J Cancer 2021; 12(5):1431-1444. doi:10.7150/jca.50413 This issue

Research Paper

Construction of a Glycolysis-related long noncoding RNA signature for predicting survival in endometrial cancer

Yuan Jiang1,2*, Jie Chen2*, Jingxian Ling2, Xianghong Zhu2, Pinping Jiang3, Xiaoqiu Tang2, Huaijun Zhou1,2✉, Rong Li2✉

1. Department of Obstetrics and Gynecology, Nanjing Drum Tower Hospital, Nanjing Medical University, Nanjing 210008, China
2. Department of Obstetrics and Gynecology, Nanjing Drum Tower Hospital, Affiliated Hospital of Nanjing University Medical School, Nanjing 210008, China
3. Department of Gynecology, The First Affiliated Hospital of Nanjing Medical University, Nanjing 210029, Jiangsu Province, China
* These authors contributed equally to this work.

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Citation:
Jiang Y, Chen J, Ling J, Zhu X, Jiang P, Tang X, Zhou H, Li R. Construction of a Glycolysis-related long noncoding RNA signature for predicting survival in endometrial cancer. J Cancer 2021; 12(5):1431-1444. doi:10.7150/jca.50413. Available from https://www.jcancer.org/v12p1431.htm

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Abstract

Graphic abstract

Background: long noncoding RNA (lncRNA) has been widely studied and understood in various cancer types. However, the expression profiles of glycolysis-related lncRNA in endometrial cancer (EC) have poorly been reported.

Methods: In this study, we retrieved the “Glycolysis” gene list from Molecular Signatures Database (MSigDB) and screened prognostic glycolysis-related lncRNA using The Cancer Genome Atlas (TCGA) Uterine Corpus Endometrial Carcinoma (UCEC) RNA-seq dataset. Then, TCGA UCEC patients were randomly divided. Lasso algorithm and multivariate cox regression analyses were then performed to further select hub prognostic lncRNA and to develop a prognostic signature. The efficacy of the signature was also evaluated in the TCGA EC cohort. Moreover, we constructed a nomogram to predict EC patient outcomes.

Results: Univariate cox analysis identified thirty-six glycolysis-related lncRNA correlated with EC patient prognosis. Among them, five lncRNA were further selected as hub lncRNA that mostly relate to EC patient outcomes, which are AL121906.2, BOLA3-AS1, LINC01833, AC016405.3, and RAB11B-AS1. A prognostic signature was then built based on the expression and coefficiency of five lncRNA. The efficacy of the signature was validated in part of and the entire TCGA EC cohort. In addition, the risk signature could precisely distinguish high- and low-risk EC patients and predict patient outcomes. The nomogram exhibited absolute concordance between the predictions and actual survival observations.

Conclusions: The glycolysis-related lncRNA signature model and the nomogram may provide a new perspective for EC patients outcome prediction in clinical use.

Keywords: Glycolysis, lncRNA, TCGA, endometrial cancer, risk signature, nomogram.