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多组学剖析卵巢癌中超增强子驱动的致癌基因表达程序。

A multi-omic dissection of super-enhancer driven oncogenic gene expression programs in ovarian cancer.

机构信息

Lineberger Comprehensive Cancer Center, University of North Carolina at Chapel Hill, Chapel Hill, NC, 27599, USA.

Bioinformatics and Computational Biology Graduate Program, University of North Carolina at Chapel Hill, Chapel Hill, NC, 27599, USA.

出版信息

Nat Commun. 2022 Jul 22;13(1):4247. doi: 10.1038/s41467-022-31919-8.

Abstract

The human genome contains regulatory elements, such as enhancers, that are often rewired by cancer cells for the activation of genes that promote tumorigenesis and resistance to therapy. This is especially true for cancers that have little or no known driver mutations within protein coding genes, such as ovarian cancer. Herein, we utilize an integrated set of genomic and epigenomic datasets to identify clinically relevant super-enhancers that are preferentially amplified in ovarian cancer patients. We systematically probe the top 86 super-enhancers, using CRISPR-interference and CRISPR-deletion assays coupled to RNA-sequencing, to nominate two salient super-enhancers that drive proliferation and migration of cancer cells. Utilizing Hi-C, we construct chromatin interaction maps that enable the annotation of direct target genes for these super-enhancers and confirm their activity specifically within the cancer cell compartment of human tumors using single-cell genomics data. Together, our multi-omic approach examines a number of fundamental questions about how regulatory information encoded into super-enhancers drives gene expression networks that underlie the biology of ovarian cancer.

摘要

人类基因组包含调控元件,如增强子,这些元件经常被癌细胞重新布线,以激活促进肿瘤发生和对治疗产生抗性的基因。对于那些在蛋白质编码基因中几乎没有或没有已知驱动突变的癌症,情况尤其如此,例如卵巢癌。在此,我们利用一组整合的基因组和表观基因组数据集来识别临床上相关的超级增强子,这些超级增强子在卵巢癌患者中优先扩增。我们使用 CRISPR 干扰和 CRISPR 缺失测定法结合 RNA 测序,系统地探测了前 86 个超级增强子,从而提名了两个突出的超级增强子,它们驱动癌细胞的增殖和迁移。利用 Hi-C,我们构建染色质相互作用图谱,为这些超级增强子注释直接靶基因,并使用单细胞基因组学数据在人类肿瘤的癌细胞区室中确认它们的活性。总之,我们的多组学方法研究了一些关于超级增强子中编码的调控信息如何驱动基因表达网络的基本问题,这些网络是卵巢癌生物学的基础。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/a420/9307778/64626fdf6df8/41467_2022_31919_Fig1_HTML.jpg

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