Wang Jianxin, Wang Yuquan, Xing Ping, Liu Qianqi, Zhang Cong, Sui Yang, Wu Changjun
Department of Ultrasound, The First Affiliated Hospital of Harbin Medical University, Harbin, Heilongjiang 150001, P.R. China.
College of Bioinformatics, Harbin Medical University, Harbin, Heilongjiang 150086, P.R. China.
Oncol Lett. 2020 Aug;20(2):1906-1914. doi: 10.3892/ol.2020.11733. Epub 2020 Jun 16.
Hypoxia, an important component of the tumor microenvironment, plays a crucial role in the occurrence and progression of cancer. However, to the best of our knowledge, a systematic analysis of a hypoxia-related prognostic signature for breast cancer is lacking and is urgently required. Therefore, in the present study, RNA-seq data and clinical information were downloaded from The Cancer Genome Atlas (TCGA) and served as a discovery cohort. Cox proportional hazards regression analysis was performed to construct a 14-gene prognostic signature (PFKL, P4HA2, GRHPR, SDC3, PPP1R15A, SIAH2, NDRG1, BTG1, TPD52, MAFF, ISG20, LALBA, ERRFI1 and VHL). The hypoxia-related signature successfully predicted survival outcomes of the discovery cohort (P<0.001 for the TCGA dataset). Three independent Gene Expression Omnibus databases (GSE10886, GSE20685 and GSE96058) were used as validation cohorts to verify the value of the predictive signature (P=0.007 for GSE10886, P=0.021 for GSE20685, P<0.001 for GSE96058). In the present study, a robust predictive signature was developed for patients with breast cancer, and the findings revealed that the 14-gene hypoxia-related signature could serve as a potential prognostic biomarker for breast cancer.
缺氧是肿瘤微环境的一个重要组成部分，在癌症的发生和发展中起着关键作用。然而，据我们所知，目前缺乏对乳腺癌缺氧相关预后特征的系统分析，这一分析亟待开展。因此，在本研究中，从癌症基因组图谱（TCGA）下载了RNA测序数据和临床信息，并将其作为发现队列。进行Cox比例风险回归分析以构建一个由14个基因组成的预后特征（PFKL、P4HA2、GRHPR、SDC3、PPP1R15A、SIAH2、NDRG1、BTG1、TPD52、MAFF、ISG20、LALBA、ERRFI1和VHL）。缺氧相关特征成功预测了发现队列的生存结果（TCGA数据集的P<0.001）。使用三个独立的基因表达综合数据库（GSE10886、GSE20685和GSE96058）作为验证队列，以验证预测特征的价值（GSE10886的P=0.007，GSE20685的P=0.021，GSE缉058的P<0.001）。在本研究中，为乳腺癌患者开发了一种强大的预测特征，研究结果表明，这一由14个基因组成的缺氧相关特征可作为乳腺癌潜在的预后生物标志物。
