Wenzhou Collaborative Innovation Center of Gastrointestinal Cancer in Basic Research and Precision Medicine, Wenzhou Key Laboratory of Cancer-related Pathogens and Immunity, Department of Microbiology and Immunology, Institute of Molecular Virology and Immunology, Institute of Tropical Medicine, School of Basic Medical Sciences, Wenzhou Medical University, Wenzhou, China.
Department of Gynecologic Oncology, Women's Hospital, School of Medicine, Zhejiang University, Hangzhou, China.
Front Immunol. 2022 May 2;13:863484. doi: 10.3389/fimmu.2022.863484. eCollection 2022.
Serous ovarian carcinoma (SOC) is a gynecological malignancy with high mortality rates. Currently, there is a lack of reliable biomarkers for accurate SOC patient prognosis. Here, we analyzed SOC RNA-Seq data from The Cancer Genome Atlas (TCGA) to identify prognostic biomarkers. Through the pearson correlation analysis, univariate Cox regression analysis, and LASSO-penalized Cox regression analysis, we identified nine lncRNAs significantly associated with four types of RNA modification writers (mA, mA, APA, and A-I) and with the prognosis of SOC patients (0.05). Six writer-related lncRNAs were ultimately selected following multivariate Cox analysis. We established a risk prediction model based on these six lncRNAs and evaluated its prognostic value in multiple groups (training set, testing set, and entire set). Our risk prediction model could effectively predict the prognosis of SOC patients with different clinical characteristics and their responses to immunotherapy. Lastly, we validated the predictive reliability and sensitivity of the lncRNA-based model a nomogram. This study explored the association between RNA modification writer-related lncRNAs and SOC prognosis, providing a potential complement for the clinical management of SOC patients.
浆液性卵巢癌（SOC）是一种妇科恶性肿瘤，死亡率很高。目前，缺乏可靠的生物标志物来准确预测 SOC 患者的预后。在这里，我们分析了来自癌症基因组图谱（TCGA）的 SOC RNA-Seq 数据，以鉴定预后生物标志物。通过 pearson 相关分析、单因素 Cox 回归分析和 LASSO 惩罚 Cox 回归分析，我们确定了 9 个与四种 RNA 修饰写入器（mA、m6A、APA 和 A-I）以及 SOC 患者预后显著相关的 lncRNAs（0.05）。多因素 Cox 分析后最终选择了 6 个与写入器相关的 lncRNAs。我们基于这 6 个 lncRNAs 建立了风险预测模型，并在多个组（训练集、测试集和全集）中评估了其预后价值。我们的风险预测模型可以有效预测不同临床特征的 SOC 患者的预后及其对免疫治疗的反应。最后，我们使用列线图验证了基于 lncRNA 的模型的预测可靠性和敏感性。本研究探讨了 RNA 修饰写入器相关 lncRNAs 与 SOC 预后之间的关系，为 SOC 患者的临床管理提供了潜在的补充。
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