Background Recently, it has been demonstrated that the disturbance of RNA-binding proteins is closely related to the occurrence, progression and prognosis of diseases and cancers. Meanwhile, the role of RBPs in endometrial cancer remains to be explored. Objective To find out the prognostic role of RBPs in endometrial cancer. Methods Unit COX regression analysis, LASSO, multivariate COX regression analysis, Kaplan–Meier survival analysis. Results We obtained endometrial cancer RNA sequencing data and clinical data from TCGA, screened 284 differentially expressed genes that were differentially expressed between cancer tissues and normal tissues, performed functional enrichment analysis on them, and then screened from the differentially expressed genes identified prognostic-related genes (CD3EAP, EPHB6, CIRBP, LTK, ADARB2, RBPMS), developed a prognostic model to examine differences in OS among patients in high- and low-risk cohorts, and constructed a nomogram to predict patient survival rate. Conclusions We use public databases to study that RBPs have certain value in the occurrence and development of endometrial cancer, and can be used as a prognostic marker for endometrial cancer.

Setting：Immune cells played a vital role in the progression of endometrial cancer, whereas immunologic mechanism and clinical prognostic biomarkers remained to be identified. Objective: To explore the landscape of tumor-infiltrating immune cells in endometrial cancer and the correlation between TIICs and EC prognosis. Methods: Data of 530 patients was downloaded from TCGA. CIBERSORTx estimated the abundance of immune cells. Ranked data used Spearman coefficient to measure the relationship between immune cells and clinical characteristics. Univariate COX hazard analysis was performed to explore the relationship between immune cells and clinical outcomes. Lasso regression was used to screen variables. Multivariate regression was performed for stepwise regression variable screening. Univariate and multivariate Independent prognosis analysis was performed to validate the reliability of the multivariate risk model. A nomographic chart was used to grade and assess each patient’s prognosis. Results: 11 type TIICs were increased in cancer tissues(P<0.05). Univariable regression revealed that 5 types TIICs were significantly related with EC prognosis. TTMMD model was constructed via lasso regression and multivariable cox regression. Log-rank was used to perform survival analysis, and the result was presented by Kaplan-Meier curve(p=5.13e-03). The Roc curve was simultaneously fulfilled to validate the model’s accuracy and predictability(AUC=0.676). The Kaplan-Meier curve of the training set was statistically significant(p=3.807e-02). ROC curve replied the feasibility of the model(AUC=0.678). The risk model was an independent factor for predicting EC prognosis(p<0.001). Conclusion: 11 types TIICs were significantly associated with the prognosis of endometrial cancers. The immune-cells-based risk model was reliable for prognosis assessment.