Abstract:Objective To explore the construction of immune-related gene prognostic index (IRGPI) model in order to improve the accuracy of predicting the prognosis of uterine corpus endometrial carcinoma (UCEC) and provide potential biomarkers and molecular therapeutic targets for its diagnosis and treatment. Methods From the Cancer Genome Atlas (TCGA) database, the UCEC dataset was obtained for an analysis. Cox regression analysis and weighted gene co-expression network analysis (WGCNA) were used for the identification of immune-related hub genes and the establishment of IRGPI models. Kaplan Meier (K-M) survival curve was used to verify the accuracy of IRGPI model. High IRGPI subgroup and low IRGPI subgroup were designed by R software to analyze the differences in immune cell infiltration and immune function between two subgroups. Results Out of 23 cases of normal tissues and 554 cases of tumor tissues obtained in UCEC dataset from TCGA database, 31 immune-related genes were selected for constructing IRGPI model. Excluding 11 invalid tissues from 554 tumor tissues, K-M survival curve analysis showed that the overall survival of the high IRGPI subgroup (n=271) was significantly lower than that of the low IRGPI subgroup (n=272, P<0.01). ROC curve revealed that the AUC of IRGPI model in predicting the prognosis of UCEC (0.885)was obviously greater than that of the tumor immune dysfunction and exclusion (TIDE) model (0.490) and the tumor inflammation signature (TIS) model (0.427). There were statistical differences in immune cell infiltration and immunological function between high IRGPI subgroup and low IRGPI subgroup (P＜0.05). Conclusion IRGPI model can accurately predict the prognosis of patients with UCEC, and provide potential biomarkers and molecular therapeutic targets for the treatment of UCEC.