AUTHOR=Ding Hongmei , Jiang Fei , Deng Lifeng , Wang Juan , Wang Ping , Ji Mintao , Li Jie , Shi Weiqiang , Pei Yufang , Li Jiafu , Zhang Yue , Zhang Zengli , Chen Youguo , Li Bingyan TITLE=Prediction of Clinical Outcome in Endometrial Carcinoma Based on a 3-lncRNA Signature JOURNAL=Frontiers in Cell and Developmental Biology VOLUME=9 YEAR=2022 URL=https://www.frontiersin.org/journals/cell-and-developmental-biology/articles/10.3389/fcell.2021.814456 DOI=10.3389/fcell.2021.814456 ISSN=2296-634X ABSTRACT=
Endometrial carcinoma (EC) is one of the common gynecological cancers with increasing incidence and revived mortality recently. Given the heterogeneity of tumors and the complexity of lncRNAs, a panel of lncRNA biomarkers might be more precise and stable for prognosis. In the present study, we developed a new lncRNA model to predict the prognosis of patients with EC. EC-associated differentially expressed long noncoding RNAs (lncRNAs) were identified from The Cancer Genome Atlas (TCGA). Univariate COX regression and least absolute shrinkage and selection operator (LASSO) model were selected to find the 8-independent prognostic lncRNAs of EC patient. Furthermore, the risk score of the 3-lncRNA signature for overall survival (OS) was identified as CTD-2377D24.6 expression × 0.206 + RP4-616B8.5 × 0.341 + RP11-389G6.3 × 0.343 by multivariate Cox regression analysis. According to the median cutoff value of this prognostic signature, the EC samples were divided into two groups, high-risk set (3-lncRNAs at high levels) and low-risk set (3-lncRNAs at low levels), and the Kaplan–Meier survival curves demonstrated that the low-risk set had a higher survival rate than the high-risk set. In addition, the 3-lncRNA signature was closely linked with histological subtype (