Construction of Prognostic Risk Assessment Model of Endometrial Cancer Based on miRNAs

The aberrant level expression of miRNAs is closely related to endometrial cancer progression as well as prognosis, and the survival prediction of endometrial cancer patients based on miRNAs can be used to evaluate the disease progression of patients. In this study, we used differential analysis method to screen the differentially expressed miRNAs in endometrial cancer tissues using the miRNAs expression profile data of endometrial cancer, sample related clinical information and follow-up information from TCGA database. Univariate Cox regression analysis was used to initially screen the miRNAs that had an association with the prognosis of endometrial cancer. The screened miRNAs were subjected to multivariate Cox regression analysis to construct a 9-miRNA prognostic risk score model for endometrial cancer. Based on the K-M survival curve and ROC curve, the validity of the model was assessed. The results showed that patients with a high-risk score had a significantly worse prognosis than those with a low-risk score. The area under the ROC curve of model 5-year overall survival was 0.81, which illustrated that this model could effectively predict the prognostic risk of patients with endometrial cancer. Prediction of mRNAs potentially bound by model miRNAs was performed by online databases, and their functions were predicted by Gene Ontology (GO), Kyoto Encyclopedia of genes and genomes (KEGG). The results indicated that the present model miRNAs molecules were involved in several relevant metabolic pathways in endometrial cancer. Finally, to demonstrate the potential of the model application, we give two demonstrations of applying the model. We constructed a nomogram and developed a simple clinically assisted GUI software. Overall, we screened miRNAs molecular markers with associations with endometrial cancer prognosis, constructed a miRNAs prognostic risk score model which had good predictive efficacy for the survival status of endometrial cancer patients. It can provide strong theoretical support for clinicians in making judgment and developing treatment plans.