Identification of a glycolysis‐related gene signature for survival prediction of ovarian cancer patients

Abstract Background Ovarian cancer (OV) is deemed the most lethal gynecological cancer in women. The aim of this study was to construct an effective gene prognostic model for predicting overall survival (OS) in patients with OV. Methods The expression profiles of glycolysis‐related genes (GRGs) and...

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Autores principales:	Dai Zhang, Yiche Li, Si Yang, Meng Wang, Jia Yao, Yi Zheng, Yujiao Deng, Na Li, Bajin Wei, Ying Wu, Zhen Zhai, Zhijun Dai, Huafeng Kang
Formato:	article
Lenguaje:	EN
Publicado:	Wiley 2021
Materias:	bioinformatics glycolysis ovarian cancer prognostic signature Neoplasms. Tumors. Oncology. Including cancer and carcinogens RC254-282
Acceso en línea:	https://doaj.org/article/f21e41a493894f62885d5ec701973153
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spelling	oai:doaj.org-article:f21e41a493894f62885d5ec7019731532021-11-22T09:08:48ZIdentification of a glycolysis‐related gene signature for survival prediction of ovarian cancer patients2045-763410.1002/cam4.4317https://doaj.org/article/f21e41a493894f62885d5ec7019731532021-11-01T00:00:00Zhttps://doi.org/10.1002/cam4.4317https://doaj.org/toc/2045-7634Abstract Background Ovarian cancer (OV) is deemed the most lethal gynecological cancer in women. The aim of this study was to construct an effective gene prognostic model for predicting overall survival (OS) in patients with OV. Methods The expression profiles of glycolysis‐related genes (GRGs) and clinical data of patients with OV were extracted from The Cancer Genome Atlas (TCGA) database. Univariate, multivariate, and least absolute shrinkage and selection operator Cox regression analyses were conducted, and a prognostic signature based on GRGs was constructed. The predictive ability of the signature was analyzed using training and test sets. Results A gene risk signature based on nine GRGs (ISG20, CITED2, PYGB, IRS2, ANGPTL4, TGFBI, LHX9, PC, and DDIT4) was identified to predict the survival outcome of patients with OV. The signature showed a good prognostic ability for OV, particularly high‐grade OV, in the TCGA dataset, with areas under the curve (AUC) of 0.709 and 0.762 for 3‐ and 5‐year survival, respectively. Similar results were found in the test sets, and the AUCs of 3‐, 5‐year OS were 0.714 and 0.772 in the combined test set. And our signature was an independent prognostic factor. Moreover, a nomogram combining the prediction model and clinical factors was developed. Conclusion Our study established a nine‐GRG risk model and nomogram to better predict OS in patients with OV. The risk model represents a promising and independent prognostic predictor for patients with OV. Moreover, our study on GRGs could offer guidance for the elucidation of underlying mechanisms in future studies.Dai ZhangYiche LiSi YangMeng WangJia YaoYi ZhengYujiao DengNa LiBajin WeiYing WuZhen ZhaiZhijun DaiHuafeng KangWileyarticlebioinformaticsglycolysisovarian cancerprognostic signatureNeoplasms. Tumors. Oncology. Including cancer and carcinogensRC254-282ENCancer Medicine, Vol 10, Iss 22, Pp 8222-8237 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	bioinformatics glycolysis ovarian cancer prognostic signature Neoplasms. Tumors. Oncology. Including cancer and carcinogens RC254-282
spellingShingle	bioinformatics glycolysis ovarian cancer prognostic signature Neoplasms. Tumors. Oncology. Including cancer and carcinogens RC254-282 Dai Zhang Yiche Li Si Yang Meng Wang Jia Yao Yi Zheng Yujiao Deng Na Li Bajin Wei Ying Wu Zhen Zhai Zhijun Dai Huafeng Kang Identification of a glycolysis‐related gene signature for survival prediction of ovarian cancer patients
description	Abstract Background Ovarian cancer (OV) is deemed the most lethal gynecological cancer in women. The aim of this study was to construct an effective gene prognostic model for predicting overall survival (OS) in patients with OV. Methods The expression profiles of glycolysis‐related genes (GRGs) and clinical data of patients with OV were extracted from The Cancer Genome Atlas (TCGA) database. Univariate, multivariate, and least absolute shrinkage and selection operator Cox regression analyses were conducted, and a prognostic signature based on GRGs was constructed. The predictive ability of the signature was analyzed using training and test sets. Results A gene risk signature based on nine GRGs (ISG20, CITED2, PYGB, IRS2, ANGPTL4, TGFBI, LHX9, PC, and DDIT4) was identified to predict the survival outcome of patients with OV. The signature showed a good prognostic ability for OV, particularly high‐grade OV, in the TCGA dataset, with areas under the curve (AUC) of 0.709 and 0.762 for 3‐ and 5‐year survival, respectively. Similar results were found in the test sets, and the AUCs of 3‐, 5‐year OS were 0.714 and 0.772 in the combined test set. And our signature was an independent prognostic factor. Moreover, a nomogram combining the prediction model and clinical factors was developed. Conclusion Our study established a nine‐GRG risk model and nomogram to better predict OS in patients with OV. The risk model represents a promising and independent prognostic predictor for patients with OV. Moreover, our study on GRGs could offer guidance for the elucidation of underlying mechanisms in future studies.
format	article
author	Dai Zhang Yiche Li Si Yang Meng Wang Jia Yao Yi Zheng Yujiao Deng Na Li Bajin Wei Ying Wu Zhen Zhai Zhijun Dai Huafeng Kang
author_facet	Dai Zhang Yiche Li Si Yang Meng Wang Jia Yao Yi Zheng Yujiao Deng Na Li Bajin Wei Ying Wu Zhen Zhai Zhijun Dai Huafeng Kang
author_sort	Dai Zhang
title	Identification of a glycolysis‐related gene signature for survival prediction of ovarian cancer patients
title_short	Identification of a glycolysis‐related gene signature for survival prediction of ovarian cancer patients
title_full	Identification of a glycolysis‐related gene signature for survival prediction of ovarian cancer patients
title_fullStr	Identification of a glycolysis‐related gene signature for survival prediction of ovarian cancer patients
title_full_unstemmed	Identification of a glycolysis‐related gene signature for survival prediction of ovarian cancer patients
title_sort	identification of a glycolysis‐related gene signature for survival prediction of ovarian cancer patients
publisher	Wiley
publishDate	2021
url	https://doaj.org/article/f21e41a493894f62885d5ec701973153
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_version_	1718417831663501312

Identification of a glycolysis‐related gene signature for survival prediction of ovarian cancer patients

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