Zhang Jiyan, Xi Jie, Huang Ping, Zeng Saitian
Department of Gynecologic Oncology, Cangzhou Central Hospital, Cangzhou, China.
Department I of Obstetrics and Gynecology, Cangzhou Central Hospital, Cangzhou, China.
Front Med (Lausanne). 2021 Mar 5;8:644053. doi: 10.3389/fmed.2021.644053. eCollection 2021.
This study aimed to explore ferroptosis-related mRNAs as potential therapeutic targets for ovarian cancer treatment. Molecular subtypes were classified based on ferroptosis-related mRNAs via ConsensusClusterPlus package. The differences in prognosis, stromal score, immune score, immune function, and immune checkpoints were assessed between subtypes. Small molecular drugs were predicted via the CMap database. The sensitivity to chemotherapy drugs was estimated through the GDSC. A LASSO Cox regression model was conducted via the glmnet package, followed by a nomogram model. Based on ferroptosis mRNA expression profile, two molecular subtypes (C1 and C2) were classified, with distinct clinical outcomes. C1 subtype exhibited higher stromal score, immune cell score (T helper, Treg, neutrophil) and immune function (APC co-inhibition, parainflammation and Type II IFN response). Higher mRNA expression levels of immune checkpoints (like PDCD1) were found in C1 than C2. Potential small molecular drugs (PI3K and mTOR inhibitors) were found for treatment of ovarian cancer. C1 was more sensitive to eight chemotherapy drugs (A.443654, AZD.0530, AZD6482, AZD7762, AZD8055, BAY.61.3606, Bicalutamide, and CGP.60474). A 15-ferroptosis-related mRNA signature was developed, which could robustly and independently predict the outcomes. Moreover, a nomogram was established combining the signature and age, which could intuitively and accurately predict the 5-year overall survival probability. Our study characterized two ferroptosis-related subtypes with distinct prognosis and tumor immune features, which could assist clinicians make decisions and individual therapy. Moreover, 15 ferroptosis-related mRNAs were identified, which could become potential therapeutic targets for ovarian cancer.
本研究旨在探索铁死亡相关mRNA作为卵巢癌治疗的潜在靶点。通过ConsensusClusterPlus软件包基于铁死亡相关mRNA对分子亚型进行分类。评估各亚型之间在预后、基质评分、免疫评分、免疫功能和免疫检查点方面的差异。通过CMap数据库预测小分子药物。通过GDSC评估对化疗药物的敏感性。使用glmnet软件包构建LASSO Cox回归模型,随后构建列线图模型。基于铁死亡mRNA表达谱,分类出两种分子亚型(C1和C2),其临床结局不同。C1亚型表现出更高的基质评分、免疫细胞评分(辅助性T细胞、调节性T细胞、中性粒细胞)和免疫功能(抗原呈递细胞共抑制、副炎症和II型干扰素反应)。C1中免疫检查点(如PDCD1)的mRNA表达水平高于C2。发现了用于治疗卵巢癌的潜在小分子药物(PI3K和mTOR抑制剂)。C1对八种化疗药物(A.443654、AZD.0530、AZD6482、AZD7762、AZD8055、BAY.61.3606、比卡鲁胺和CGP.60474)更敏感。开发了一个15个铁死亡相关mRNA特征,可有力且独立地预测结局。此外,建立了一个结合该特征和年龄的列线图,可直观且准确地预测5年总生存概率。我们的研究对两种具有不同预后和肿瘤免疫特征的铁死亡相关亚型进行了表征,这有助于临床医生做出决策和进行个体化治疗。此外,鉴定出15个铁死亡相关mRNA,它们可能成为卵巢癌的潜在治疗靶点。