Sequential pattern mining techniques have been widely used in different topics of interest, such as mining customer purchasing sequences from a transactional database. Notably, observation of gene expressions to discover gene regulations during biological or clinical progression via microarray approaches has become the dominant trend. By converting microarray datasets into the format of transactional databases, sequential patterns implying gene regulations could be identified. However, there exists no effective method in current studies that can handle such kind of dataset as every transaction may contain too many items/genes and the resultant patterns are very susceptible to item order. We propose a new method called CTGR-Span (Cross-Timepoint Gene Regulation Sequential Patterns) to efficiently mine CTGR-SPs (cross-timepoint gene regulation sequential patterns). The proposed method was experimented with two publicly available human time course microarray datasets and it outperformed traditional methods over 2,000 times in terms of the execution efficiency. Furthermore, via a Gene Ontology enrichment analysis, the resultant patterns are more meaningful biologically compared to previous literature reports. Hence, it could provide biologists more insights into the mechanisms of novel gene regulations in certain disease progressions.