Mining Prognostic Genes In Immune Microenvironment Of Endometrial Carcinoma Based On TCGA Database And Construction Of Prognostic Model

Objective: To explore the expression and potential function of prognostic genes in immune microenvironment of endometrial carcinoma(EC)based on TCGA database and to build a prediction model for prognosis.Thus,we can offer a new insight for EC immunotherapy and assessment of prognosis.Methods:1.The RNA-seq data of TCGA-EC were obtained from the official website of UCSC Xena.ESTIMATE was applied to evaluate the proportion of immunities and stromata in tumor microenvironment(TME),then to study the relation of immune/stromal scores to survival and clinicopathologic features.By analyzing the difference between high-cohort and low-cohort of immune/stromal scores,the differential genes influencing EC-TME were found out.Next,GO and KEGG analysis were performed to explore the biological function.Then PPI network interaction analysis was performed on the differential gene,hubba algorithm of Cytoscape and univariate Cox regression analysis were used to screen the prognostic value.Finally,the expression of TIGIT in tumor tissue was verified by GEPIA2 database,and the key gene to be studied was locked.2.The expression of TIGIT in EC was validated by immunohistochemical technique.3.Patients were divided into high and low cohorts based on the mean of TIGIT expression,and the difference in TIGIT expression were analyzed.GO,KEGG and GSEA analysis were carried out to investigate the biological function.Then,CIBERSORT was applied to compare the difference of the tumor infiltrating immune cells(TIICs)in the high/low cohort.Meanwhile,the relationship between the TIGIT and immune cells was analyzed.Via downlanding the EC somatic mutation data from UCSC Xena,calculating the tumor mutational burden(TMB)of EC,and obtaining the gene expression profile of the immune checkpoints from the previous literature,the relationship between TIGIT with TMB and the immunity checkpoints was tested.Furthermore,the immunophenoscore of EC was obtained from the TCIA database to analyse the sensitivity of TIGIT to immune checkpoint inhibitors.4.TCGA-EC data were randomly assigned to a training and a texting group in a ratio of 7:3.In order to determine the optimal genome for the prognosis model,TIGIT screened in the training group were analyzed by single variable Cox,LASSO and multivariate Cox regression.The ROC was applied to evaluate the reliability of the model and the prediction performance of the model was validated by the test group and entire group.Finally,a nomogram was drawn based on the risk score and the clinical information of patients(age,grade and FIGO stage),calibration curve and5-year DCA curve were used to evaluate model.Results:1.Survival rate of EC patients was correlated with immune score positively(P <0.05),and low-grade EC patients showed significantly higher immune score(P <0.05).A total of 269 genes affecting tumor microenvironment were obtained by differential comparative analysis,including 264 up-regulated genes and 5down-regulated genes.The results of GO and KEGG indicated that these genes had some relationship with immunity.Based on the single variable Cox regression analysis,38 genes associated with survival of patients were screened out of 269 differential genes(P < 0.05).Crossed with the first 15 nodes of PPI,six prognostic genes: TIGIT,CD28,SLAMF1,IL21 R,ITK and CD40 LG were obtained.The expression of those genes in EC tumor tissues was confirmed by GEPIA2 database.The results showed that TIGIT was significantly regulated in EC tissues(P < 0.05),while CD28,SLAMF1,IL21 R,ITK and CD40 LG were not significantly different(P > 0.05).2.Immunohistochemistry showed that the expression of TIGIT in EC tissue was significantly higher than that in paracancer tissue(P < 0.05).3.Through differential comparison analysis,753 differential genes associated with TIGIT expression were obtained,including 622 up-regulated genes and 131down-regulated genes.The analysis of GO,KEGG and GSEA demonstrated that TIGIT was associated with immunity.The infiltration density of 12 immune cells was significantly different from the expression of TIGIT(P < 0.05).Correlation analysis indicated 13 types of TIICs were related to TIGIT(P < 0.05).By finally intercrossing,11 types of TIICs had a close relationship with TIGIT.Subsequent correlation analysis showed that TIGIT was positively correlated with the expression of 38 immune checkpoints(R > 0 and P < 0.05)and TMB(R = 0.34 and P < 0.05).Meanwhile,the high expression of TIGIT was found to enhance the susceptibility of PD-1 and CTLA-4 based on the obtained endometrial immunophenoscore(P < 0.05).4.TRBJ2-3,LAMP3,TNFRSF18,NAPSB,CST5,RPL35AP4 and TSPAN7 were identified by single variable Cox,LASSO and multiple Cox regression.In the training group,the AUC used to predict survival rates of EC patients at different time points(1-year,3-year and 5-year)respectively were 0.819,0.755 and 0.788,those in the texting group were 0.739,0.644 and 0.600,in the whole data group were 0.815,0.775 and 0.801.Then,the prognosis model was plotted by combining the risk score with clinical information.The calibration curves of 1-year,3-year and 5-year were all close to the diagonal gray line(actual survival rate),and the 5-year DCA was far from the extreme curve.Conclusions:1.TIGIT is a prognostic value gene affecting the immune microenvironment of EC and is highly expressed in EC cancer tissues.2.The expression of TIGIT is closely related to the immune activity of EC tumor microenvironment.EC patients with high expression of TIGIT have a higher TMB and are more sensitive to ICIs therapy.Moreover,TIGIT is helpful to improve the effect of PD-1 blocking therapy,which is a potential target for EC immunotherapy.3.The prognostic model and corresponding nomogram of EC patients constructed based on prognostic value genes closely related to TIGIT(TRBJ2-3,LAMP3,TNFRSF18,NAPSB,CST5,RPL35AP4 and TSPAN7)have good predictive performance and are valuable for clinical decision making.