Abstract: In this paper we proposed an approach for tumor informative genes selection by analysis of gene sen- sitivity based on SVM.We analyzed the gene expression profiles of colon and recursively eliminated the genes which have lower sensitivity to SVM,then a set of candidate nested feature subsets were generated.Support Vector Machines were employed to classify the samples using these candidate feature subsets,and the feature subset with a minimum error was chosen as a set of colon informative genes.The results show that this feature subset contains more tumor classification information than other feature subsets identified in the literatures. The method proposed in this paper is feasible and effective.