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Identification of a Multi-RNA-Type-Based Signature for Recurrence-Free Survival Prediction in Patients with Uterine Corpus Endometrial Carcinoma by Wang, Peizhi; Zeng, Zhi; Shen, Xiaoting; Tian, Xiaohui; Ye, Qingjian is a scholarly article available to read on EtoBox.

What is Identification of a Multi-RNA-Type-Based Signature for Recurrence-Free Survival Prediction in Patients with Uterine Corpus Endometrial Carcinoma about?

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 ( = 75, recurrent; = 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: , , , , , , ) was developed for the prediction of RFS. The risk scoring system establ

Author

Wang, Peizhi; Zeng, Zhi; Shen, Xiaoting; Tian, Xiaohui; Ye, Qingjian

Publisher

Mary Ann Liebert; Mary Ann Liebert Inc.; Mary Ann Liebert Inc (ISSN 1044-5498)

Published

2020

Language

EN