Uterine corpus endometrial carcinoma (UCEC) is one of the leading causes of death from gynecological cancer due to the high recurrence rate. A recent study indicated that molecular biomarkers can enhance the recurrence prediction power if they were integrated with clinical information. In this study, we attempted to identify a new multi-RNA-type-based molecular biomarker for predicting the recurrence risk and recurrence-free survival (RFS). Matched mRNA (including lncRNA) and miRNA RNA-sequencing data from 463 UCEC patients (n = 75, recurrent; n = 388, non-recurrent) were downloaded from The Cancer Genome Atlas database. LASSO (least absolute shrinkage and selection operator) analysis was used to screen the optimal combination of prognostic RNAs and then the risk score model was constructed. Moreover, the molecular mechanisms of prognostic RNAs were explored by establishing various interaction networks based on corresponding predictive databases. A multi-RNA-type-based signature (including three miRNAs: hsa-miR-6511b, hsa-miR-184, hsa-miR-4461; three lncRNAs: ENO1-IT1, MCCC1-AS1, AATBC; and 7 mRNAs: EPPK1, ASB9, BDNF, CYP11A1, ECEL1, EN2, F13A1) was developed for the prediction of RFS. The risk scoring system established by these signature genes was effective for the discrimination of the 5-year RFS in the high-risk from low-risk patients in the training [an area under the receiver operating characteristic curve (AUC) = 0.960], validation (AUC = 0.863), and entire datasets (AUC = 0.873). This risk score model was also proved to be a more excellent, independent prognostic discriminator than the single-RNA-type (overall AUC: 0.947 vs. 0.677, lncRNAs; 0.709, miRNAs; 0.899, mRNAs) and clinical staging (overall AUC: 0.947 vs. 0.517). Furthermore, the downstream mechanisms for some prognostic miRNAs or lncRNAs (HAND2-AS1-hsa-miR-6511b-APC2, PAX8-AS1-hsa-miR-4461-TNIK and MCCC1-AS1/ENO1-IT1-TNIK) were newly predicted based on the coexpression or competitive endogenous RNA theories. In conclusion, our findings may provide novel biomarkers for recurrence prediction and targets for treatment of UCEC.
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