Selecting informative and discriminative genes from huge microarray gene expression data is an important and challenging bioinformatics research topic. This paper proposes a fuzzy-granular method for the gene selection task. Firstly, genes are grouped into different function granules with the Fuzzy C-Means algorithm (FCM). And then informative genes in each cluster are selected with the Signal to Noise metric (S2N). With fuzzy granulation, information loss in the process of gene selection is decreased. As a result, more informative genes for cancer classification are selected and more accurate classifiers can be modeled. The simulation results on two publicly available microarray expression datasets show that the proposed method is more accurate than traditional algorithms for cancer classification. And hence we expect that genes being selected can be more helpful for further biological studies.