Identification of a glycolysis‐related gene signature for survival prediction of ovarian cancer patients. (5th October 2021)

• Record Type: Journal Article
• Authors: Zhang, Dai; Li, Yiche; Yang, Si; Wang, Meng; Yao, Jia; Zheng, Yi; Deng, Yujiao; Li, Na; Wei, Bajin; Wu, Ying; Zhai, Zhen; Dai, Zhijun; Kang, Huafeng
• Is Part Of: Cancer medicine. Volume 10:Number 22(2021)
• Page Start: 8222
• Page End: 8237
• Publication Date: 2021-10-05
• Subjects: bioinformatics -- glycolysis -- ovarian cancer -- prognostic signature; 616.994005
• DOI: 10.1002/cam4.4317
• Languages: English
• ISSNs: 2045-7634

Abstract

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. Abstract : Novel 9‐gene risk signature based on glycolysis‐related genes was developed to predict ovarian cancer survival. A nomogram combining the gene signature and patient characteristics provided superior estimation of overall survival. Our research provided a new prognostic tool and guidelines for future research.