Kuang Mingqin, Liu Yueyang, Chen Hongxi, Chen Guandi, Gao Tian, You Keli
Gynecology and Oncology Department of Ganzhou Cancer Hospital, Ganzhou, Jiangxi, China.
Department of Gynecology, Guangdong Provincial People's Hospital (Guangdong Academy of Medical Sciences), Southern Medical University, Guangzhou, China.
Front Immunol. 2025 Feb 25;16:1555782. doi: 10.3389/fimmu.2025.1555782. eCollection 2025.
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 experiments to assess the model's performance and the functional impact of key CuLncs on OC cell proliferation, invasion, and migration. 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. experiments revealed that knockdown of LINC01956, a key CuLnc in the risk signature, significantly inhibited the proliferation, invasion, and migration of OC cells (P<0.05). 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.
背景：卵巢癌（OC）是一种严重的恶性肿瘤，对女性健康构成重大威胁，其特点是死亡率高，尽管采用了细胞减灭术和铂类化疗等传统治疗方法，预后仍较差。铜死亡是一种由铜离子积累引发的新型细胞死亡形式，在癌症治疗中显示出潜力，特别是通过铜相关长链非编码RNA（CuLncs）的参与。本研究旨在确定OC中与CuLncs相关的风险特征，构建预后模型，并探索潜在治疗药物以及CuLncs对OC细胞行为的影响。 方法：我们分析了来自TCGA数据库的卵巢癌数据（TCGA-OV），包括376例患者的转录组和临床数据。使用Pearson相关性分析和LASSO回归，我们确定了8个预后CuLncs以构建风险特征模型。根据风险评分将患者分为高风险和低风险组。我们进行了生存分析、模型验证、药物敏感性分析以及实验，以评估模型的性能以及关键CuLncs对OC细胞增殖、侵袭和迁移的功能影响。 结果：该预后模型显示出显著的预测能力，1年生存曲线下面积（AUC）为0.702，3年为0.640，5年为0.618，优于分期和分级等临床病理特征。高风险OC患者表现出更高的肿瘤免疫功能障碍和排除（TIDE）评分，表明更强的免疫逃逸能力。JQ12、PD-0325901和索拉非尼等药物在高风险组中显示出降低的半数抑制浓度（IC50）值，表明具有潜在的治疗益处。实验表明，敲低风险特征中的关键CuLnc LINC01956可显著抑制OC细胞的增殖、侵袭和迁移（P<0.05）。 结论：我们的研究确定了基于CuLncs的预后风险模型，并探索了它们作为OC治疗靶点的潜力。研究结果突出了CuLncs在OC预后和免疫反应中的重要性，为未来研究和临床应用提供了新的见解。
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