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 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.

Integrated clinical characteristics and omics analysis identifies a ferroptosis and iron-metabolism-related lncRNA signature for predicting prognosis and therapeutic responses in ovarian cancer

“…IRS2, ANGPTL4, TGFBI, LHX9, PC, and DDIT4 [ 23 ]. Zhou et al identified a DNA methylation-driven genes signature, including PON3, MFAP4, AKAP12, and BHMT2 [ 24 ].…”

Derivation, Comprehensive Analysis, and Assay Validation of a Pyroptosis-Related lncRNA Prognostic Signature in Patients With Ovarian Cancer

“…The results were also exciting: in the C-index for predicting the TCGA cohort, the risk signature of our study showed better predictive value than the glycolysis-related gene signature established by Zhang et al. ( 34 ), the glycolysis-related lncRNA signature established by Zheng et al. ( 35 ), and the DNA methylation-driven gene signature established by Zhou et al.…”

PTH2R is related to cell proliferation and migration in ovarian cancer: a multi-omics analysis of bioinformatics and experiments

“…TGCA database is also widely used in oncology studies; it is a pan-cancer project hosted by the National Institutes of Health (NIH), providing a wide variety of tumors and different molecular data types that can be downloaded and analyzed. In the past few years, most studies have focused on RNA sequencing (RNA-Seq) analysis, such as mRNA and non-coding RNAs [ 12 – 15 ]. Recently, following the updating of old algorithms and the discovery of new ones, CIBERSORT, MCPcounter, and other algorithms have been used to assess tumor immune cell infiltration.…”