Discovery of Biomarkers for Hexachlorobenzene Toxicity Using Population Based Methods on Gene Expression Data

  • Cem Meydan
  • Alper Küçükural
  • Deniz Yörükoğlu
  • O. Uğur Sezerman
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 5265)


Discovering toxicity biomarkers is important in drug discovery to safely evaluate possible toxic effects of a substance in early phases. We tried evolutionary classification methods for selecting the important classifier genes in hexachlorobenzene toxicity using microarray data. Using modified genetic algorithms for selection of minimum number of features for classification of gene expression data, we discovered a number of gene sets of size 4 that were able to discriminate between the control and the hexachlorobenzene (HCB) exposed group of Brown-Norway rats with >99% accuracy in 5-fold cross-validation tests, whereas classification using all of the genes with SVM and other methods yielded results that vary between 48.48% to 81.81%. Making use of this small number of genes as biomarkers may allow us to detect toxicity of substances with mechanisms of toxicity similar to HCB in a fast and cost efficient manner when there are no emerging symptoms.


Feature selection toxicogenomics genetic algorithms biomarker discovery 


  1. 1.
    Küçükural, A., Yeniterzi, R., Yeniterzi, S., Sezerman, O.U.: Evolutionary selection of minimum number of features for classification of gene expression data using genetic algorithms. In: 9th Annual ACM Conference on Genetic Evolutionary Computation, pp. 401–406. ACM Press, New York (2007)Google Scholar
  2. 2.
    Gocmen, A., Peters, H.A., Cripps, D.J., Bryan, G.T., Morris, C.R.: Hexachlorobenzene episode in Turkey. Biomed Environ. Sci. 2(1), 36–43 (1989)PubMedGoogle Scholar
  3. 3.
    International Agency for Research on Cancer, IARC Monographs on the Evaluation of Carcinogenic Risk to Humans, World Health Organisation, vol.79, 493–567 (2001) Google Scholar
  4. 4.
    Collings, F.B., Vaidya, V.S.: Novel technologies for the discovery and quantitation of biomarkers of toxicity. Toxicology (2008) doi:10.1016/j.tox.2007.11.020Google Scholar
  5. 5.
    Tugwood, J.D., Hollins, L.E., Cockerill, M.J.: Genomics and the search for novel biomarkers in toxicology. Biomarkers 8(2), 79–92 (2003)CrossRefPubMedGoogle Scholar
  6. 6.
    Arcellana-Panlilio, M., Robbins, S.M.: Cutting-edge technology: I. Global gene expression profiling using DNA microarrays. Am.J. Physiol. Gastrointest. Liver Physiol. 282, 397–402 (2002)CrossRefGoogle Scholar
  7. 7.
    Geschwind, D.H.: DNA microarrays: translation of the genome from laboratory to clinic. Lancet Neurol. 2, 275–282 (2003)CrossRefPubMedGoogle Scholar
  8. 8.
    Ishkanian, S., Malloff, C.A., Watson, S.K., DeLeeuw, R.J., Chi, B., Coe, B.P., Snijders, A., Albertson, D.G., Pinkel, D., Marra, M.A., Ling, V., MacAulay, C., Lam, W.L.: A tiling resolution DNA microarray with complete coverage of the human genome. Nat.Genet. 36, 299–303 (2004)CrossRefPubMedGoogle Scholar
  9. 9.
    Ichimura, T., Bonventre, J.V., Bailly, V., Wei, H., Hession, C.A., Cate, R.L., Sanicola, M.: Kidney injury molecule-1 (KIM-1), a putative epithelial cell adhesion molecule containing a novel immunoglobulin domain, is up-regulated in renal cells after injury. Biol. Chem. 273, 4135–4142 (1998)CrossRefGoogle Scholar
  10. 10.
    Hubank, M., Schatz, D.G.: Identifying differences in mRNA expression by representational difference analysis of cDNA. Nucleic Acids Res. 22, 5640–5648 (1994)CrossRefPubMedPubMedCentralGoogle Scholar
  11. 11.
    Kruse, J., Stewart, F.A.: Gene expression arrays as a tool to unravel mechanisms of normal tissue radiation injury and prediction of response. World. J. Gastroenterol. 13(19), 2669–2674 (2007)CrossRefPubMedPubMedCentralGoogle Scholar
  12. 12.
    Rokushima, M., Omi, K., Imura, K., Araki, A., Furukawa, N., Itoh, F., Miyazaki, M., Yamamoto, J., Rokushima, M., Okada, M., Torii, M., Kato, I., Ishizaki, J.: Toxicogenomics of drug-induced hemolytic anemia by analyzing gene expression profiles in the spleen. Toxicol Sci. 100(1), 290–302 (2007)CrossRefPubMedGoogle Scholar
  13. 13.
    Huang, Q., Dunn, 2.R.T., Jayadev, S., DiSorbo, O., Pack, F.D., Farr, S.B., Stoll, R.E., Blanchard, K.T.: Assessment of cisplatin-induced nephrotoxicity by microarray technology. Toxicol Sci. 63(2), 196–207 (2001)CrossRefPubMedGoogle Scholar
  14. 14.
    Kiyosawa, N., Uehara, T., Gao, W., Omura, K., Hirode, M., Shimizu, T., Mizukawa, Y., Ono, A., Miyagishima, T., Nagao, T., Urushidani, T.: Identification of glutathione depletion-responsive genes using phorone-treated rat liver. J. Toxicol Sci. 32(5), 469–486 (2007)CrossRefPubMedGoogle Scholar
  15. 15.
    Mendrick, D.L.: Genomic and Genetic Biomarkers of Toxicity. Toxicology (2007)doi:10.1016/j.tox.2007.11.013Google Scholar
  16. 16.
    Ezendam, J., Staedtler, F., Pennings, J., Vandebriel, R.J., Pieters, R., Boffetta, P., Harleman, J.H., Vos, J.G.: Toxicogenomics of subchronic hexachlorobenzene exposure in Brown Norway rats. Environ Health Perspect 112(7), 782–791 (2004)PubMedPubMedCentralGoogle Scholar
  17. 17.
    Schölkopf, B., Smola, A.J.: Learning with Kernels. MIT Press, Cambridge (2002)Google Scholar
  18. 18.
    Duda, R.O., Hart, P.E., Stork, D.G.: Pattern classification, 2nd edn. John Wiley and Sons, New York (2001)Google Scholar
  19. 19.
    Webb, A.R.: Statistical Pattern Recognition. John Wiley and Sons, New York (2002)CrossRefGoogle Scholar
  20. 20.
    Vapnik, V.: Statistical Learning Theory. John Wiley and Sons, New York (1998)Google Scholar
  21. 21.
    Raymer, M., Punch, W., Goodman, E., Kuhn, L., Jain, A.: Dimensionality Reduction Using Genetic Algorithms. IEEE Transactions on Evolutionary computing (2000) Google Scholar
  22. 22.
    Ferri, F.J., Kadirkamanathan, V., Kittler, J.: Feature Subset Search using Genetic Algorithms. In: IEE/IEEE Workshop on Natural Algorithms in Signal Processing, Essex (1993)Google Scholar
  23. 23.
    Richeldi, M., Lanzi, P.: A Tool for Performing effective feature selection by investigating the deep structure of the data. In: Proc. of the International Conference on Tools with Artifcial Intelligence, pp. 102–105 (1996)Google Scholar
  24. 24.
    Witten, H., Frank, E.: Data Mining: Practical machine learning tools and techniques, 2nd edn. Morgan Kaufmann, San Francisco (2005)Google Scholar
  25. 25.
    Alon, U., Barkai, N., Notterman, D., Gish, K., Ybarra, S., Mack, D., Levine, A.: Broad patterns of gene expression revealed by clustering analysis of tumor and normal colon cancer tissues probed by oligonucleotide arrays. Cell Biology 96, 6745–6750 (1999)Google Scholar
  26. 26.
    Fröhlich, H.: Feature Selection for Support Vector Machines by Means of Genetic Algorithms. Diploma Thesis in Computer Science, University Marburg (2002)Google Scholar
  27. 27.
    Dawkins, R.: The Selfish Gene – new edition. Oxford University Press, Oxford (1989)Google Scholar
  28. 28.
    Chang, C., Lin, C.: LIBSVM: a library for support vector machines, Software (2001),
  29. 29.
    EL-Manzalawy, Y., Honavar, V.: WLSVM: Integrating LibSVM into Weka Environment, Software (2005),
  30. 30.
    Brazma, A., Parkinson, H., Sarkans, U., Shojatalab, M., Vilo, J., Abeygunawardena, N., Holloway, E., Kapushesky, M., Kemmeren, P., Lara, G.G., Oezcimen, A., Rocca-Serra, P., Sansone, S.A.: ArrayExpress–a public repository for microarray gene expression data at the EBI. Nucleic Acids Res. 1 31(1), 68–71 (2003)CrossRefGoogle Scholar
  31. 31.
    Extoxnet. The Extension Toxicology Network, P.I.P. Pesticide Information Profiles: Hexachlorobenzene hexachlorobenzene-ext.html (1996a),
  32. 32.
    Michielsen, C., Zeamari, S., Leusink-Muis, A., Vos, J., Bloksma, N.: The environmental pollutant hexachlorobenzene causes eosinophilic and granulomatous inflammation and in vitro airways hyperreactivity in the Brown Norway rat. Arch. Toxicol. 76, 236–247Google Scholar
  33. 33.
    Imai, N., Ichihara, T., Nabae, K., Hagiwara, A., Tamano, S., Shirai T.: Dose Dependent Promoting Effects of Hexachlorobenzene on Hepatocarcinogenesis in a Rat Medium-Term Liver Bioassay. In: Proc. of the 32nd Annual Meeting of CarcinogenicityGoogle Scholar
  34. 34.
    Garner, S.R.: The waikato environment for knowledge analysis. In: Proc. of the New Zealand Computer Science Research Students Conference, pp. 57–64 (1995)Google Scholar
  35. 35.
    Pavlidis, P., Wapinski, I., Noble, W.S.: Support vector machine classification on the web. Bioinformatics 1 20(4), 586–587 (2004)CrossRefGoogle Scholar
  36. 36.
    Liu, G., Loraine, A.E., Shigeta, R., Cline, M., Cheng, J., Chervitz, S.A., Kulp, D., Siani-Rose, M.A.: NetAffx: affymetrix probeset annotations. In: 2002 ACM Symposium on Applied Computing, pp. 147–150. ACM Press, New York (2002)CrossRefGoogle Scholar
  37. 37.
    Kanehisa, M., Araki, M., Goto, S., Hattori, M., Hirakawa, M., Itoh, M., Katayama, T., Kawashima, S., Okuda, S., Tokimatsu, T., Yamanishi, Y.: KEGG for linking genomes to life and the environment. Nucleic Acids Res. 36, 480–484 (2008)CrossRefGoogle Scholar
  38. 38.
    Kanehisa, M., Goto, S., Hattori, M., Aoki-Kinoshita, K.F., Itoh, M., Kawashima, S., Katayama, T., Araki, M., Hirakawa, M.: From genomics to chemical genomics: new developments in KEGG. Nucleic Acids Res. 34, 354–357 (2006)CrossRefGoogle Scholar
  39. 39.
    Kanehisa, M., Goto, S.: KEGG: Kyoto Encyclopedia of Genes and Genomes. Nucleic Acids Res. 28, 27–30 (2000)CrossRefPubMedPubMedCentralGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2008

Authors and Affiliations

  • Cem Meydan
    • 1
  • Alper Küçükural
    • 1
  • Deniz Yörükoğlu
    • 1
  • O. Uğur Sezerman
    • 1
  1. 1.Biological Sciences and BioengineeringSabanci UniversityOrhanlı-TuzlaTürkiye

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