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BioMed Research International
Volume 2015, Article ID 964795, 8 pages
http://dx.doi.org/10.1155/2015/964795
Research Article

Identification of Novel Thyroid Cancer-Related Genes and Chemicals Using Shortest Path Algorithm

1Department of Surgery, China-Japan Union Hospital of Jilin University, Changchun 130033, China
2The Key Laboratory of Stem Cell Biology, Institute of Health Sciences, Shanghai Institutes for Biological Sciences, Chinese Academy of Sciences, Shanghai 200031, China

Received 20 September 2014; Accepted 5 December 2014

Academic Editor: Tao Huang

Copyright © 2015 Yang Jiang 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. Y. E. Nikiforov and M. N. Nikiforova, “Molecular genetics and diagnosis of thyroid cancer,” Nature Reviews Endocrinology, vol. 7, no. 10, pp. 569–580, 2011. View at Publisher · View at Google Scholar · View at Scopus
  2. M. A. Ginos, G. P. Page, B. S. Michalowicz et al., “Identification of a gene expression signature associated with recurrent disease in squamous cell carcinoma of the head and neck,” Cancer Research, vol. 64, no. 1, pp. 55–63, 2004. View at Publisher · View at Google Scholar · View at Scopus
  3. A. Mathur, W. Moses, R. Rahbari et al., “Higher rate of BRAF mutation in papillary thyroid cancer over time: a single-institution study,” Cancer, vol. 117, no. 19, pp. 4390–4395, 2011. View at Publisher · View at Google Scholar · View at Scopus
  4. S. Chevillard, N. Ugolin, P. Vielh et al., “Gene expression profiling of differentiated thyroid neoplasms: diagnostic and clinical implications,” Clinical Cancer Research, vol. 10, no. 19, pp. 6586–6597, 2004. View at Publisher · View at Google Scholar · View at Scopus
  5. A. Baccarelli and V. Bollati, “Epigenetics and environmental chemicals,” Current Opinion in Pediatrics, vol. 21, no. 2, pp. 243–251, 2009. View at Publisher · View at Google Scholar · View at Scopus
  6. A. Prüss-Ustün, C. Vickers, P. Haefliger, and R. Bertollini, “Knowns and unknowns on burden of disease due to chemicals: a systematic review,” Environmental Health: A Global Access Science Source, vol. 10, no. 1, article 9, 2011. View at Publisher · View at Google Scholar · View at Scopus
  7. S. Chanda, U. B. Dasgupta, D. GuhaMazumder et al., “DNA hypermethylation of promoter of gene p53 and p16 in arsenic-exposed people with and without malignancy,” Toxicological Sciences, vol. 89, no. 2, pp. 431–437, 2006. View at Publisher · View at Google Scholar · View at Scopus
  8. C. J. Marsit, K. Eddy, and K. T. Kelsey, “MicroRNA responses to cellular stress,” Cancer Research, vol. 66, no. 22, pp. 10843–10848, 2006. View at Publisher · View at Google Scholar · View at Scopus
  9. J. M. Stuart, E. Segal, D. Koller, and S. K. Kim, “A gene-coexpression network for global discovery of conserved genetic modules,” Science, vol. 302, no. 5643, pp. 249–255, 2003. View at Publisher · View at Google Scholar · View at Scopus
  10. L. Chen, B.-Q. Li, and K.-Y. Feng, “Predicting biological functions of protein complexes using graphic and functional features,” Current Bioinformatics, vol. 8, no. 5, pp. 545–551, 2013. View at Publisher · View at Google Scholar · View at Scopus
  11. D. Warde-Farley, S. L. Donaldson, O. Comes et al., “The GeneMANIA prediction server: biological network integration for gene prioritization and predicting gene function,” Nucleic Acids Research, vol. 38, no. 2, Article ID gkq537, pp. W214–W220, 2010. View at Publisher · View at Google Scholar · View at Scopus
  12. L. Chen, W.-M. Zeng, Y.-D. Cai, and T. Huang, “Prediction of metabolic pathway using graph property, chemical functional group and chemical structural set,” Current Bioinformatics, vol. 8, no. 2, pp. 200–207, 2013. View at Publisher · View at Google Scholar · View at Scopus
  13. D. Marbach, J. C. Costello, R. Küffner et al., “Wisdom of crowds for robust gene network inference,” Nature Methods, vol. 9, no. 8, pp. 796–804, 2012. View at Publisher · View at Google Scholar · View at Scopus
  14. T. Huang, L. Chen, Y.-D. Cai, and K.-C. Chou, “Classification and analysis of regulatory pathways using graph property, biochemical and physicochemical property, and functional property,” PLoS ONE, vol. 6, no. 9, Article ID e25297, 2011. View at Publisher · View at Google Scholar · View at Scopus
  15. L. Chen, W.-M. Zeng, Y.-D. Cai, K.-Y. Feng, and K.-C. Chou, “Predicting anatomical therapeutic chemical (ATC) classification of drugs by integrating chemical-chemical interactions and similarities,” PLoS ONE, vol. 7, no. 4, Article ID e35254, 2012. View at Publisher · View at Google Scholar · View at Scopus
  16. Y. C. Wang, N. Deng, S. Chen, and Y. Wang, “Computational study of drugs by integrating omics data with kernel methods,” Molecular Informatics, vol. 32, no. 11-12, pp. 930–941, 2013. View at Publisher · View at Google Scholar · View at Scopus
  17. Y. Yamanishi, M. Araki, A. Gutteridge, W. Honda, and M. Kanehisa, “Prediction of drug-target interaction networks from the integration of chemical and genomic spaces,” Bioinformatics, vol. 24, no. 13, pp. i232–i240, 2008. View at Publisher · View at Google Scholar · View at Scopus
  18. L. Chen, J. Lu, T. Huang et al., “Finding candidate drugs for hepatitis C based on chemical-chemical and chemical-protein interactions,” PLoS ONE, vol. 9, no. 9, Article ID e107767, 2014. View at Publisher · View at Google Scholar
  19. F. Napolitano, Y. Zhao, V. M. Moreira et al., “Drug repositioning: a machine-learning approach through data integration,” Journal of Cheminformatics, vol. 5, article 30, 2013. View at Publisher · View at Google Scholar · View at Scopus
  20. L. Chen, J. Lu, N. Zhang, T. Huang, and Y.-D. Cai, “A hybrid method for prediction and repositioning of drug anatomical therapeutic chemical classes,” Molecular BioSystems, vol. 10, no. 4, pp. 868–877, 2014. View at Publisher · View at Google Scholar · View at Scopus
  21. L. Wu, N. Ai, Y. Liu, Y. Wang, and X. Fan, “Relating anatomical therapeutic indications by the ensemble similarity of drug sets,” Journal of Chemical Information and Modeling, vol. 53, no. 8, pp. 2154–2160, 2013. View at Publisher · View at Google Scholar · View at Scopus
  22. B.-Q. Li, T. Huang, L. Liu, Y.-D. Cai, and K.-C. Chou, “Identification of colorectal cancer related genes with mRMR and shortest path in protein-protein interaction network,” PLoS ONE, vol. 7, no. 4, Article ID e33393, 2012. View at Publisher · View at Google Scholar · View at Scopus
  23. M. Jiang, Y. Chen, Y. Zhang et al., “Identification of hepatocellular carcinoma related genes with k-th shortest paths in a protein-protein interaction network,” Molecular BioSystems, vol. 9, no. 11, pp. 2720–2728, 2013. View at Publisher · View at Google Scholar · View at Scopus
  24. J. Zhang, M. Jiang, F. Yuan et al., “Identification of age-related macular degeneration related genes by applying shortest path algorithm in protein-protein interaction network,” BioMed Research International, vol. 2013, Article ID 523415, 8 pages, 2013. View at Publisher · View at Google Scholar · View at Scopus
  25. Y.-F. Gao, Y. Shu, L. Yang et al., “A graphic method for identification of novel glioma related genes,” BioMed Research International, vol. 2014, Article ID 891945, 8 pages, 2014. View at Publisher · View at Google Scholar
  26. M. Zhao, J. Sun, and Z. Zhao, “TSGene: a web resource for tumor suppressor genes,” Nucleic Acids Research, vol. 41, no. 1, pp. D970–D976, 2013. View at Publisher · View at Google Scholar · View at Scopus
  27. UniProt Consortium, “Update on activities at the Universal Protein Resource (UniProt) in 2013,” Nucleic Acids Research, vol. 41, pp. D43–D47, 2012. View at Publisher · View at Google Scholar
  28. S. McNeil, A. Budhu, N. Grantees et al., Imaging, National Cancer Institute, 2013.
  29. A. P. Davis, C. G. Murphy, R. Johnson et al., “The comparative toxicogenomics database: update 2013,” Nucleic Acids Research, vol. 41, no. 1, pp. D1104–D1114, 2013. View at Publisher · View at Google Scholar · View at Scopus
  30. Y. F. Gao, L. Chen, Y. D. Cai, K. Y. Feng, T. Huang, and Y. Jiang, “Predicting metabolic pathways of small molecules and enzymes based on interaction information of chemicals and proteins,” PLoS ONE, vol. 7, no. 9, Article ID e45944, 2012. View at Publisher · View at Google Scholar · View at Scopus
  31. L. J. Jensen, M. Kuhn, M. Stark et al., “STRING 8—a global view on proteins and their functional interactions in 630 organisms,” Nucleic Acids Research, vol. 37, no. 1, pp. D412–D416, 2009. View at Publisher · View at Google Scholar · View at Scopus
  32. L. Hu, T. Huang, X.-J. Liu, and Y.-D. Cai, “Predicting protein phenotypes based on protein-protein interaction network,” PLoS ONE, vol. 6, no. 3, Article ID e17668, 2011. View at Publisher · View at Google Scholar · View at Scopus
  33. L.-L. Hu, T. Huang, Y.-D. Cai, and K.-C. Chou, “Prediction of body fluids where proteins are secreted into based on protein interaction network,” PLoS ONE, vol. 6, no. 7, Article ID e22989, 2011. View at Publisher · View at Google Scholar · View at Scopus
  34. P. Gao, Q.-P. Wang, L. Chen, and T. Huang, “Prediction of human genes' regulatory functions based on proteinprotein interaction network,” Protein and Peptide Letters, vol. 19, no. 9, pp. 910–916, 2012. View at Publisher · View at Google Scholar · View at Scopus
  35. L. L. Hu, T. Huang, X. Shi, W.-C. Lu, Y.-D. Cai, and K.-C. Chou, “Predicting functions of proteins in mouse based on weighted protein-protein interaction network and protein hybrid properties,” PLoS ONE, vol. 6, no. 1, Article ID e14556, 2011. View at Publisher · View at Google Scholar · View at Scopus
  36. 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
  37. M. Kanehisa and S. Goto, “KEGG: kyoto encyclopedia of genes and genomes,” Nucleic Acids Research, vol. 28, no. 1, pp. 27–30, 2000. View at Publisher · View at Google Scholar · View at Scopus
  38. T. H. Gormen, C. E. Leiserson, R. L. Rivest, and C. Stein, Eds., Introduction to Algorithms, MIT Press, Cambridge, Mass, USA, 1990.
  39. D. W. Huang, B. T. Sherman, and R. A. Lempicki, “Bioinformatics enrichment tools: paths toward the comprehensive functional analysis of large gene lists,” Nucleic Acids Research, vol. 37, no. 1, pp. 1–13, 2009. View at Publisher · View at Google Scholar · View at Scopus
  40. A. A. Bapat, G. Hostetter, D. D. von Hoff, and H. Han, “Perineural invasion and associated pain in pancreatic cancer,” Nature Reviews Cancer, vol. 11, no. 10, pp. 695–707, 2011. View at Publisher · View at Google Scholar · View at Scopus
  41. D. Hanahan and R. A. Weinberg, “Hallmarks of cancer: the next generation,” Cell, vol. 144, no. 5, pp. 646–674, 2011. View at Publisher · View at Google Scholar · View at Scopus
  42. T. Kurosaki, M. Suzuki, Y. Enomoto et al., “Polymorphism of cytochrome P450 2B6 and prostate cancer risk: a significant association in a Japanese population,” International Journal of Urology, vol. 16, no. 4, pp. 364–368, 2009. View at Publisher · View at Google Scholar · View at Scopus
  43. J. Kumagai, T. Fujimura, S. Takahashi et al., “Cytochrome P450 2B6 is a growth-inhibitory and prognostic factor for prostate cancer,” The Prostate, vol. 67, no. 10, pp. 1029–1037, 2007. View at Publisher · View at Google Scholar · View at Scopus
  44. R. E. Page, A. J. P. Klein-Szanto, S. Litwin et al., “Increased expression of the pro-protein convertase furin predicts decreased survival in ovarian cancer,” Cellular Oncology, vol. 29, no. 4, pp. 289–299, 2007. View at Google Scholar · View at Scopus
  45. D. E. Bassi, H. Mahloogi, R. L. de Cicco, and A. Klein-Szanto, “Increased furin activity enhances the malignant phenotype of human head and neck cancer cells,” The American Journal of Pathology, vol. 162, no. 2, pp. 439–447, 2003. View at Publisher · View at Google Scholar · View at Scopus
  46. D. E. Bassi, R. L. de Cicco, H. Mahloogi, S. Zucker, G. Thomas, and A. J. P. Klein-Szanto, “Furin inhibition results in absent or decreased invasiveness and tumorigenicity of human cancer cells,” Proceedings of the National Academy of Sciences of the United States of America, vol. 98, no. 18, pp. 10326–10331, 2001. View at Publisher · View at Google Scholar · View at Scopus
  47. R.-N. Chen, Y.-H. Huang, Y.-C. Lin et al., “Thyroid hormone promotes cell invasion through activation of furin expression in human hepatoma cell lines,” Endocrinology, vol. 149, no. 8, pp. 3817–3831, 2008. View at Publisher · View at Google Scholar · View at Scopus
  48. M. Cook, X.-M. Yu, and H. Chen, “Notch in the development of thyroid C-cells and the treatment of medullary thyroid cancer,” American Journal of Translational Research, vol. 2, no. 1, pp. 119–125, 2010. View at Google Scholar · View at Scopus
  49. A. Verma, S. L. Warner, H. Vankayalapati, D. J. Bearss, and S. Sharma, “Targeting Axl and Mer kinases in cancer,” Molecular Cancer Therapeutics, vol. 10, no. 10, pp. 1763–1773, 2011. View at Publisher · View at Google Scholar · View at Scopus
  50. C. T. Cummings, D. DeRyckere, H. S. Earp, and D. K. Graham, “Molecular pathways: MERTK signaling in cancer,” Clinical Cancer Research, vol. 19, no. 19, pp. 5275–5280, 2013. View at Publisher · View at Google Scholar · View at Scopus
  51. K. Q. Nguyen, W. I. Tsou, D. A. Calarese et al., “Overexpression of MERTK receptor tyrosine kinase in epithelial cancer cells drives efferocytosis in a gain-of-function capacity,” The Journal of Biological Chemistry, vol. 289, no. 37, pp. 25737–25749, 2014. View at Publisher · View at Google Scholar
  52. K. L. Mine, N. Shulzhenko, A. Yambartsev et al., “Gene network reconstruction reveals cell cycle and antiviral genes as major drivers of cervical cancer,” Nature Communications, vol. 4, article 1806, 2013. View at Publisher · View at Google Scholar · View at Scopus
  53. L. Sabatino, A. Fucci, M. Pancione et al., “UHRF1 coordinates peroxisome proliferator activated receptor gamma (PPARG) epigenetic silencing and mediates colorectal cancer progression,” Oncogene, vol. 31, no. 49, pp. 5061–5072, 2012. View at Publisher · View at Google Scholar · View at Scopus
  54. R. Pestell, L. Tian, C. Wang et al., “Abstract P2-06-02: Pparg deacetylation by SIRT1 determines breast tumor lipid synthesis and growth,” Cancer Research, vol. 73, p. P2-06-02, 2014. View at Publisher · View at Google Scholar
  55. O. Veiseh, F. M. Kievit, R. G. Ellenbogen, and M. Zhang, “Cancer cell invasion: treatment and monitoring opportunities in nanomedicine,” Advanced Drug Delivery Reviews, vol. 63, no. 8, pp. 582–596, 2011. View at Publisher · View at Google Scholar · View at Scopus
  56. B. R. Haas and H. Sontheimer, “Inhibition of the sodium-potassium-chloride cotransporter isoform-1 reduces glioma invasion,” Cancer Research, vol. 70, no. 13, pp. 5597–5606, 2010. View at Publisher · View at Google Scholar · View at Scopus
  57. S. Kumar, J. Huang, J. R. Cushnir, P. Španěl, D. Smith, and G. B. Hanna, “Selected ion flow tube-MS analysis of headspace vapor from gastric content for the diagnosis of gastro-esophageal cancer,” Analytical Chemistry, vol. 84, no. 21, pp. 9550–9557, 2012. View at Publisher · View at Google Scholar · View at Scopus
  58. P. J. Branton, K. G. McAdam, D. B. Winter, C. Liu, M. G. Duke, and C. J. Proctor, “Reduction of aldehydes and hydrogen cyanide yields in mainstream cigarette smoke using an amine functionalised ion exchange resin,” Chemistry Central Journal, vol. 5, no. 1, article 15, 2011. View at Publisher · View at Google Scholar · View at Scopus
  59. T. Carreón, M. Hein, K. Hanley, S. Viet, and A. Ruder, “0094Bladder cancer incidence among workers exposed to o-toluidine, aniline and nitrobenzene at a rubber chemical manufacturing plant,” Occupational & Environmental Medicine, vol. 71, pp. A9–A10, 2014. View at Publisher · View at Google Scholar
  60. S. Kannan, R. Fielder, J. Tristan, E. Longoria, and A. Castillon, “Molecular mechanism of aniline induced bladder cancer,” The FASEB Journal, vol. 27, pp. 793–791, 2013. View at Publisher · View at Google Scholar