Prediction Model for the Cellular Immortalization and Transformation Potentials of Cell Substrates

  • Lee, Min-Su (Department of Computer Science and Engineering, Ewha Womans University) ;
  • Matthews Clayton A. (Human Genome Research Institute and Department of Biochemistry and Molecular Biology, Seoul National University College of Medicine) ;
  • Chae Min-Ju (Human Genome Research Institute and Department of Biochemistry and Molecular Biology, Seoul National University College of Medicine) ;
  • Choi, Jung-Yun (Biologics Headquater, Korea Food and Drug Administration) ;
  • Sohn Yeo-Won (Biologics Headquater, Korea Food and Drug Administration) ;
  • Kim, Min-Jung (Laboratory of Radiation Experimental Therapeutics, Korea Institute of Radiological & Medical Sciences) ;
  • Lee, Su-Jae (Laboratory of Radiation Experimental Therapeutics, Korea Institute of Radiological & Medical Sciences) ;
  • Park, Woong-Yang (Human Genome Research Institute and Department of Biochemistry and Molecular Biology, Seoul National University College of Medicine)
  • 발행 : 2006.12.31

초록

The establishment of DNA microarray technology has enabled high-throughput analysis and molecular profiling of various types of cancers. By using the gene expression data from microarray analysis we are able to investigate diagnostic applications at the molecular level. The most important step in the application of microarray technology to cancer diagnostics is the selection of specific markers from gene expression profiles. In order to select markers of Immortalization and transformation we used c-myc and $H-ras^{V12}$ oncogene-transfected NIH3T3 cells as our model system. We have identified 8751 differentially expressed genes in the immortalization/transformation model by multivariate permutation F-test (95% confidence, FDR<0.01). Using the support vector machine algorithm, we selected 13 discriminative genes which could be used to predict immortalization and transformation with perfect accuracy. We assayed $H-ras^{V12}$-transfected 'transformed' cells to validate our immortalization/transformation dassification system. The selected molecular markers generated valuable additional information for tumor diagnosis, prognosis and therapy development.

키워드

참고문헌

  1. Ayllon, V. and Rebollo, A. (2000). Ras-induced cellular events. Mol Membr Biol. 17, 65-73 https://doi.org/10.1080/09687680050117093
  2. Bodnar, A.G., Ouellette, M., Frolkis, M., Holt, S.E., Chiu, C.P., Morin, G.B., Harley, C.B., Shay, J.W., Lichtsteiner, S., and Wright, W.E. (1998). Extension of life-span by introduction of telomerase into normal human cells. Science 279, 349-352 https://doi.org/10.1126/science.279.5349.349
  3. Bos, J.L. (1989). Ras oncogenes in human cancer. Cancer Res. 49, 4682-4689
  4. Bouchard, C., Staller, P., and Eilers, M. (1998). Control of cell proliferation by Myc. Trends Cell Biol. 8, 202-206 https://doi.org/10.1016/S0962-8924(98)01251-3
  5. Greider, C.W. (1999). Telomerase activation. One step on the road to cancer? Trends Genet. 15, 109-112 https://doi.org/10.1016/S0168-9525(98)01681-3
  6. Guyon, I., Weston, J., Barnhill, S., and Vapnik, V. (2002). Gene selection for cancer classification using support vector machines. Machine Learning 46, 389-422 https://doi.org/10.1023/A:1012487302797
  7. Hayflick, L. (1965). The limited in vitro lifetime of human diploid cell strains. Exp Cell Res. 37, 614-636 https://doi.org/10.1016/0014-4827(65)90211-9
  8. Irizarry, R.A., Hobbs, B., Collin, F., Beazer-Barclay, Y.D., Antonellis, K.J., Scherf, U., and Speed, T.P. (2003). Exploration, normalization, and summaries of high density oligonucleotide array probe level data. Biostatistics 4, 249-264 https://doi.org/10.1093/biostatistics/4.2.249
  9. Kim, J.H., Ha, I.S., Hwang, C.I., Lee, Y.J., Kim, J., Yang, S.H., Kim, Y.S., Cao, Y.A., Choi, S., and Park, W.Y. (2004). Gene expression profiling of anti-GBM glomerulonephritis model: the role of NF-kappaB in immune complex kidney disease. Kidney Int. 66, 1826-1837 https://doi.org/10.1111/j.1523-1755.2004.00956.x
  10. Korn, E.L., Troendle, J.F., McShane, L.M., Simon, R. (2004) Controlling the number of false discoveries: Application to high-dimensional genomic data. J Stat Plan Inf. 124, 379-398 https://doi.org/10.1016/S0378-3758(03)00211-8
  11. Platt, J. (1998). Fast training of support vector machines using sequential minimal optimization, advances in kernel methods - Support Vector Learning. MIT Press, Boston, MA
  12. Simon, R., Korn, E., McShane, L., Radmacher, M., Wright, G., Zhao, Y. (2004). Design and Analysis of DNA Microarray Investigations. Springer-Verlag New York, NY
  13. Tan, P.N., Stenbach, M., and Kumar, V. (2005). Introduction to data mining, Addison Wesley, New York, NY
  14. The Gene Ontology Consortium. (2000). Gene Ontology: Tool for the unification of biology. Nat. Genetics 25, 25-29 https://doi.org/10.1038/75556
  15. Vapnik, V.N. (1998). Statistical Learning Theory. Wiley, New York, NY
  16. Wiehle, R.D., Helftenbein, G., Land, H., Neumann, K., and Beato, M. (1990). Establishment of rat endometrial cell lines by retroviral mediated transfer of immortalizing and transforming oncogenes. Oncogene 5, 787-794