Abstract
Recent researches of Colorectal Cancer (CRC) aim to look for the answers for its occurrence in the disrupted gene expressions by examining colorectal carcinogenic and healthy tissues with different microarray technologies. In this paper, we propose a novel generative modelling of the Bayes’ classification for the CRC problem in order to differentiate between colorectal cancer stages. The main contribution of this paper is the solution of the distinguishing problem between the critical CRC stages that remained unsolved in the published materials - distinguishing the stage I with stage IV, and stage II with stage III. The Bayesian classifier enabled application of the ’smoothing procedure’ over the data from the third stage, which succeeded to distinguish the probabilities of the mentioned stages. This results are obtained as a continuation of our previous work, where we proposed methodologies for statistical analysis of colorectal gene expression data obtained from the two widely used platforms, Affymetrix and Illumina. Furthermore, the unveiled biomarkers from the two platforms were used in our generative approach for modelling the gene expression probability distribution and were used in the Bayes’ classification system, where we performed binary classifications. This novel approach will help in producing an accurate diagnostics system and precising the actual stage of the cancer. It is of great advantage for early prognosis of the disease and appropriate treatment.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
References
GLOBOCAN (2008), http://globocan.iarc.fr/factsheets/cancers/colorectal.asp
Jain, K.: Applications of biochips: From diagnostics to personalized medicine. Current Opinion in Drug Disc. & Develop. 7(3), 285–289 (2004)
Simjanoska, M., Bogdanova, A.M., Popeska, Z.: Bayesian posterior probability classification of colorectal cancer probed with affymetrix microarray technology. In: Proceedings of the 36th International Convention, MIPRO, CIS Intelligent Systems (2013)
NCI: Colon cancer treatment (2013), http://www.cancer.gov/cancertopics/pdq/treatment/colon/Patient/page2
Eschrich, S., Yang, I., Bloom, G., Kwong, K.Y., Boulware, D., Cantor, A., Coppola, D., Kruhøffer, M., Aaltonen, L., Orntoft, T.F., et al.: Molecular staging for survival prediction of colorectal cancer patients. Journal of Clinical Oncology 23(15), 3526–3535 (2005)
Salazar, R., Roepman, P., Capella, G., Moreno, V., Simon, I., Dreezen, C., Lopez-Doriga, A., Santos, C., Marijnen, C., Westerga, J., et al.: Gene expression signature to improve prognosis prediction of stage ii and iii colorectal cancer. Journal of clinical oncology 29(1), 17–24 (2011)
Donada, M., Bonin, S., Barbazza, R., Pettirosso, D., Stanta, G.: Management of stage ii colon cancer-the use of molecular biomarkers for adjuvant therapy decision. BMC Gastroenterology 13(1), 1–13 (2013)
Ahmed, F.E.: Artificial neural networks for diagnosis and survival prediction in colon cancer. Molecular Cancer 4(1), 29 (2005)
Frederiksen, C.M., Knudsen, S., Laurberg, S., Ørntoft, T.F.: Classification of dukes’ b and c colorectal cancers using expression arrays. Journal of Cancer Research and Clinical Oncology 129(5), 263–271 (2003)
Laibe, S., Lagarde, A., Ferrari, A., Monges, G., Birnbaum, D., Olschwang, the COL2 Project, S.: A seven-gene signature aggregates a subgroup of stage ii colon cancers with stage iii. OMICS: A Journal of Integrative Biology 16(10), 560–565 (2012)
Tsukamoto, S., Ishikawa, T., Iida, S., Ishiguro, M., Mogushi, K., Mizushima, H., Uetake, H., Tanaka, H., Sugihara, K.: Clinical significance of osteoprotegerin expression in human colorectal cancer. Clinical Cancer Research 17(8), 2444–2450 (2011)
Hong, Y., Downey, T., Eu, K.W., Koh, P.K., Cheah, P.Y.: A metastasis-pronesignature for early-stage mismatch-repair proficient sporadic colorectal cancer patients and its implications for possible therapeutics. Clinical & Experimental Metastasis 27(2), 83–90 (2010)
Jorissen, R.N., Gibbs, P., Christie, M., Prakash, S., Lipton, L., Desai, J., Kerr, D., Aaltonen, L.A., Arango, D., Kruhøffer, M., et al.: Metastasis-associated gene expression changes predict poor outcomes in patients with dukes stage b and c colorectal cancer. Clinical Cancer Research 15(24), 7642–7651 (2009)
Schlicker, A., Beran, G., Chresta, C.M., McWalter, G., Pritchard, A., Weston, S., Runswick, S., Davenport, S., Heathcote, K., Castro, D.A., et al.: Subtypes of primary colorectal tumors correlate with response to targeted treatment in colorectal cell lines. BMC Medical Genomics 5(1), 66 (2012)
OConnell, J.B., Maggard, M.A., Ko, C.Y.: Colon cancer survival rates with the new american joint committee on cancer sixth edition staging. Journal of the National Cancer Institute 96(19), 1420–1425 (2004)
Gene expression omnibus (2013), http://www.ncbi.nlm.nih.gov/geo/
MayoClinic: Colon cancer (2013), http://www.mayoclinic.com/health/colon-cancer/DS00035/DSECTION=tests-and-diagnosis
Wu, Z., Aryee, M.: Subset quantile normalization using negative control features. Journal of Computational Biology 17(10), 1385–1395 (2010)
Hui, Y., Kang, T., Xie, L., Yuan-Yuan, L.: Digout: Viewing differential expression genes as outliers. Journal of Bioinformatics and Computational Biology 8(supp. 01), 161–175 (2010)
Tamayo, P., Slonim, D., Mesirov, J., Zhu, Q., Kitareewan, S., Dmitrovsky, E., Lander, E.S., Golub, T.R.: Interpreting patterns of gene expression with self-organizing maps: Methods and application to hematopoietic differentiation. Proceedings of the National Academy of Sciences 96(6), 2907–2912 (1999)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2014 Springer International Publishing Switzerland
About this paper
Cite this paper
Simjanoska, M., Bogdanova, A.M., Popeska, Z. (2014). Bayesian Multiclass Classification of Gene Expression Colorectal Cancer Stages. In: Trajkovik, V., Anastas, M. (eds) ICT Innovations 2013. ICT Innovations 2013. Advances in Intelligent Systems and Computing, vol 231. Springer, Heidelberg. https://doi.org/10.1007/978-3-319-01466-1_17
Download citation
DOI: https://doi.org/10.1007/978-3-319-01466-1_17
Publisher Name: Springer, Heidelberg
Print ISBN: 978-3-319-01465-4
Online ISBN: 978-3-319-01466-1
eBook Packages: EngineeringEngineering (R0)