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.

Background: 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. Methods: We analyzed ovarian cancer data (TCGA-OV) from the TCGA database, including transcriptomic and clinical data from 376 patients. Using Pearson correlation and LASSO regression, we identified 8 prognostic CuLncs to construct a risk signature model. Patients were categorized into high- and low-risk groups based on their risk scores. We performed survival analysis, model validation, drug sensitivity analysis, and Results: The prognostic model demonstrated significant predictive power, with an area under the curve (AUC) of 0.702 for 1-year, 0.640 for 3-year, and 0.618 for 5-year survival, outperforming clinical pathological features such as stage and grade. High-risk OC patients exhibited higher Tumor Immune Dysfunction and Exclusion (TIDE) scores, indicating stronger immune evasion ability. Drugs such as JQ12, PD-0325901, and sorafenib showed reduced IC50 values in the high-risk group, suggesting potential therapeutic benefits. Conclusion: Our study identified a prognostic risk model based on CuLncs and explored their potential as therapeutic targets in OC. The findings highlight the importance of CuLncs in OC prognosis and immune response, providing new insights for future research and clinical applications.