Discovering new information about groups of genes implied in a disease is still challenging. Microarrays are a powerful tool to analyse gene expression. They provide an expression level for genes under given biological situations. In this paper, we propose a new approach outlining relationships between genes based on their ordered expressions. Our contribution is twofold. First, we propose to use a new material, called sequential patterns, to be investigated by biologists. But, due to the expression matrice density, extracting sequential patterns from microarray datasets is far away from being easy. Secondly, we propose to introduce a knowledge support during the mining task. In this way, the search space is reduced and more relevant results (from a biological point of view) are obtained. Results of various experiments on real biological data highlight the relevance of our proposal.