Table of Contents Author Guidelines Submit a Manuscript
BioMed Research International
Volume 2014, Article ID 193817, 13 pages
http://dx.doi.org/10.1155/2014/193817
Research Article

Drug Repositioning Discovery for Early- and Late-Stage Non-Small-Cell Lung Cancer

1Department of Computer Science and Information Engineering, National Formosa University, 64 Wen-Hwa Road, Hu-Wei, Yun-Lin 632, Taiwan
2Division of Hematology and Oncology, Department of Medicine, Taipei Veterans General Hospital, Faculty of Medicine, National Yang-Ming University, Taipei 112, Taiwan
3Cancer Center, Keelung Chang Gang Memorial Hospital, Keelung 204, Taiwan
4Institute of Biopharmaceutical Sciences, National Yang-Ming University, No. 155, Section 2, Linong Street, Taipei 112, Taiwan
5Genome Research Center, National Yang-Ming University, Taipei 112, Taiwan
6Department of Biomedical Informatics, Asia University, 500 Lioufeng Road, Wufeng Shiang, Taichung 41354, Taiwan
7Department of Medical Research, China Medical University Hospital, China Medical University, Taichung 40402, Taiwan

Received 10 April 2014; Revised 7 July 2014; Accepted 12 July 2014; Published 18 August 2014

Academic Editor: X. Li

Copyright © 2014 Chien-Hung Huang et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Linked References

  1. A. Jemal, R. Siegel, E. Ward et al., “Cancer statistics, 2008,” CA: A Cancer Journal for Clinicians, vol. 58, no. 2, pp. 71–96, 2008. View at Publisher · View at Google Scholar · View at Scopus
  2. Department of Health, Cancer Registry Annual Report in Taiwan Area, Department of Health, Executive Yuan, China, 2007.
  3. C. H. Huang, M. Y. Wu, P. M. H. Chang, C. Y. Huang, and K. L. Ng, “In silico identification of potential targets and drugs for non small cell lung cancer,” IET Systems Biology, vol. 8, no. 2, pp. 56–66, 2014. View at Google Scholar
  4. M. Y. Lan, C. L. Chen, K. T. Lin et al., “From NPC therapeutic target identification to potential treatment strategy,” Molecular Cancer Therapeutics, vol. 9, no. 9, pp. 2511–2523, 2010. View at Publisher · View at Google Scholar · View at Scopus
  5. D. C. Hassane, M. L. Guzman, C. Corbett et al., “Discovery of agents that eradicate leukemia stem cells using an in silico screen of public gene expression data,” Blood, vol. 111, no. 12, pp. 5654–5662, 2008. View at Publisher · View at Google Scholar · View at Scopus
  6. J. M. Rosenbluth, D. J. Mays, M. F. Pino, L. J. Tang, and J. A. Pietenpol, “A gene signature-based approach identifies mTOR as a regulator of p73,” Molecular and Cellular Biology, vol. 28, no. 19, pp. 5951–5964, 2008. View at Publisher · View at Google Scholar · View at Scopus
  7. S. R. Setlur, K. D. Mertz, Y. Hoshida et al., “Estrogen-dependent signaling in a molecularly distinct subclass of aggressive prostate cancer,” Journal of the National Cancer Institute, vol. 100, no. 11, pp. 815–825, 2008. View at Publisher · View at Google Scholar · View at Scopus
  8. T. Barrett, S. E. Wilhite, P. Ledoux et al., “NCBI GEO: archive for functional genomics data sets—update,” Nucleic Acids Research, vol. 41, no. 1, pp. D991–D995, 2013. View at Publisher · View at Google Scholar · View at Scopus
  9. L.-J. Su, C.-W. Chang, Y.-C. Wu et al., “Selection of DDX5 as a novel internal control for Q-RT-PCR from microarray data using a block bootstrap re-sampling scheme,” BMC Genomics, vol. 8, article 140, 2007. View at Publisher · View at Google Scholar · View at Scopus
  10. M. T. Landi, T. Dracheva, M. Rotunno et al., “Gene expression signature of cigarette smoking and its role in lung adenocarcinoma development and survival,” PLoS ONE, vol. 3, no. 2, Article ID e1651, 2008. View at Publisher · View at Google Scholar · View at Scopus
  11. T. Lu, M. Tsai, J. Lee et al., “Identification of a novel biomarker, SEMA5A, for non-small cell lung carcinoma in nonsmoking women,” Cancer Epidemiology Biomarkers and Prevention, vol. 19, no. 10, pp. 2590–2597, 2010. View at Publisher · View at Google Scholar · View at Scopus
  12. T. Y. Wei, C. C. Juan, J. Y. Hisa et al., “Protein arginine methyltransferase 5 is a potential oncoprotein that upregulates G1 cyclins/cyclin-dependent kinases and the phosphoinositide 3-kinase/AKT signaling cascade,” Cancer Science, vol. 103, no. 9, pp. 1640–1650, 2012. View at Publisher · View at Google Scholar · View at Scopus
  13. R. A. Weinberg, The Biology of Cancer, Garland Science, New York, NY, USA, 2nd edition, 2013.
  14. B. Efron and R. Tibshirani, “Empirical Bayes methods and false discovery rates for microarrays,” Genetic Epidemiology, vol. 23, no. 1, pp. 70–86, 2002. View at Publisher · View at Google Scholar · View at Scopus
  15. M. K. Kerr, C. A. Afshari, L. a. Bennett, J. Martinez, and N. J. Walker, “Statistical analysis of a gene expression microarray experiment with replication,” Statistica Sinica, vol. 12, no. 1, pp. 203–217, 2002. View at Google Scholar · View at MathSciNet · View at Scopus
  16. V. G. Tusher, R. Tibshirani, and G. Chu, “Significance analysis of microarrays applied to the ionizing radiation response,” Proceedings of the National Academy of Sciences of the United States of America, vol. 98, no. 9, pp. 5116–5121, 2001. View at Publisher · View at Google Scholar · View at Zentralblatt MATH · View at Scopus
  17. S. Zhang, “A comprehensive evaluation of SAM, the SAM R-package and a simple modification to improve its performance,” BMC Bioinformatics, vol. 8, article 230, 2007. View at Publisher · View at Google Scholar · View at Scopus
  18. B. Efron, R. Tibshirani, J. D. Storey, and V. Tusher, “Empirical Bayes analysis of a microarray experiment,” Journal of the American Statistical Association, vol. 96, no. 456, pp. 1151–1160, 2001. View at Publisher · View at Google Scholar · View at MathSciNet · View at Scopus
  19. B. Efron, “Robbins, empirical Bayes and microarrays,” The Annals of Statistics, vol. 31, no. 2, pp. 366–378, 2003. View at Publisher · View at Google Scholar · View at Zentralblatt MATH · View at MathSciNet · View at Scopus
  20. http://www.bioconductor.org.
  21. R. A. Irizarry, “From CEL files to annotated lists of interesting genes,” in Bioinformatics and Computational Biology Solutions Using R & Bioconductor, pp. 431–442, Springer, New York, NY, USA, 2005. View at Google Scholar
  22. S. T. Chen, H. F. Wu, and K. L. Ng, “A platform for querying breast and prostate cancer-related microNA genes,” in Proceeding of the International Conference on Bioinformatics and Biomedical Engineering (ICBBE '12), pp. 271–274, Shanghai , China, 2012.
  23. A. D. King, N. Pržulj, and I. Jurisica, “Protein complex prediction via cost-based clustering,” Bioinformatics, vol. 20, no. 17, pp. 3013–3020, 2004. View at Publisher · View at Google Scholar · View at Scopus
  24. Y. Qi, F. Balem, C. Faloutsos, J. Klein-Seetharaman, and Z. Bar-Joseph, “Protein complex identification by supervised graph local clustering,” Bioinformatics, vol. 24, no. 13, pp. 250–268, 2008. View at Publisher · View at Google Scholar · View at Scopus
  25. J. B. Pereira-Leal, E. D. Levy, and S. A. Teichmann, “The origins and evolution of functional modules: lessons from protein complexes,” Philosophical Transactions of the Royal Society B: Biological Sciences, vol. 361, no. 1467, pp. 507–517, 2006. View at Publisher · View at Google Scholar · View at Scopus
  26. B. Adamcsek, G. Palla, I. J. Farkas, I. Derényi, and T. Vicsek, “CFinder: locating cliques and overlapping modules in biological networks,” Bioinformatics, vol. 22, no. 8, pp. 1021–1023, 2006. View at Publisher · View at Google Scholar · View at Scopus
  27. J. Wang, B. Liu, M. Li, and Y. Pan, “Identifying protein complexes from interaction networks based on clique percolation and distance restriction,” BMC Genomics, vol. 11, article S10, supplement 2, 2010. View at Publisher · View at Google Scholar · View at Scopus
  28. D. W. Huang, B. T. Sherman, and R. A. Lempicki, “Systematic and integrative analysis of large gene lists using DAVID bioinformatics resources,” Nature Protocols, vol. 4, no. 1, pp. 44–57, 2009. View at Publisher · View at Google Scholar · View at Scopus
  29. Gene Ontology Consortium, “The gene ontology (GO) project in 2006,” Nucleic Acids Research, vol. 34, pp. D322–D326, 2006. View at Google Scholar
  30. A. Kamburov, C. Wierling, H. Lehrach, and R. Herwig, “ConsensusPathDB—a database for integrating human functional interaction networks,” Nucleic Acids Research, vol. 37, no. 1, pp. D623–D628, 2009. View at Publisher · View at Google Scholar · View at Scopus
  31. M. Kuhn, C. von Mering, M. Campillos, L. J. Jensen, and P. Bork, “STITCH: interaction networks of chemicals and proteins,” Nucleic Acids Research, vol. 36, no. 1, pp. D684–D688, 2008. View at Publisher · View at Google Scholar · View at Scopus
  32. T. T. Ashburn and K. B. Thor, “Drug repositioning: identifying and developing new uses for existing drugs,” Nature Reviews Drug Discovery, vol. 3, no. 8, pp. 673–683, 2004. View at Publisher · View at Google Scholar · View at Scopus
  33. F. Iorio, R. Bosotti, E. Scacheri et al., “Discovery of drug mode of action and drug repositioning from transcriptional responses,” Proceedings of the National Academy of Sciences of the United States of America, vol. 107, no. 33, pp. 14621–14626, 2010. View at Publisher · View at Google Scholar · View at Scopus
  34. Z. Wu, Y. Wang, and L. Chen, “Network-based drug repositioning,” Molecular BioSystems, vol. 9, no. 6, pp. 1268–1281, 2013. View at Publisher · View at Google Scholar · View at Scopus
  35. Z. Wu, Y. Wang, and L. Chen, “A new method to identify repositioned drugs for prostate cancer,” in Proceedings of the 6th IEEE International Conference on Systems Biology (ISB '12), pp. 280–284, Xian, China, August 2012. View at Publisher · View at Google Scholar · View at Scopus
  36. S. Y. Sun, Z. P. Liu, T. Zeng, Y. Wang, and L. Chen, “Spatio-temporal analysis of type 2 diabetes mellitus based on differential expression networks,” Scientific Reports, vol. 3, article 2268, 2013. View at Google Scholar
  37. S. Zhao and S. Li, “Network-based relating pharmacological and genomic spaces for drug target identification,” PLoS ONE, vol. 5, no. 7, Article ID e11764, 2010. View at Publisher · View at Google Scholar · View at Scopus
  38. S. Zhao and S. Li, “A co-module approach for elucidating drug-disease associations and revealing their molecular basis,” Bioinformatics, vol. 28, no. 7, pp. 955–961, 2012. View at Publisher · View at Google Scholar · View at Scopus
  39. J. Ahmed, T. Meinel, M. Dunkel et al., “CancerResource: a comprehensive database of cancer-relevant proteins and compound interactions supported by experimental knowledge,” Nucleic Acids Research, vol. 39, no. 1, pp. D960–D967, 2011. View at Publisher · View at Google Scholar · View at Scopus
  40. F. M. Wolf, Meta-Analysis: Quantitative Methods for Research Synthesis, Sage, Thousand Oaks, Calif, USA, 1986.
  41. M. Borenstein, L. V. Hedges, J. P. T. Higgins, and H. R. Rothstein, Introduction to Meta-Analysis, John Wiley & Sons, London, UK, 2009.
  42. B. Breitkreutz, C. Stark, T. Reguly et al., “The BioGRID interaction database: 2008 update,” Nucleic Acids Research, vol. 36, no. 1, pp. D637–D640, 2008. View at Publisher · View at Google Scholar · View at Scopus
  43. D. Croft, G. O'Kelly, G. Wu et al., “Reactome: a database of reactions, pathways and biological processes,” Nucleic Acids Research, vol. 39, no. 1, pp. D691–D697, 2011. View at Publisher · View at Google Scholar · View at Scopus
  44. M. Kanehisa, S. Goto, S. Kawashima, Y. Okuno, and M. Hattori, “The KEGG resource for deciphering the genome,” Nucleic Acids Research, vol. 32, pp. D277–D280, 2004. View at Publisher · View at Google Scholar · View at Scopus
  45. S. A. Frank, Dynamics of Cancer: Incidence, Inheritance, and Evolution, Princeton University Press, Princeton, NJ, USA, 2007.
  46. C. Boccaccio and P. M. Comoglio, “A functional role for hemostasis in early cancer development,” Cancer Research, vol. 65, no. 19, pp. 8579–8582, 2005. View at Publisher · View at Google Scholar · View at Scopus
  47. M. Franchini, M. Montagnana, E. J. Favaloro, and G. Lippi, “The bidirectional relationship of cancer and hemostasis and the potential role of anticoagulant therapy in moderating thrombosis and cancer spread,” Seminars in Thrombosis and Hemostasis, vol. 35, no. 7, pp. 644–653, 2009. View at Publisher · View at Google Scholar · View at Scopus
  48. S. Jain, J. Harris, and J. Ware, “Platelets: linking hemostasis and cancer,” Arteriosclerosis, Thrombosis, and Vascular Biology, vol. 30, no. 12, pp. 2362–2367, 2010. View at Publisher · View at Google Scholar · View at Scopus
  49. D. Garnier, N. Magnus, E. D'Asti et al., “PL-05 genetic pathways linking hemostasis and cancer,” Thrombosis Research, vol. 129, no. 1, pp. S22–S29, 2012. View at Publisher · View at Google Scholar · View at Scopus
  50. J. A. Varner and D. A. Cheresh, “Integrins and cancer,” Current Opinion in Cell Biology, vol. 8, no. 5, pp. 724–730, 1996. View at Publisher · View at Google Scholar · View at Scopus
  51. D. Hanahan and R. A. Weinberg, “The hallmarks of cancer,” Cell, vol. 100, no. 1, pp. 57–70, 2000. View at Publisher · View at Google Scholar · View at Scopus
  52. R. Rathinam and S. K. Alahari, “Important role of integrins in the cancer biology,” Cancer and Metastasis Reviews, vol. 29, no. 1, pp. 223–237, 2010. View at Publisher · View at Google Scholar · View at Scopus
  53. D. Subramani and S. K. Alahari, “Integrin-mediated function of Rab GTPases in cancer progression,” Molecular Cancer, vol. 9, article 312, 2010. View at Publisher · View at Google Scholar · View at Scopus
  54. L. F. Stead, S. Berri, H. M. Wood et al., “The transcriptional consequences of somatic amplifications, deletions, and rearrangements in a human lung squamous cell carcinoma,” Neoplasia, vol. 14, no. 11, pp. 1075–1086, 2012. View at Publisher · View at Google Scholar · View at Scopus
  55. R. J. Gillies, I. Robey, and R. A. Gatenby, “Causes and consequences of increased glucose metabolism of cancers,” Journal of Nuclear Medicine, vol. 49, supplement 2, pp. 24S–42S, 2008. View at Publisher · View at Google Scholar · View at Scopus
  56. R. B. Hamanaka and N. S. Chandel, “Targeting glucose metabolism for cancer therapy,” Journal of Experimental Medicine, vol. 209, no. 2, pp. 211–215, 2012. View at Publisher · View at Google Scholar · View at Scopus
  57. A. Annibaldi and C. Widmann, “Glucose metabolism in cancer cells,” Current Opinion in Clinical Nutrition and Metabolic Care, vol. 13, no. 4, pp. 466–470, 2010. View at Publisher · View at Google Scholar · View at Scopus
  58. R. J. B. King and M. W. Robins, Cancer Biology, Prentice Hall, Upper Saddle River, NJ, USA, 3rd edition, 2006.
  59. G. J. P. L. Kops, B. A. A. Weaver, and D. W. Cleveland, “On the road to cancer: aneuploidy and the mitotic checkpoint,” Nature Reviews Cancer, vol. 5, no. 10, pp. 773–785, 2005. View at Publisher · View at Google Scholar · View at Scopus
  60. J. Nilsson, “Cdc20 control of cell fate during prolonged mitotic arrest: Do Cdc20 protein levels affect cell fate in response to antimitotic compounds?” BioEssays, vol. 33, no. 12, pp. 903–909, 2011. View at Publisher · View at Google Scholar · View at Scopus
  61. A. M. Fry, “Cdc20 turnover rate: a key determinant in cancer patient response to anti-mitotic therapies?” BioEssays, vol. 35, no. 9, pp. 762–762, 2013. View at Publisher · View at Google Scholar · View at Scopus
  62. L. A. Schimmenti, H.-C. Yan, J. A. Madri, and S. M. Albelda, “Platelet endothelial cell adhesion molecule, PECAM-1, modulates cell migration,” Journal of Cellular Physiology, vol. 153, no. 2, pp. 417–428, 1992. View at Publisher · View at Google Scholar · View at Scopus
  63. H. M. DeLisser, M. Christofidou-Solomidou, R. M. Strieter et al., “Involvement of endothelial PECAM-1/CD31 in angiogenesis,” The American Journal of Pathology, vol. 151, no. 3, pp. 671–677, 1997. View at Google Scholar · View at Scopus
  64. N. Ilan and J. A. Madri, “PECAM-1: old friend, new partners,” Current Opinion in Cell Biology, vol. 15, no. 5, pp. 515–524, 2003. View at Publisher · View at Google Scholar · View at Scopus
  65. Y. H. Tan, S. Krishnaswamy, S. Nandi et al., “CBL is frequently altered in lung cancers: its relationship to mutations in met and EGFR tyrosine kinases,” PLoS ONE, vol. 5, no. 1, Article ID e8972, 2010. View at Publisher · View at Google Scholar · View at Scopus
  66. L. Karabon, E. Pawlak, A. Tomkiewicz et al., “CTLA-4, CD28, and ICOS gene polymorphism associations with non-small-cell lung cancer,” Human Immunology, vol. 72, no. 10, pp. 947–954, 2011. View at Publisher · View at Google Scholar · View at Scopus
  67. T. Okegawa, R. Pong, Y. Li, and J. Hsieh, “The role of cell adhesion molecule in cancer progression and its application in cancer therapy,” Acta Biochimica Polonica, vol. 51, no. 2, pp. 445–457, 2004. View at Google Scholar · View at Scopus
  68. N. Makrilia, A. Kollias, L. Manolopoulos, and K. Syrigos, “Cell adhesion molecules: role and clinical significance in cancer,” Cancer Investigation, vol. 27, no. 10, pp. 1023–1037, 2009. View at Publisher · View at Google Scholar · View at Scopus
  69. M. Zigler, A. S. Dobroff, and M. Bar-Eli, “Cell adhesion: Implication in tumor progression,” Minerva Medica, vol. 101, no. 3, pp. 149–162, 2010. View at Google Scholar · View at Scopus
  70. N. Sawada, M. Murata, K. Kikuchi et al., “Tight junctions and human diseases,” Medical Electron Microscopy, vol. 36, no. 3, pp. 147–156, 2003. View at Publisher · View at Google Scholar · View at Scopus
  71. Y. Soini, “Tight junctions in lung cancer and lung metastasis: a review,” International Journal of Clinical and Experimental Pathology, vol. 5, no. 2, pp. 126–136, 2012. View at Google Scholar · View at Scopus
  72. K. Brennan, G. Offiah, E. A. McSherry, and A. M. Hopkins, “Tight junctions: a barrier to the initiation and progression of breast cancer?” Journal of Biomedicine and Biotechnology, vol. 2010, Article ID 460607, 16 pages, 2010. View at Publisher · View at Google Scholar · View at Scopus
  73. T. A. Martin and W. G. Jiang, “Tight junctions and their role in cancer metastasis,” Histology and Histopathology, vol. 16, no. 4, pp. 1183–1195, 2001. View at Google Scholar · View at Scopus
  74. T. A. Martin and W. G. Jiang, “Loss of tight junction barrier function and its role in cancer metastasis,” Biochimica et Biophysica Acta, vol. 1788, no. 4, pp. 872–891, 2009. View at Publisher · View at Google Scholar · View at Scopus
  75. T. A. Martin, M. D. Mason, and W. G. Jiang, “Tight junctions in cancer metastasis,” Frontiers in Bioscience, vol. 16, no. 3, pp. 898–936, 2011. View at Publisher · View at Google Scholar · View at Scopus
  76. J. Liu, X. Y. Yang, and W. J. Shi, “Identifying differentially expressed genes and pathways in two types of non-small cell lung cancer (NSCLC): adenocarcinoma and squamous cell carcinoma,” Genetics and Molecular Research, vol. 13, no. 1, pp. 95–102, 2014. View at Publisher · View at Google Scholar
  77. T. Yohena, I. Yoshino, T. Takenaka et al., “Upregulation of hypoxia-inducible factor-1αmRNA and its clinical significance in non-small cell lung cancer,” Journal of Thoracic Oncology, vol. 4, no. 3, pp. 284–290, 2009. View at Publisher · View at Google Scholar · View at Scopus
  78. A. L. Jackson, B. Zhou, and W. Y. Kim, “HIF, hypoxia and the role of angiogenesis in non-small cell lung cancer,” Expert Opinion on Therapeutic Targets, vol. 14, no. 10, pp. 1047–1057, 2010. View at Publisher · View at Google Scholar · View at Scopus
  79. M. Ioannou, G. Simos, and G. K. Koukoulis, “HIF-1alpha in lung carcinoma: histopathological evidence of hypoxia targets in patient biopsies,” Journal of Solid Tumors, vol. 3, no. 2, pp. 35–43, 2013. View at Google Scholar
  80. M. S. Cline, M. Smoot, E. Cerami et al., “Integration of biological networks and gene expression data using Cytoscape,” Nature Protocols, vol. 2, no. 10, pp. 2366–2382, 2007. View at Publisher · View at Google Scholar · View at Scopus
  81. J. Lamb, E. D. Crawford, D. Peck et al., “The connectivity map: using gene-expression signatures to connect small molecules, genes, and disease,” Science, vol. 313, no. 5795, pp. 1929–1935, 2006. View at Publisher · View at Google Scholar · View at Scopus