Department of Obstetrics and Gynecology, Shengjing Hospital of China Medical University, Shenyang 110004, Liaoning, China.
Liaoning Provincial Key Laboratory of Obstetrics and Gynecology, Shengjing Hospital of China Medical University, Shenyang 110004, Liaoning, China.
Carcinogenesis. 2023 May 15;44(1):80-92. doi: 10.1093/carcin/bgac082.
Ovarian cancer is one of the deadliest malignant tumors. Ferroptosis is closely related to various cancers, including ovarian cancer, but the genes involved in regulating ferroptosis in ovarian cancer are still unclear. The aim of this study is to construct a long non-coding RNA (lncRNA) signature related to ferroptosis and evaluate its relationship with the prognosis and clinicopathological characteristics of patients with ovarian cancer. In this study, a prognostic risk model comprising 18 lncRNAs related to ferroptosis was obtained. Compared to the low-risk group, the high-risk group based on the FerRLSig score had significantly poorer overall survival (P < 0.001). The receiver operating characteristics curve supported the accuracy of the model, established a prognostic nomogram combining FerRLSig and clinical characteristics, and showed a good prognosis and survival risk stratification predictive power. In addition, Gene Set Enrichment Analysis (GSEA) showed that FerRLSig was involved in many malignant tumor-related immunomodulatory pathways. Based on the risk model, we found that immune status and immunotherapy, chemotherapy, and targeted therapy were significantly different between the high-risk and low-risk groups. This study provided a more in-depth understanding of the molecular and signaling pathways of ferroptosis in ovarian cancer and showed the impact of tumor microenvironment on ovarian cancer, as well as provided a prognostic model for ovarian cancer patients to guide the clinical treatment of ovarian cancer.
卵巢癌是最致命的恶性肿瘤之一。铁死亡与包括卵巢癌在内的各种癌症密切相关，但调节卵巢癌中铁死亡的相关基因尚不清楚。本研究旨在构建与铁死亡相关的长非编码 RNA（lncRNA）特征，并评估其与卵巢癌患者预后和临床病理特征的关系。在本研究中，获得了一个包含 18 个与铁死亡相关的 lncRNA 的预后风险模型。与低风险组相比，基于 FerRLSig 评分的高风险组的总生存期明显更差（P<0.001）。接受者操作特征曲线支持该模型的准确性，建立了一个结合 FerRLSig 和临床特征的预后列线图，并显示了良好的预后和生存风险分层预测能力。此外，基因集富集分析（GSEA）表明 FerRLSig 参与了许多恶性肿瘤相关的免疫调节途径。基于风险模型，我们发现高危组和低危组之间的免疫状态和免疫治疗、化疗和靶向治疗有显著差异。本研究更深入地了解了卵巢癌中铁死亡的分子和信号通路，并显示了肿瘤微环境对卵巢癌的影响，为卵巢癌患者提供了预后模型，以指导卵巢癌的临床治疗。
