Background and Aims: Long non-coding RNAs (lncRNAs) exert significant influences on the prognosis of gastric cancer patients. This study was designated to construct a lncRNA-based prediction model for accurately evaluating the prognosis of gastric cancer patients through bioinformatics approaches. 
Methods: The data obtained from The Cancer Genome Atlas (TCGA) and Genotype-Tissue Expression (GTEx) databases were used for constructing the prognosis model (modeling group), while the data from The Gene Expression Omnibus (GEO) database were used for validation (validation group). The differentially expressed lncRNAs were screened using edgeR package in R software. Univariate and multivariate Cox regression were used to evaluate the association between LncRNA and survival time. prognostic model was created through univariate and multivariate Cox regression analyses and the risk score were calculated. The patients were divided into high-risk group and low-risk group according to their risk scores, and the relations of the risk score with clinicopathologic variables and prognosis were analyzed. The results of the modeling group were verified in the sample from validation group.
Results: A total of 288 differentially expressed lncRNAs were screened, and 28 of them were associated with the prognosis of gastric cancer (all P<0.05). Ten lncRNA biomarkers (MEG3, DNAJC9-AS1, ACTA2-AS1, C15orf54, LINC01210, OVAAL, POU6F2-AS2, ERICH3-AS1, LINC00326 and LINC01526) were identified and used to construct a prognostic model. Both overall survival rate and disease-free survival rate in high-risk group were significantly lower than those in low-risk group (both P<0.01). ROC curve confirmed that the prediction model had certain accuracy (AUC=0.700). The results of univariate and multivariate Cox regression analyses showed that the risk score was an independent prognostic factor (both P<0.001). The risk score had significant relation with T stage (P=0.031) and the degree of tumor differentiation (P=0.044). In validation cohort, the overall survival rate and disease-free survival rate in high-risk group were also lower than those in low-risk group, and the risk score remained an independent prognostic factor (all P<0.05). 
Conclusion: The constructed 10-lncRNA model has certain value in predicting the prognosis of gastric cancer patients, and the screened differentially expressed lncRNAs also provide the basis for further investigating the molecular mechanism of gastric cancer.