Abstract: OBJECTIVE To analyze the microarray data of the gastric cancer patients with chemotherapy resistance to look for the differences of gene modules and key pathways. METHODS Gene expression profiles of the gastric cancer with the information of chemoresistance in GEO database were selected. The GEO2R tools were used to identify differential expression genes (DEGs) and String database was employed to visualization analysis for protein-protein interaction (PPI) network. Then, the PPI network was imported into the Cytoscape software to find key nodes and stabilization modules by Centiscape, MCODE. After that, DAVID database was used to enrich and annotate the pathway and key modules. Finally, the C-MAP platform was used to simulate the compounds that matched the variation trend of differential genes. RESULTS This study identified 609 characteristic genes of chemoresistance gastric cancer. ACLY, AKT1 were key nodal proteins for drug resistance in gastric cancer, related with immune and tumor growth signal pathway. The results of C-MAP analysis suggested that the score of 7 candidate compounds was more than 0.9. CONCLUSION This study employed bioinformatics method from various perspectives to define the gene expression characteristics of gastric cancer with chemoresistance. These results may facilitate the discovery of biomarkers for predicting chemotherapy responses in gastric cancer and contribute in developing personalized medicines.