Gu Peng, Zhang Lei, Wang Ruitao, Ding Wentao, Wang Wei, Liu Yuan, Wang Wenhao, Li Zuyin, Yan Bin, Sun Xing
Department of General Surgery, Shanghai General Hospital, School of Medicine, Shanghai Jiao Tong University, Shanghai, China.
Department of Vascular Surgery, Intervention Center, Shanghai General Hospital, School of Medicine, Shanghai Jiao Tong University, Shanghai, China.
Front Cell Dev Biol. 2021 Dec 16;9:796729. doi: 10.3389/fcell.2021.796729. eCollection 2021.
Female breast cancer is currently the most frequently diagnosed cancer in the world. This study aimed to develop and validate a novel hypoxia-related long noncoding RNA (HRL) prognostic model for predicting the overall survival (OS) of patients with breast cancer. The gene expression profiles were downloaded from The Cancer Genome Atlas (TCGA) database. A total of 200 hypoxia-related mRNAs were obtained from the Molecular Signatures Database. The co-expression analysis between differentially expressed hypoxia-related mRNAs and lncRNAs based on Spearman's rank correlation was performed to screen out 166 HRLs. Based on univariate Cox regression and least absolute shrinkage and selection operator Cox regression analysis in the training set, we filtered out 12 optimal prognostic hypoxia-related lncRNAs (PHRLs) to develop a prognostic model. Kaplan-Meier survival analysis, receiver operating characteristic curves, area under the curve, and univariate and multivariate Cox regression analyses were used to test the predictive ability of the risk model in the training, testing, and total sets. A 12-HRL prognostic model was developed to predict the survival outcome of patients with breast cancer. Patients in the high-risk group had significantly shorter median OS, DFS (disease-free survival), and predicted lower chemosensitivity (paclitaxel, docetaxel) compared with those in the low-risk group. Also, the risk score based on the expression of the 12 HRLs acted as an independent prognostic factor. The immune cell infiltration analysis revealed that the immune scores of patients in the high-risk group were lower than those of the patients in the low-risk group. RT-qPCR assays were conducted to verify the expression of the 12 PHRLs in breast cancer tissues and cell lines. Our study uncovered dozens of potential prognostic biomarkers and therapeutic targets related to the hypoxia signaling pathway in breast cancer.
女性乳腺癌是目前全球诊断最为频繁的癌症。本研究旨在开发并验证一种新型的缺氧相关长链非编码RNA（HRL）预后模型，用于预测乳腺癌患者的总生存期（OS）。基因表达谱数据从癌症基因组图谱（TCGA）数据库下载。从分子特征数据库中总共获取了200个缺氧相关的信使核糖核酸（mRNA）。基于斯皮尔曼等级相关性进行差异表达的缺氧相关mRNA与长链非编码RNA（lncRNA）之间的共表达分析，以筛选出166个HRL。在训练集中基于单因素Cox回归以及最小绝对收缩和选择算子Cox回归分析，我们筛选出12个最佳的预后缺氧相关lncRNA（PHRL）以建立一个预后模型。采用Kaplan-Meier生存分析、受试者工作特征曲线、曲线下面积以及单因素和多因素Cox回归分析来检验风险模型在训练集、测试集和总样本集中的预测能力。构建了一个12-HRL预后模型来预测乳腺癌患者的生存结局。与低风险组相比，高风险组患者的中位OS、无病生存期（DFS）显著更短，并且预测的化疗敏感性（紫杉醇、多西他赛）更低。此外，基于12个HRL表达的风险评分是一个独立的预后因素。免疫细胞浸润分析显示，高风险组患者的免疫评分低于低风险组患者。进行逆转录定量聚合酶链反应（RT-qPCR）检测以验证12个PHRL在乳腺癌组织和细胞系中的表达。我们的研究发现了数十种与乳腺癌缺氧信号通路相关的潜在预后生物标志物和治疗靶点。
