. Trends Mol Med 12(12):580–587

    Article  CAS  PubMed  Google Scholar 

  32. Schmitz U, Wolkenhauer O, Vera J (2013) MicroRNA cancer regulation advanced concepts, bioinformatics and systems biology tools. Springer, Dordrecht

    Google Scholar 

  33. Mendes ND, Freitas AT, Sagot MF (2009) Current tools for the identification of miRNA genes and their targets. Nucleic Acids Res 37(8):2419–2433

    Article  PubMed Central  CAS  PubMed  Google Scholar 

  34. Friedman Y, Balaga O, Linial M (2013) Working together: combinatorial regulation by microRNAs. In: Schmitz U, Wolkenhauer O, Vera J (eds) MicroRNA cancer regulation. Springer, Dordrecht, The Netherlands, pp 317–337

    Chapter  Google Scholar 

  35. Lai X, Schmitz U, Gupta SK et al (2012) Computational analysis of target hub gene repression regulated by multiple and cooperative miRNAs. Nucleic Acids Res 40(18):8818–8834

    Article  PubMed Central  CAS  PubMed  Google Scholar 

  36. Vera J et al (2013) MicroRNA-regulated networks: the perfect storm for classical molecular biology, the ideal scenario for systems biology. Adv Exp Med Biol 774:55–76

    Article  CAS  PubMed  Google Scholar 

  37. Vera J, Wolkenhauer O, Schmitz U (2014) Current achievements in cancer systems biology. In: eLS. Wiley, Chichester. http://www.els.net

  38. Bhattacharya A et al (2012) Regulation of cell cycle checkpoint kinase WEE1 by miR-195 in malignant melanoma. Oncogene 32:3175–3183

    Article  PubMed  CAS  Google Scholar 

  39. Alla V et al (2012) E2F1 confers anticancer drug resistance by targeting ABC transporter family members and Bcl-2 via the p73/DNp73-miR-205 circuitry. Cell Cycle 11(16):3067–3078

    Article  PubMed Central  CAS  PubMed  Google Scholar 

  40. Vera J et al (2013) Kinetic modeling-based detection of genetic signatures that provide chemoresistance via the E2F1-p73/DNp73-miR-205 network. Cancer Res 73(12):3511–3524

    Article  CAS  PubMed  Google Scholar 

  41. Bhattacharya A et al (2015) miR-638 promotes melanoma metastasis and protects melanoma cells from apoptosis and autophagy. Oncotarget 6(5):2966–2980

    Article  PubMed Central  PubMed  Google Scholar 

  42. Knoll S et al (2014) E2F1 induces miR-224/452 expression to drive EMT through TXNIP downregulation. EMBO Rep 15(12):1315–1329

    Article  PubMed Central  CAS  PubMed  Google Scholar 

  43. Vaske CJ et al (2010) Inference of patient-specific pathway activities from multi-dimensional cancer genomics data using PARADIGM. Bioinformatics 26(12):i237–i245

    Article  PubMed Central  CAS  PubMed  Google Scholar 

  44. Ruan K, Fang X, Ouyang G (2009) MicroRNAs: novel regulators in the hallmarks of human cancer. Cancer Lett 285(2):116–126

    Article  CAS  PubMed  Google Scholar 

  45. Cascione L et al (2013) Elucidating the role of microRNAs in cancer through data mining techniques. Adv Exp Med Biol 774:291–315

    Article  CAS  PubMed  Google Scholar 

  46. Liu DF et al (2012) MicroRNA expression profile analysis reveals diagnostic biomarker for human prostate cancer. Asian Pac J Cancer Prev 13(7):3313–3317

    Article  PubMed  Google Scholar 

  47. Khanin R, Vinciotti V (2008) Computational modeling of post-transcriptional gene regulation by microRNAs. J Comput Biol 15(3):305–316

    Article  CAS  PubMed  Google Scholar 

  48. Lee Y et al (2010) Network modeling identifies molecular functions targeted by miR-204 to suppress head and neck tumor metastasis. PLoS Comput Biol 6(4):e1000730

    Article  PubMed Central  PubMed  CAS  Google Scholar 

  49. Nikolov S et al (2011) A model-based strategy to investigate the role of microRNA regulation in cancer signalling networks. Theory Biosci 130(1):55–69

    Article  CAS  PubMed  Google Scholar 

  50. Weber M et al (2013) Dynamic modelling of microRNA regulation during mesenchymal stem cell differentiation. BMC Syst Biol 7:124

    Article  PubMed Central  PubMed  CAS  Google Scholar 

  51. Röhr C et al (2013) High-throughput miRNA and mRNA sequencing of paired colorectal normal, tumor and metastasis tissues and bioinformatic modeling of miRNA-1 therapeutic applications. PLoS One 8(7):e67461

    Article  PubMed Central  PubMed  CAS  Google Scholar 

  52. Batagov AO et al (2013) Role of genomic architecture in the expression dynamics of long noncoding RNAs during differentiation of human neuroblastoma cells. BMC Syst Biol 7(Suppl 3):S11

    Article  PubMed Central  PubMed  Google Scholar 

  53. Enright AJ et al (2003) MicroRNA targets in Drosophila. Genome Biol 5(1):R1

    Article  PubMed Central  PubMed  Google Scholar 

  54. Ritchie W, Flamant S, Rasko JEJ (2009) Predicting microRNA targets and functions: traps for the unwary. Nat Methods 6(6):397–398

    Article  CAS  PubMed  Google Scholar 

  55. Ritchie W, Rasko JEJ, Flamant S (2013) MicroRNA target prediction and validation. In: Schmitz U, Wolkenhauer O, Vera J (eds) MicroRNA cancer regulation. Springer, Dordrecht, The Netherlands, pp 39–53

    Chapter  Google Scholar 

  56. Sethupathy P, Megraw M, Hatzigeorgiou AG (2006) A guide through present computational approaches for the identification of mammalian microRNA targets. Nat Methods 3(11):881–886

    Article  CAS  PubMed  Google Scholar 

  57. Vergoulis T et al (2011) TarBase 6.0: capturing the exponential growth of miRNA targets with experimental support. Nucleic Acids Res 40(D1):D222–D229

    Article  PubMed Central  PubMed  CAS  Google Scholar 

  58. Hsu SD, Tseng YT, Shrestha S, Lin YL, Khaleel A et al (2014) miRTarBase update 2014: an information resource for experimentally validated miRNA-target interactions. Nucleic Acids Res 42:D78–D85

    Google Scholar 

  59. Lim LP et al (2005) Microarray analysis shows that some microRNAs downregulate large numbers of target mRNAs. Nature 433(7027):769–773

    Article  CAS  PubMed  Google Scholar 

  60. Farh KK-H et al (2005) The widespread impact of mammalian MicroRNAs on mRNA repression and evolution. Science 310(5755):1817–1821

    Article  CAS  PubMed  Google Scholar 

  61. Huang JC et al (2007) Using expression profiling data to identify human microRNA targets. Nat Methods 4(12):1045–1049

    Article  CAS  PubMed  Google Scholar 

  62. Wang X, El Naqa IM (2008) Prediction of both conserved and nonconserved microRNA targets in animals. Bioinformatics 24(3):325–332

    Article  PubMed  CAS  Google Scholar 

  63. Volinia S et al (2009) Identification of microRNA activity by Targets’ Reverse EXpression. Bioinformatics 26(1):91–97

    Article  PubMed Central  PubMed  CAS  Google Scholar 

  64. Huang JC, Frey BJ, Morris QD (2008) Comparing sequence and expression for predicting microRNA targets using GenMiR3. Pac Symp Biocomput 2008:52–63

    Google Scholar 

  65. Bhattacharya A, Kunz M (2013) Target identification, microRNA. In: Dubitzky W et al (eds) Encyclopedia of systems biology. Springer, New York, pp 2138–2142

    Chapter  Google Scholar 

  66. Filipowicz W, Bhattacharyya SN, Sonenberg N (2008) Mechanisms of post-transcriptional regulation by microRNAs: are the answers in sight? Nat Rev Genet 9(2):102–114

    Article  CAS  PubMed  Google Scholar 

  67. Licatalosi DD et al (2008) HITS-CLIP yields genome-wide insights into brain alternative RNA processing. Nature 456(7221):464–469

    Article  PubMed Central  CAS  PubMed  Google Scholar 

  68. Schmitz U (2013) MicroRNA target regulation. In: Dubitzky W et al (eds) Encyclopedia of systems biology. Springer, New York, pp 1346–1350

    Chapter  Google Scholar 

  69. Lai X et al (2013) A systems’ biology approach to study microRNA-mediated gene regulatory networks. Biomed Res Int 2013

    Google Scholar 

  70. Schmitz U et al (2014) Cooperative gene regulation by microRNA pairs and their identification using a computational workflow. Nucleic Acids Res 42(12):7539–7552

    Article  PubMed Central  CAS  PubMed  Google Scholar 

  71. Pritchard CC, Cheng HH, Tewari M (2012) MicroRNA profiling: approaches and considerations. Nat Rev Genet 13(5):358–369

    Article  PubMed Central  CAS  PubMed  Google Scholar 

  72. Gupta SK, Schmitz U (2011) Bioinformatics analysis of high-throughput experiments. In: Singh MP, Agrawal A, Sharma B (eds) Recent trends in biotechnology. Nova Science Publishers, Inc., New York, NY, pp 129–156

    Google Scholar 

  73. Dennis G Jr et al (2003) DAVID: database for annotation, visualization, and integrated discovery. Genome Biol 4(5):P3

    Article  PubMed  Google Scholar 

  74. Satoh J-I (2012) Molecular network analysis of human microRNA targetome: from cancers to Alzheimer’s disease. BioData Min 5(1):17

    Article  PubMed Central  CAS  PubMed  Google Scholar 

  75. Forbes SA et al (2015) COSMIC: exploring the world’s knowledge of somatic mutations in human cancer. Nucleic Acids Res 43(D1):D805–D811

    Article  PubMed Central  PubMed  Google Scholar 

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

    Article  PubMed Central  CAS  PubMed  Google Scholar 

  77. Xie B et al (2013) miRCancer: a microRNA-cancer association database constructed by text mining on literature. Bioinformatics 29(5):638–644

    Article  CAS  PubMed  Google Scholar 

  78. Russo F et al (2012) miRandola: extracellular circulating microRNAs database. PLoS One 7(10):e47786

    Article  PubMed Central  CAS  PubMed  Google Scholar 

  79. Ruepp A, Kowarsch A, Theis F (2012) PhenomiR: microRNAs in human diseases and biological processes. Methods Mol Biol 822:249–260

    Article  CAS  PubMed  Google Scholar 

  80. Bhattacharya A, Ziebarth JD, Cui Y (2013) SomamiR: a database for somatic mutations impacting microRNA function in cancer. Nucleic Acids Res 41(Database issue):D977–D982

    Article  PubMed Central  CAS  PubMed  Google Scholar 

  81. Chen G et al (2012) LncRNADisease: a database for long-non-coding RNA-associated diseases. Nucleic Acids Res 41(D1):D983–D986

    Article  PubMed Central  PubMed  CAS  Google Scholar 

  82. Cun Y, Frohlich H (2013) Network and data integration for biomarker signature discovery via network smoothed T-statistics. PLoS One 8(9):e73074

    Article  PubMed Central  CAS  PubMed  Google Scholar 

  83. Stahlhut C, Slack FJ (2013) MicroRNAs and the cancer phenotype: profiling, signatures and clinical implications. Genome Med 5(12):111

    Article  PubMed Central  PubMed  Google Scholar 

  84. Ueda T et al (2010) Relation between microRNA expression and progression and prognosis of gastric cancer: a microRNA expression analysis. Lancet Oncol 11(2):136–146

    Article  PubMed Central  CAS  PubMed  Google Scholar 

  85. Davison TS, Johnson CD, Andruss BF (2006) Analyzing micro-RNA expression using microarrays. Methods Enzymol 411:14–34

    Article  CAS  PubMed  Google Scholar 

  86. Yin JQ, Zhao RC, Morris KV (2008) Profiling microRNA expression with microarrays. Trends Biotechnol 26(2):70–76

    Article  CAS  PubMed  Google Scholar 

  87. Thomas RK et al (2007) High-throughput oncogene mutation profiling in human cancer. Nat Genet 39(3):347–351

    Article  CAS  PubMed  Google Scholar 

  88. El-Metwally S et al (2013) Next-generation sequence assembly: four stages of data processing and computational challenges. PLoS Comput Biol 9(12):e1003345

    Article  PubMed Central  PubMed  CAS  Google Scholar 

  89. Ren X et al (2012) A unified computational model for revealing and predicting subtle subtypes of cancers. BMC Bioinformatics 13:70

    Article  PubMed Central  PubMed  Google Scholar 

  90. Keutgen XM et al (2012) A panel of four miRNAs accurately differentiates malignant from benign indeterminate thyroid lesions on fine needle aspiration. Clin Cancer Res 18(7):2032–2038

    Article  CAS  PubMed  Google Scholar 

  91. Kuo TY et al (2012) Computational analysis of mRNA expression profiles identifies microRNA-29a/c as predictor of colorectal cancer early recurrence. PLoS One 7(2):e31587

    Article  PubMed Central  CAS  PubMed  Google Scholar 

  92. Steyerberg EW et al (2013) Prognosis Research Strategy (PROGRESS) 3: prognostic model research. PLoS Med 10(2):e1001381

    Article  PubMed Central  PubMed  Google Scholar 

  93. Fan C et al (2011) Building prognostic models for breast cancer patients using clinical variables and hundreds of gene expression signatures. BMC Med Genomics 4:3

    Article  PubMed Central  PubMed  Google Scholar 

  94. Gould Rothberg BE et al (2009) Melanoma prognostic model using tissue microarrays and genetic algorithms. J Clin Oncol 27(34):5772–5780

    Article  PubMed Central  PubMed  Google Scholar 

  95. Motzer RJ et al (1999) Survival and prognostic stratification of 670 patients with advanced renal cell carcinoma. J Clin Oncol 17(8):2530–2540

    CAS  PubMed  Google Scholar 

  96. Shah MY, Calin GA (2014) MicroRNAs as therapeutic targets in human cancers. Wiley Interdiscip Rev RNA 5(4):537–548

    Article  PubMed Central  CAS  PubMed  Google Scholar 

  97. Wu W (2010) MicroRNA: potential targets for the development of novel drugs? Drugs R D 10(1):1–8

    Article  PubMed Central  CAS  PubMed  Google Scholar 

  98. Gironella M et al (2007) Tumor protein 53-induced nuclear protein 1 expression is repressed by miR-155, and its restoration inhibits pancreatic tumor development. Proc Natl Acad Sci U S A 104(41):16170–16175

    Article  PubMed Central  CAS  PubMed  Google Scholar 

  99. Lu Y et al (2009) A single anti-microRNA antisense oligodeoxyribonucleotide (AMO) targeting multiple microRNAs offers an improved approach for microRNA interference. Nucleic Acids Res 37(3):e24

    Article  PubMed Central  PubMed  CAS  Google Scholar 

  100. Kota J et al (2009) Therapeutic microRNA delivery suppresses tumorigenesis in a murine liver cancer model. Cell 137(6):1005–1017

    Article  PubMed Central  CAS  PubMed  Google Scholar 

  101. Misso G et al (2014) Mir-34: a new weapon against cancer? Mol Ther Nucleic Acids 3:e194

    Article  CAS  PubMed  Google Scholar 

  102. Zhao JJ et al (2008) MicroRNA-221/222 negatively regulates estrogen receptor alpha and is associated with tamoxifen resistance in breast cancer. J Biol Chem 283(45):31079–31086

    Article  PubMed Central  CAS  PubMed  Google Scholar 

  103. Ji Q et al (2009) MicroRNA miR-34 inhibits human pancreatic cancer tumor-initiating cells. PLoS One 4(8):e6816

    Article  PubMed Central  PubMed  CAS  Google Scholar 

  104. Deng J et al (2014) Targeting miR-21 enhances the sensitivity of human colon cancer HT-29 cells to chemoradiotherapy in vitro. Biochem Biophys Res Commun 443(3):789–795

    Article  CAS  PubMed  Google Scholar 

  105. Zhang Y et al (2014) MiR-124 radiosensitizes human colorectal cancer cells by targeting PRRX1. PLoS One 9(4):e93917

    Article  PubMed Central  PubMed  CAS  Google Scholar 

  106. Malek E, Jagannathan S, Driscoll JJ (2014) Correlation of long non-coding RNA expression with metastasis, drug resistance and clinical outcome in cancer. Oncotarget 5(18):8027–8038

    Article  PubMed Central  PubMed  Google Scholar 

  107. Fan Y et al (2014) Long non-coding RNA UCA1 increases chemoresistance of bladder cancer cells by regulating Wnt signaling. FEBS J 281(7):1750–1758

    Article  CAS  PubMed  Google Scholar 

  108. Chowdhury S, Pradhan RN, Sarkar RR (2013) Structural and logical analysis of a comprehensive hedgehog signaling pathway to identify alternative drug targets for glioma, colon and pancreatic cancer. PLoS One 8(7):e69132

    Article  PubMed Central  CAS  PubMed  Google Scholar 

  109. Rateitschak K et al (2012) Parameter identifiability and sensitivity analysis predict targets for enhancement of STAT1 activity in pancreatic cancer and stellate cells. PLoS Comput Biol 8(12):e1002815

    Article  PubMed Central  CAS  PubMed  Google Scholar 

  110. Wu M, Chan C (2014) Prediction of therapeutic microRNA based on the human metabolic network. Bioinformatics

    Google Scholar 

  111. Wu M, Chan C (2014) Prediction of therapeutic microRNA based on the human metabolic network. Bioinformatics. doi:10.1093/bioinformatics/btt751

    Google Scholar 

  112. Zeng T et al (2014) Prediction of dynamical drug sensitivity and resistance by module network rewiring-analysis based on transcriptional profiling. Drug Resist Updat 17(3):64–76

    Article  PubMed  Google Scholar 

  113. Kanagavel D et al (2010) A prognostic model in patients treated for metastatic gastric cancer with second-line chemotherapy. Ann Oncol 21(9):1779–1785

    Article  CAS  PubMed  Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Ulf Schmitz .

Editor information

Editors and Affiliations

Rights and permissions

Reprints and permissions

Copyright information

© 2016 Springer Science+Business Media New York

About this protocol

Cite this protocol

Amirkhah, R., Farazmand, A., Wolkenhauer, O., Schmitz, U. (2016). RNA Systems Biology for Cancer: From Diagnosis to Therapy. In: Schmitz, U., Wolkenhauer, O. (eds) Systems Medicine. Methods in Molecular Biology, vol 1386. Humana Press, New York, NY. https://doi.org/10.1007/978-1-4939-3283-2_14

Download citation

  • DOI: https://doi.org/10.1007/978-1-4939-3283-2_14

  • Publisher Name: Humana Press, New York, NY

  • Print ISBN: 978-1-4939-3282-5

  • Online ISBN: 978-1-4939-3283-2

  • eBook Packages: Springer Protocols

Publish with us

Policies and ethics