Stem Cell Bank/Stem Cell Core Facility, Institute of Biochemistry and Cell Biology, Shanghai Institutes for Biological Sciences, Chinese Academy of Sciences, Shanghai, China.
Agricultural Genomics Institute at Shenzhen, Chinese Academy of Agricultural Sciences, Shenzhen, China.
Mol Oncol. 2019 Jul;13(7):1490-1502. doi: 10.1002/1878-0261.12491. Epub 2019 May 29.
As a critical feature of the tumor microenvironment, hypoxia is known to be a potent inducer of tumor metastasis, and it has been proposed that the initial steps in metastasis involve epithelial-mesenchymal transition (EMT). The strong correlation among hypoxia, EMT, and metastasis suggests that integrative assessment of gene expression and the DNA modification program of hypoxia-induced EMT via high-throughput sequencing technologies may increase our understanding of the molecular basis of tumor invasion and metastasis. Here, we present the genomewide transcriptional and epigenetic profiles of non-small-cell lung cancer (NSCLC) cells under normoxic and hypoxic conditions. We demonstrate that hypoxia induces EMT along with dynamic alterations of transcriptional expression and epigenetic modifications in both A549 and HCC827 cells. After training using a dataset from patients with invasive and noninvasive lung adenocarcinomas with an artificial neural network algorithm, a characteristic 17-gene panel was identified, consisting of genes involved in EMT, hypoxia response, glycometabolism, and epigenetic modifications. This 17-gene signature clearly stratified NSCLC patients with significant differences in overall survival across three independent datasets. Our study may be suitable as a basis for further selection of gene signatures to potentially guide prognostic stratification in patients with NSCLC.
作为肿瘤微环境的一个关键特征,缺氧已知是肿瘤转移的有力诱导剂,并且已经提出转移的初始步骤涉及上皮-间充质转化 (EMT)。缺氧、EMT 和转移之间的强烈相关性表明,通过高通量测序技术对缺氧诱导的 EMT 的基因表达和 DNA 修饰程序进行综合评估,可能会增加我们对肿瘤侵袭和转移的分子基础的理解。在这里,我们展示了非小细胞肺癌 (NSCLC) 细胞在常氧和缺氧条件下的全基因组转录组和表观遗传谱。我们证明,缺氧诱导 EMT 以及 A549 和 HCC827 细胞中转录表达和表观遗传修饰的动态变化。使用具有侵袭性和非侵袭性肺腺癌患者的数据集和人工神经网络算法进行训练后,确定了一个由 17 个基因组成的特征性基因面板,其中包括涉及 EMT、缺氧反应、糖代谢和表观遗传修饰的基因。这个 17 个基因的特征明显在三个独立的数据集中分层了 NSCLC 患者的总生存差异。我们的研究可能适合作为进一步选择基因特征的基础,以潜在地指导 NSCLC 患者的预后分层。