Abstract: In order to discover informative gene of cancer, a view to regard different tumors as a single class was pesented in this paper. The purpose is to find informative genes that can classify tumor tissues from normal tissues, which can be used for gene expression research of biomedicine and biotechnology. We used the correlation coefficient for each gene as the criterion for classification, and remove the ‘noise-genes’ with smaller correlation coefficient values. A statistical method called ‘nearest shrunken centroids’ is applied in order to find informative gene with good ability of classifying and clustering the samples corresponding to their tissues types. We correctly clustered 87.7% samples and classify the testing samples with an accuracy of 81.1% using the informative genes. The results show that both the performance of clustering and classifying are improved after the feature selection.