Abstract
The assessment of non-genotoxic hepatocarcinogenicity of chemicals is currently based on 2-year rodent bioassays. It is desirable to develop a fast and effective method to accelerate the identification of potential hepatocarcinogenicity of non-genotoxic chemicals. In this study, a novel method CPI is proposed to predict potential hepatocarcinogenicity of non-genotoxic chemicals. The CPI method is based on chemical-protein interactions and interpretable decision tree classifiers.The interpretable rules generated by the CPI method are analyzed to provide insights into the mechanism and biomarkers of non-genotoxic hepatocarcinogenicity. The CPI method with an independent test accuracy of 86% using only 1 protein biomarker outperforms the state-of-the-art methods of gene expression profile-based toxicogenomics using 90 gene biomarkers. A protein ABCC3 was identified as a potential protein biomarker for further exploration. This study presents the potential application of CPI method for assessing non-genotoxic hepatocarcinogenicity of chemicals.
Chapter PDF
Similar content being viewed by others
Keywords
References
Hayashi, Y.: Overview of genotoxic carcinogens and non-genotoxic carcinogens. Exp. Toxicol. Pathol. 44, 465–471 (1992)
Melnick, R.L., Kohn, M.C., Portier, C.J.: Implications for risk assessment of suggested nongenotoxic mechanisms of chemical carcinogenesis. Environ. Health Perspect. 104 (suppl. 1), 123–134 (1996)
Weisburger, J.H., Williams, G.M.: The distinction between genotoxic and epigenetic carcinogens and implication for cancer risk. Toxicol. Sci. 57, 4–5 (2000)
Gold, L.S., Manley, N.B., Slone, T.H., Rohrbach, L., Garfinkel, G.B.: Supplement to the carcinogenic potency database (cpdb): results of animal bioassays published in the general literature through 1997 and by the national toxicology program in 1997-1998. Toxicol. Sci. 85, 747–808 (2005)
Kar, S., Deeb, O., Roy, K.: Development of classification and regression based qsar models to predict rodent carcinogenic potency using oral slope factor. Ecotoxicol. Environ. Saf. 82, 85–95 (2012)
Kar, S., Roy, K.: First report on development of quantitative interspecies structure-carcinogenicity relationship models and exploring discriminatory features for rodent carcinogenicity of diverse organic chemicals using oecd guidelines. Chemosphere 87, 339–355 (2012)
Tanabe, K., Kurita, T., Nishida, K., Lucic, B., Amic, D., Suzuki, T.: Improvement of carcinogenicity prediction performances based on sensitivity analysis in variable selection of svm models. SAR QSAR Environ. Res. (2013)
Yuan, J., Pu, Y., Yin, L.: Qsar study of liver specificity of carcinogenicity of n-nitroso compounds. Ecotoxicol. Environ. Saf. 84, 282–292 (2012)
Liu, Z., Kelly, R., Fang, H., Ding, D., Tong, W.: Comparative analysis of predictive models for nongenotoxic hepatocarcinogenicity using both toxicogenomics and quantitative structure-activity relationships. Chem. Res. Toxicol. 24, 1062–1070 (2011)
Yamada, F., Sumida, K., Uehara, T., Morikawa, Y., Yamada, H., Urushidani, T., Ohno, Y.: Toxicogenomics discrimination of potential hepatocarcinogenicity of non-genotoxic compounds in rat liver. J. Appl. Toxicol. (2012)
Uehara, T., Hirode, M., Ono, A., Kiyosawa, N., Omura, K., Shimizu, T., Mizukawa, Y., Miyagishima, T., Nagao, T., Urushidani, T.: A toxicogenomics approach for early assessment of potential non-genotoxic hepatocarcinogenicity of chemicals in rats. Toxicology 250, 15–26 (2008)
Kuhn, M., von Mering, C., Campillos, M., Jensen, L.J., Bork, P.: Stitch: interaction networks of chemicals and proteins. Nucleic Acids Res. 36, D684–D688 (2008)
Kuhn, M., Szklarczyk, D., Franceschini, A., Campillos, M., von Mering, C., Jensen, L.J., Beyer, A., Bork, P.: Stitch 2: an interaction network database for small molecules and proteins. Nucleic Acids Res. 38, D552–D556 (2010)
Kuhn, M., Szklarczyk, D., Franceschini, A., von Mering, C., Jensen, L.J., Bork, P.: Stitch 3: zooming in on protein-chemical interactions. Nucleic Acids Res. 40, D876–D880 (2012)
Kim Kjaerulff, S., Wich, L., Kringelum, J., Jacobsen, U.P., Kouskoumvekaki, I., Audouze, K., Lund, O., Brunak, S., Oprea, T.I., Taboureau, O.: Chemprot-2.0: visual navigation in a disease chemical biology database. Nucleic Acids Res. 41, 464–469 (2013)
Taboureau, O., Nielsen, S.K., Audouze, K., Weinhold, N., Edsgard, D., Roque, F.S., Kouskoumvekaki, I., Bora, A., Curpan, R., Jensen, T.S., Brunak, S., Oprea, T.I.: Chemprot: a disease chemical biology database. Nucleic Acids Res. 39, D367–D372 (2011)
Mattingly, C.J., Colby, G.T., Forrest, J.N., Boyer, J.L.: The comparative toxicogenomics database (ctd). Environ. Health Perspec.t 111, 793–795 (2003)
Young, J., Tong, W., Fang, H., Xie, Q., Pearce, B., Hashemi, R., Beger, R., Cheeseman, M., Chen, J., Chang, Y.C., Kodell, R.: Building an organ-specific carcinogenic database for sar analyses. J. Toxicol. Environ. Health A 67, 1363–1389 (2004)
von Mering, C., Jensen, L.J., Snel, B., Hooper, S.D., Krupp, M., Foglierini, M., Jouffre, N., Huynen, M.A., Bork, P.: String: known and predicted protein-protein associations, integrated and transferred across organisms. Nucleic Acids Res. 33, D433–D437 (2005)
Tung, C.W., Ho, S.Y.: Popi: predicting immunogenicity of mhc class i binding peptides by mining informative physicochemical properties. Bioinformatics 23, 942–949 (2007)
Tung, C.W., Ho, S.Y.: Computational identification of ubiquitylation sites from protein sequences. BMC Bioinformatics 9, 310 (2008)
Liaw, C., Tung, C.W., Ho, S.J., Ho, S.Y.: Sequence-based prediction of gamma-turn types using a physicochemical property-based decision tree method. Proceeding of World Academy of Science, Engineering and Technology 41, 898–902 (2010)
Huang, W.L., Tung, C.W., Ho, S.W., Ho, S.Y.: Proloc-rgo: Using rule-based knowledge with gene ontology terms for prediction of protein subnuclear localization. In: IEEE Symposium on Computational Intelligence in Bioinformatics and Computational Biology, CIBCB 2008, pp. 201–206. IEEE (2008)
Quinlan, J.: C4. 5: programs for machine learning. Morgan kaufmann (1993)
Kuhn, M., Weston, S.: code for C5.0 by R. Quinlan, N.C.C.: C50: C5.0 Decision Trees and Rule-Based Models (2012); R package version 0.1.0-013
Ding, C., Peng, H.: Minimum redundancy feature selection from microarray gene expression data. J. Bioinform. Comput. Biol. 3, 185–205 (2005)
Kryston, T.B., Georgiev, A.B., Pissis, P., Georgakilas, A.G.: Role of oxidative stress and dna damage in human carcinogenesis. Mutat. Res. 711, 193–201 (2011)
Ziech, D., Franco, R., Georgakilas, A.G., Georgakila, S., Malamou-Mitsi, V., Schoneveld, O., Pappa, A., Panayiotidis, M.I.: The role of reactive oxygen species and oxidative stress in environmental carcinogenesis and biomarker development. Chem. Biol. Interact. 188, 334–339 (2010)
Ponka, P., Beaumont, C., Richardson, D.R.: Function and regulation of transferrin and ferritin. Semin. Hematol. 35, 35–54 (1998)
McCord, J.M.: Iron, free radicals, and oxidative injury. Semin. Hematol. 35, 5–12 (1998)
Linn, S.: Dna damage by iron and hydrogen peroxide in vitro and in vivo. Drug Metab. Rev. 30, 313–326 (1998)
Gill, J.H., Brickell, P., Dive, C., Roberts, R.A.: The rodent non-genotoxic hepatocarcinogen nafenopin suppresses apoptosis preferentially in non-cycling hepatocytes but also elevates cdk4, a cell cycle progression factor. Carcinogenesis 19, 1743–1747 (1998)
Weinberg, R.A.: The retinoblastoma protein and cell cycle control. Cell 81, 323–330 (1995)
Butterworth, B.E., Bogdanffy, M.S.: A comprehensive approach for integration of toxicity and cancer risk assessments. Regul. Toxicol. Pharmacol. 29, 23–36 (1999)
Nguyen-Ba, G., Vasseur, P.: Epigenetic events during the process of cell transformation induced by carcinogens (review). Oncol. Rep. 6, 925–932 (1999)
Silva Lima, B., Van der Laan, J.W.: Mechanisms of nongenotoxic carcinogenesis and assessment of the human hazard. Regul. Toxicol. Pharmacol. 32, 135–143 (2000)
Williams, G.M., Iatropoulos, M.J., Weisburger, J.H.: Chemical carcinogen mechanisms of action and implications for testing methodology. Exp. Toxicol. Pathol. 48, 101–111 (1996)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2013 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Tung, CW. (2013). Prediction of Non-genotoxic Hepatocarcinogenicity Using Chemical-Protein Interactions. In: Ngom, A., Formenti, E., Hao, JK., Zhao, XM., van Laarhoven, T. (eds) Pattern Recognition in Bioinformatics. PRIB 2013. Lecture Notes in Computer Science(), vol 7986. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-39159-0_21
Download citation
DOI: https://doi.org/10.1007/978-3-642-39159-0_21
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-39158-3
Online ISBN: 978-3-642-39159-0
eBook Packages: Computer ScienceComputer Science (R0)
