The aim of the present study was repositioning of ivermectin in treatment of gastric cancer (GC) by computational prediction based on gene expression profiles of human and mouse model of GC and validations with , and approaches. Computational drug repositioning was performed using connectivity map (cMap) and data/pathway mining with the Ingenuity Knowledge Base. Tissue samples of GC were collected from 16 patients and 57 mice for gene expression profiling. Additional seven independent datasets of gene expression of human GC from the TCGA database were used for validation. testing was performed by constructing interaction networks of ivermectin and the downstream effects in targeted signaling pathways. testing was carried out in human GC cell lines (MKN74 and KATO-III). testing was performed in a transgenic mouse model of GC (INS-GAS mice). GC gene expression "signature" and data/pathway mining but not cMAP revealed nine molecular targets of ivermectin in both human and mouse GC associated with WNT/β-catenin signaling as well as cell proliferation pathways. inhibition of the targets of ivermectin and concomitant activation of ivermectin led to the inhibition of WNT/β-catenin signaling pathway in "dose-depended" manner. , ivermectin inhibited cell proliferation in time- and concentration-depended manners, and cells were arrested in the G phase at IC and shifted to S phase arrest at >IC. , ivermectin reduced the tumor size which was associated with inactivation of WNT/β-catenin signaling and cell proliferation pathways and activation of cell death signaling pathways. Ivermectin could be recognized as a repositioning candidate in treatment of gastric cancer.