Abstract: Objective To explore the pathogenesis of gastric cancer through a bioinformatic approach to provide evidence for the prevention and treatment of gastric cancer. Methods The differentially expressed genes (DEGs) in gastric cancer and normal gastric mucosa in GSE79973 dataset were analyzed using GEO2R online tool. GO analysis and KEGG pathway enrichment analysis of the DEGs in DAVID database were performed. The protein interaction network was constructed using STRING database, and the key genes (Hub genes) were screened and their functional modules were analyzed using Cytoscape software. The GEPIA database was used to validate the Hub genes, and the Target Scan database was used to predict the microRNAs that regulate the target genes; OncomiR was used to analyze the expressions of the microRNAs in gastric cancer tissues and their relationship with the survival outcomes of the patients. Results A total of 181 DEGs were identified in gastric cancer, and 10 hub genes were screened by the protein- protein interaction network. Functional analysis showed that these DEGs were involved mainly in protein digestion and absorption, PI3K-Akt signaling pathway, ECM-receptor interaction and platelet activation signal pathway. GEPIA database validation showed that COL1A1 was highly expressed in gastric cancer tissues and was associated with a poor prognosis of patients with gastric cancer. MiR-129-5p was found to bind to the 3’UTR of COL1A1 mRNA, and compared with that in normal tissues, miR-129-5p expression was obviously down-regulated in gastric cancer tissues, and was correlated with the prognosis of the patients. Conclusion COL1A1 under regulation by MiR-129-5p is a potential therapeutic target for gastric cancer.