Diagnosis System for Predicting the Centrosome Hyperamplification in Bladder Cancer by Using DNA Microarray Data
Kazuhiro Tokunaga*, Fumiya Kubosaka**, Noriaki Suetake**,
Eiji Uchino*,**, and Hideyasu Matsuyama***
*Fuzzy Logic Systems Institute, 680-41 Kawazu, Iizuka, Fukuoka 820-0067, Japan
**Graduate School of Science and Engineering, Yamaguchi University, 1677-1 Yoshida, Yamaguchi 753-8512, Japan
***Graduate School of Medicine, Yamaguchi University, 1-1-1 Minami-kogushi, Ube 755-8505, Japan
In this paper, we propose a Computer Aided Diagnosis (CAD) system using a DNA microarray data for an early detection of a bladder cancer. In previous works, it is reported that one of the generating factors of the cancer cells is a centrosome hyperamplification. The centrosome hyperamplification is caused by a damage of DNA. Therefore, it is possible to detect the cancer by using the DNA microarray data. In this paper, the CAD system implemented based on a stochastic approach is proposed. The effectiveness of the present system is verified by the actual experiments.
-  Y. Yamamoto, H. Matsuyama, T. Fruya, A. Oga, S. Yoshihiro, M. Okuda, S. Kawauchi, K. Sasaki, and K. Naito, “Centrosome hyperamplification predicts progression and tumor recurrence in bladder cancer,” Clinical Cancer Research, Vol.10, pp. 6449-6455, 2004.
-  Y. Yamamoto, H. Matsuyama, S. Kawauchi, T. Furuya, X. P. Liu, K. Ikemoto, A. Oga, K. Naito, and K. Sasaki, “Biological characteristics in bladder cancer depend on the type of genetic instability,” Clinical Cancer Research, Vol.12, pp. 2752-2758, 2006.
-  Y. Yamamoto, H. Matsuyama, Y. Chochi, M. Okuda, S. Kawauchi, R. Inoue, T. Furuya, A. Oga, K. Naito, and K. Sasaki, “Overexpression of BUBR1 is associated with chromosomal instability in bladder cancer,” Cancer Genetics and Cytogenetics, Vol.174, pp. 42-47, 2007.
-  Y. Yamamoto, S. Eguchi, J. Akao, K. Nagao, S. Sakano, T. Furuya, A. Oga, S. Kawauchi, K. Sasaki, and H. Matsuyama, “Intercellular centrosome number is correlated with the copy number of chromosomes in bladder cancer,” Cancer Genetics and Cytogenetics, Vol.191, pp. 38-42, 2009.
-  T. Kohonen, “Self-organized formation of topologically correct feature maps,” Biological Cybernetics, Vol.43, pp. 59-69, 1982.
-  P. Tamayo, D. Slonim, J. Mesirov, Q. Zhu, S. Kitareewan, E. Dmitrovsky, E. S. Lander, and T. R. Golub, “Interpreting patterns of gene expression with self-organizing maps: Methods and application to hematopoietic differentiation,” Proc. of the National Academy of Sciences of the United States of America, Vol.96, pp. 2907-2912, 1999.
-  P. Toronen, M. Kolehmainen, G. Wong, and E. Castren, “Analysis of gene expression data using self-organizing maps,” FEBS letters, Vol.451, pp. 142-146, 1999.
-  A. Soukas, P. Cohen, N. D. Socci, and J. M. Friedman, “Leptinspecific patterns of gene expression in white adipose tissue,” Genes & Development, Vol.14, pp. 963-980, 2000.
-  S. Tavazoie, J. D. Hughes, M. J. Campbell, R. J. Cho, and G. M. Church, “Systematic determination of genetic network architecture,” Nature genetics, Vol.22, pp. 281-285, 1999.
-  B. J. T. Morgan and A. P. G Ray, “Non-uniqueness and inversions in cluster analysis,” Applied Statistics, Vol.44, pp. 117-134, 1995.
-  D. Shalon, S. Smith, and P. Brown, “A DNA microarray system for analyzing complex DNA samples using two-color fluorescent probe hybridization,” Genome Research, Vol.6, pp. 639-645, 1996.
-  D. Call, “DNA microarrays – their mode of action and possible applications in molecular diagnostics,” Veterinary Sciences Tomorrow, Issue 3, pp. 1-9, 2001.
This article is published under a Creative Commons Attribution-NoDerivatives 4.0 Internationa License.