The translation of high-throughput gene expression data into biologically meaningful information remains a bottleneck. We developed a novel computational algorithm, PATHOME, for detecting differentially expressed biological pathways. This algorithm employs straightforward statistical tests to evaluate the significance of differential expression patterns along subpathways. Applying it to gene expression data sets of gastric cancer (GC), we compared its performance with those of other leading programs. Based on a literature-driven reference set, PATHOME showed greater consistency in identifying known cancer-related pathways. For the WNT pathway uniquely identified by PATHOME, we validated its involvement in gastric carcinogenesis through experimental perturbation of both cell lines and animal models. We identified HNF4α-WNT5A regulation in the cross-talk between the AMPK metabolic pathway and the WNT signaling pathway, and further identified WNT5A as a potential therapeutic target for GC. We have demonstrated PATHOME to be a powerful tool, with improved sensitivity for identifying disease-related dysregulated pathways.