Objective:Based on bioinformatics,new immune-related LncRNAs related to the prognosis of gastric cancer were screened,and a prognostic risk model of immune-related LncRNA was further constructed,in order to be used as a new indicator for early diagnosis and prognostic status of gastric cancer.Methods:The gastric cancer transcriptome data and corresponding clinical prog-nosis data were downloaded from multiple data platforms,and the immune-related LncRNAs of gastric cancer were screened by bioin-formatics methods.Cox regression analysis was used to screen LncRNAs related to immune prognosis in gastric cancer,and LncRNAs related to immune prognosis with independent prognostic significance were identified to construct a prognostic risk model,and the risk score of each patient was calculated.Patients were divided into low-risk and high-risk groups according to the cutpoint.Kaplan-Meier analysis was performed for survival analysis and survival curves were drawn,nomograms were drawn and internal validation was per-formed,and univariate and multivariate Cox regression analysis was performed to analyze the relationship between risk scores and clin-icopathological characteristics and survival prognosis of gastric cancer patients.Results:Three immune prognosis-related LncRNAs(UCA1,MIR4435-1HG,RP11-617F23.1)were identified by Cox regression analysis,and a predictive scoring model was constructed to divide the patients into high-risk group and low-risk group according to the prognosis score.There was a statistically significant dif-ference in the prognosis of patients between the two groups(P<0.05).The multivariate Cox regression analysis risk score was an inde-pendent risk factor for the prognosis of gastric cancer,and the internal verification of the nomogram showed good reliability.Conclu-sion:Three immune-related LncRNAs in gastric cancer are significantly correlated with the prognosis of gastric cancer patients,and the predictive scoring model constructed based on them can effectively predict the prognosis and can be used as their independent prog-nostic biomarkers.