Xin Sun1, Panling Xu1, Fengli Zhang1, Ting Sun1, Haili Jiang1, Xinyuan Lu2, Mei Zhang1, Ping Li1. 1. Department of Traditional Chinese and Western Oncology, The First Affiliated Hospital of Anhui Medical University, Hefei, China. 2. The Graduate School, Anhui University of Traditional Chinese Medicine, Hefei, China.
Abstract
Background: Studies have shown that copper is involved in the tumorigenesis and development of ovarian cancer. In this work, we aimed to build a prognostic classification system associated with cuproptosis to predict ovarian cancer prognosis. Methods: Information of ovarian cancer samples were acquired from The Cancer Genome Atlas (TCGA)-ovarian cancer and GSE26193 dataset. Cuproptosis-related genes were screened from previous research. ConsensusClusterPlus was applied to determine molecular subtypes, which were evaluated by tumor immune microenvironment analysis, TIDE algorithm, and functional enrichment analysis. Furthermore, limma analysis and univariate Cox analysis were used to construct a cuproptosis-related prognostic signature for ovarian cancer. Univariate and multivariate Cox regression analyses were used to analyze the independence of clinical factors and model. Results: A total of 15 genes related to cuproptosis were identified, and 2 clusters (C1 and C2) were determined. C1 had a better survival outcome, less advanced stage, enhanced immune infiltration, was more sensitive to immunotherapy, and showed enrichment in tricarboxylic acid (TCA)-related pathways. An 8 cuproptosis-associated gene signature was constructed, and the signature was verified in the GSE26193 dataset. A higher risk score of the cuproptosis-related gene signature was significantly correlated with worse overall survival (OS) (P<0.0001), which was validated in GSE26193 dataset successfully. Cox survival analysis showed that risk score was an independent predictor [hazard ratio (HR) =2.66, P<0.001]. Functional enrichment and tumor immune microenvironment analyses showed that high-risk patients tended to have immunologically sensitive tumors. Conclusions: The cuproptosis-related gene signature may serve as a potential prognostic predictor for ovarian cancer patients and may offer novel treatment strategies for ovarian cancer. 2022 Annals of Translational Medicine. All rights reserved.
Background: Studies have shown that copper is involved in the tumorigenesis and development of ovarian cancer. In this work, we aimed to build a prognostic classification system associated with cuproptosis to predict ovarian cancer prognosis. Methods: Information of ovarian cancer samples were acquired from The Cancer Genome Atlas (TCGA)-ovarian cancer and GSE26193 dataset. Cuproptosis-related genes were screened from previous research. ConsensusClusterPlus was applied to determine molecular subtypes, which were evaluated by tumor immune microenvironment analysis, TIDE algorithm, and functional enrichment analysis. Furthermore, limma analysis and univariate Cox analysis were used to construct a cuproptosis-related prognostic signature for ovarian cancer. Univariate and multivariate Cox regression analyses were used to analyze the independence of clinical factors and model. Results: A total of 15 genes related to cuproptosis were identified, and 2 clusters (C1 and C2) were determined. C1 had a better survival outcome, less advanced stage, enhanced immune infiltration, was more sensitive to immunotherapy, and showed enrichment in tricarboxylic acid (TCA)-related pathways. An 8 cuproptosis-associated gene signature was constructed, and the signature was verified in the GSE26193 dataset. A higher risk score of the cuproptosis-related gene signature was significantly correlated with worse overall survival (OS) (P<0.0001), which was validated in GSE26193 dataset successfully. Cox survival analysis showed that risk score was an independent predictor [hazard ratio (HR) =2.66, P<0.001]. Functional enrichment and tumor immune microenvironment analyses showed that high-risk patients tended to have immunologically sensitive tumors. Conclusions: The cuproptosis-related gene signature may serve as a potential prognostic predictor for ovarian cancer patients and may offer novel treatment strategies for ovarian cancer. 2022 Annals of Translational Medicine. All rights reserved.