Fischer Martin, Grossmann Patrick, Padi Megha, DeCaprio James A
Molecular Oncology, Medical School, University of Leipzig, Leipzig 04103, Germany Department of Medical Oncology, Dana-Farber Cancer Institute, Boston, MA 02215, USA Department of Medicine, Harvard Medical School, Boston, MA 02215, USA
Department of Radiation Oncology, Dana-Farber Cancer Institute, Brigham and Women's Hospital, Harvard Medical School, Boston, MA 02215, USA Department of Biostatistics & Computational Biology, Dana-Farber Cancer Institute, Boston, MA 02215, USA.
Nucleic Acids Res. 2016 Jul 27;44(13):6070-86. doi: 10.1093/nar/gkw523. Epub 2016 Jun 8.
Cell cycle (CC) and TP53 regulatory networks are frequently deregulated in cancer. While numerous genome-wide studies of TP53 and CC-regulated genes have been performed, significant variation between studies has made it difficult to assess regulation of any given gene of interest. To overcome the limitation of individual studies, we developed a meta-analysis approach to identify high confidence target genes that reflect their frequency of identification in independent datasets. Gene regulatory networks were generated by comparing differential expression of TP53 and CC-regulated genes with chromatin immunoprecipitation studies for TP53, RB1, E2F, DREAM, B-MYB, FOXM1 and MuvB. RNA-seq data from p21-null cells revealed that gene downregulation by TP53 generally requires p21 (CDKN1A). Genes downregulated by TP53 were also identified as CC genes bound by the DREAM complex. The transcription factors RB, E2F1 and E2F7 bind to a subset of DREAM target genes that function in G1/S of the CC while B-MYB, FOXM1 and MuvB control G2/M gene expression. Our approach yields high confidence ranked target gene maps for TP53, DREAM, MMB-FOXM1 and RB-E2F and enables prediction and distinction of CC regulation. A web-based atlas at www.targetgenereg.org enables assessing the regulation of any human gene of interest.
细胞周期(CC)和TP53调控网络在癌症中经常失调。虽然已经对TP53和CC调控基因进行了大量全基因组研究,但研究之间的显著差异使得评估任何给定感兴趣基因的调控变得困难。为了克服单个研究的局限性,我们开发了一种荟萃分析方法来识别高可信度的靶基因,这些基因反映了它们在独立数据集中的识别频率。通过将TP53和CC调控基因的差异表达与针对TP53、RB1、E2F、DREAM、B-MYB、FOXM1和MuvB的染色质免疫沉淀研究进行比较,生成了基因调控网络。来自p21基因缺失细胞的RNA测序数据表明,TP53介导的基因下调通常需要p21(CDKN1A)。被TP53下调的基因也被鉴定为与DREAM复合物结合的CC基因。转录因子RB、E2F1和E2F7与一部分在细胞周期G1/S期发挥作用的DREAM靶基因结合,而B-MYB、FOXM1和MuvB控制G2/M期的基因表达。我们的方法产生了针对TP53、DREAM、MMB-FOXM1和RB-E2F的高可信度排名靶基因图谱,并能够预测和区分细胞周期调控。www.targetgenereg.org上基于网络的图谱能够评估任何感兴趣的人类基因的调控情况。