Song Xuming, Zhang Te, Ding Hanlin, Feng Yipeng, Yang Wenmin, Yin Xuewen, Chen Bing, Liang Yingkuan, Mao Qixing, Xia Wenjie, Yu Guiping, Xu Lin, Dong Gaochao, Jiang Feng
Department of Thoracic Surgery, Nanjing Medical University Affiliated Cancer Hospital & Jiangsu Cancer Hospital & Jiangsu Institute of Cancer Research, 210009, Nanjing, P. R. China.
Jiangsu Key Laboratory of Molecular and Translational Cancer Research, Cancer Institute of Jiangsu Province, Nanjing, P. R. China.
Oncogenesis. 2022 Oct 10;11(1):61. doi: 10.1038/s41389-022-00436-0.
Lung adenocarcinoma (LUAD) exhibits high heterogeneity and is well known for its high genetic variation. Recently, the understanding of non-genetic variation provides a new perspective to study the heterogeneity of LUAD. Little is known about whether super-enhancers (SEs) may be primarily responsible for the inter-tumor heterogeneity of LUAD. We used super-enhancer RNA (seRNA) levels of a large-scale clinical well-annotated LUAD cohort to stratify patients into three clusters with different prognosis and other malignant characteristics. Mechanistically, estrogen-related receptor alpha (ERRα) in cluster 3-like cell lines acts as a cofactor of BRD4 to assist SE-promoter loops to activate glycolysis-related target gene expression, thereby promoting glycolysis and malignant progression, which confers a therapeutic vulnerability to glycolytic inhibitors. Our study identified three groups of patients according to seRNA levels, among which patients in cluster 3 have the worst prognosis and vulnerability of glycolysis dependency. We also proposed a 3-TF index model to stratify patients with glycolysis-addicted tumors according to tumor SE stratification.
肺腺癌(LUAD)具有高度异质性,以其高遗传变异而闻名。最近,对非遗传变异的认识为研究LUAD的异质性提供了新的视角。关于超级增强子(SEs)是否可能是LUAD肿瘤间异质性的主要原因,目前知之甚少。我们使用了一个大规模临床注释良好的LUAD队列的超级增强子RNA(seRNA)水平,将患者分为三个具有不同预后和其他恶性特征的簇。从机制上讲,3型样细胞系中的雌激素相关受体α(ERRα)作为BRD4的辅因子,协助SE-启动子环激活糖酵解相关靶基因表达,从而促进糖酵解和恶性进展,这赋予了对糖酵解抑制剂的治疗易感性。我们的研究根据seRNA水平确定了三组患者,其中3簇患者的预后最差,糖酵解依赖性最强。我们还提出了一个3-TF指数模型,根据肿瘤SE分层对糖酵解成瘾肿瘤患者进行分层。