Wu Song, Pan Ruilin, Lu Jibu, Wu Xiaoling, Xie Jingdong, Tang Hailin, Li Xing
State Key Laboratory of Oncology in South China, Collaborative Innovation Center of Cancer Medicine, Sun Yat-sen University Cancer Center, Guangzhou, China.
Department of Breast Surgery, The First People's Hospital of Foshan, Foshan, China.
Front Oncol. 2022 Jun 2;12:896927. doi: 10.3389/fonc.2022.896927. eCollection 2022.
Triple-negative breast cancer (TNBC) is the subtype with the worst prognosis of breast cancer. Ferroptosis, a novel iron-dependent programmed cell death, has an increasingly important role in tumorigenesis and development. However, there is still a lack of research on the relationship between ferroptosis-related genes and the prognosis of TNBC. In this study, we obtained the gene expression profile of TNBC patients and matched clinical data from The Cancer Genome Atlas (TCGA) database. Univariate Cox analysis was used to screen out ferroptosis-related genes associated with TNBC prognosis. Then, the least absolute shrinkage and selection operator (LASSO) regression analysis was employed to establish a prognostic prediction model. A 15-ferroptosis-related gene prognostic prediction model was developed, which classified patients into low-risk (LR) or high-risk (HR) groups. Kaplan-Meier analysis results showed that the prognosis of the LR group was better. The receiver operating characteristic (ROC) curve also confirmed the satisfactory predictive ability of this model. Evaluation of the immune microenvironment of TNBC patients in the HR and LR group suggested these 15 ferroptosis-related genes might affect the prognosis of TNBC by regulating the tumor microenvironment. Our prognostic model can provide a theoretical basis for accurate prognosis prediction of TNBC in clinical practice.
三阴性乳腺癌（TNBC）是乳腺癌预后最差的亚型。铁死亡是一种新型的铁依赖性程序性细胞死亡，在肿瘤发生发展中发挥着越来越重要的作用。然而，关于铁死亡相关基因与TNBC预后之间的关系仍缺乏研究。在本研究中，我们从癌症基因组图谱（TCGA）数据库中获取了TNBC患者的基因表达谱及匹配的临床数据。采用单因素Cox分析筛选出与TNBC预后相关的铁死亡相关基因。然后，运用最小绝对收缩和选择算子（LASSO）回归分析建立预后预测模型。构建了一个包含15个铁死亡相关基因的预后预测模型，该模型将患者分为低风险（LR）组或高风险（HR）组。Kaplan-Meier分析结果显示，LR组的预后较好。受试者工作特征（ROC）曲线也证实了该模型具有令人满意的预测能力。对HR组和LR组TNBC患者免疫微环境的评估表明，这15个铁死亡相关基因可能通过调节肿瘤微环境影响TNBC的预后。我们的预后模型可为临床实践中TNBC的准确预后预测提供理论依据。
