Gene selection is an important issue for cancer classification based on gene expression profiles. Filter and wrapper approaches are used widely for gene selection, where the former is hard to measure the relationship between genes and the latter requires lots of computation. We present a novel method, called gene boosting, to select relevant gene subsets by integrating filter and wrapper approaches. It repeatedly selects a set of top-ranked informative genes by a filtering algorithm with respect to a temporal training dataset constructed according to the classification result for the original training dataset. Empirical results on three microarray benchmark datasets have shown that the proposed method is effective and efficient in finding a relevant and concise gene subset. Competitive performance was achieved with fewer genes in a reasonable time. This also led to the identification of some genes selected frequently as useful features.