biostudies-literatureUnknown16Kuang M<h4>Background</h4>Ovarian cancer (OC) is a severe malignant tumor with a significant threat to women's health, characterized by a high mortality rate and poor prognosis despite conventional treatments such as cytoreductive surgery and platinum-based chemotherapy. Cuproptosis, a novel form of cell death triggered by copper ion accumulation, has shown potential in cancer therapy, particularly through the involvement of CuLncs. This study aims to identify risk signatures associated with CuLncs in OC, construct a prognostic model, and explore potential therapeutic drugs and the impact of CuLncs on OC cell behavior.<h4>Methods</h4>We analyzed ovarian cancer data (TCGA-OV) from the TCGA database, including transcriptomic and clinical data from 376 patients. Using Pearson correlation and LASSO rFrontiers in immunology1555782https://www.ebi.ac.uk/biostudies/studies/S-EPMC11893572biostudies-literatureBig 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.PMC11893572You KLiu YChen GKuang MChen HGao TfalseBig 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.<h4>Background</h4>Ovarian cancer (OC) is a severe malignant tumor with a significant threat to women's health, characterized by a high mortality rate and poor prognosis despite conventional treatments such as cytoreductive surgery and platinum-based chemotherapy. Cuproptosis, a novel form of cell death triggered by copper ion accumulation, has shown potential in cancer therapy, particularly through the involvement of CuLncs. This study aims to identify risk signatures associated with CuLncs in OC, construct a prognostic model, and explore potential therapeutic drugs and the impact of CuLncs on OC cell behavior.<h4>Methods</h4>We analyzed ovarian cancer data (TCGA-OV) from the TCGA database, including transcriptomic and clinical data from 376 patients. Using Pearson correlation and LASSO r2025-01-01T00:00:00Z20252025-04-04T20:25:55.54Z2025-04-04T20:25:55.54ZS-EPMC118935724007082110.3389/fimmu.2025.1555782