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 classification system. The selected molecular markers generated valuable additional information for tumor diagnosis, prognosis and therapy development.
Classification ; Diagnosis ; Gene Expression ; Microarray Analysis ; Oligonucleotide Array Sequence Analysis ; Prognosis ; Transcriptome ; Support Vector Machine