ArticleAnnals of translational medicine2022
The cuproptosis-related gene signature serves as a potential prognostic predictor for ovarian cancer using bioinformatics analysis.
Article in Annals of translational medicine, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers.
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Who cites it
8 citing papers in PubMed, 12 citations in OpenAlex.
- Multiple machine learning algorithms construct cuproptosis genes and oxidative stress genes-related LncRNAs signature with prognostic and therapeutic relevance in ovarian cancer.Genes & genomics · 2026Article
- Aging at the Crossroads of Cuproptosis and Ferroptosis: From Molecular Pathways to Age-Related Pathologies and Therapeutic Perspectives.International journal of molecular sciences · 2026Review
- Identification of a Novel Pyroptosis-Related lncRNAs Prognosis Model and Subtypes in Ovarian Cancer.Phenomics (Cham, Switzerland) · 2025Article
- Big data analysis and machine learning of the role of cuproptosis-related long non-coding RNAs (CuLncs) in the prognosis and immune landscape of ovarian cancer.Frontiers in immunology · 2025Article
- Senescence-Related LncRNAs: Pioneering Indicators for Ovarian Cancer Outcomes.Phenomics (Cham, Switzerland) · 2024Review
- Establishment of an ovarian cancer exhausted CD8+T cells-related genes model by integrated analysis of scRNA-seq and bulk RNA-seq.European journal of medical research · 2024Article
- Systematic analysis of the cuprotosis in tumor microenvironment and prognosis of gastric cancer.Heliyon · 2023Article
- Identification of a ferroptosis- and oxidative stress-associated gene signature for prognostic stratification of ovarian cancer.American journal of translational research · 2023Article
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Authors and funding
8 authors at 3 institutions in 1 country.
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No grant is acknowledged in the PubMed record.
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.
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