Master transcription factors form interconnected circuitry and orchestrate transcriptional networ...

中文标题：在食管腺癌中核心转录因子形成了相互作用的回路并且策划了转录网络

文章主要思想：

1）少部分核心转录因子通过调控超级增强子有助于编排基因表达程序
2）肿瘤的恶性转化与表观的增强子活性的获得和缺失相关，导致转录调控的改变
3）由于食管鳞癌和腺癌的超级增强子全景不同导致了细胞特异的转录失调
4）食管腺癌中存在一个由核心转录因子构成的连通的转录回路 （ELF3, KLF5,GATA6 and EHF）
5）核心转录因子占据了大部分食管腺癌的超级增强子区域，促进细胞的生存和增殖
6）

Result(生物信息部分..实验部分有时间再看吧)

Characterisation of enhancer landscapes in oesophageal cancer and distinct super-enhancer landscapes in EAC and ECCC

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Identification of interconnected transcriptional circuitry formed by master TFs in EAC

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Master TFs cooperatively orchestrate the transcriptional network of eAC

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elF3, KlF5 and GATA6 cooperatively activate the super enhancer of elF3

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文章用到生物信息方法总结：

4C-seq data analysis ：

R package r3CSeq [1]

Computational analysis of ChIP-Seq data

Raw data processing and peak calling:

50-bp single-end and 150-bp paired-reads
(Bowtie1 (v1.1.2) and Bowtie2 (v2.2.6))

alignment parameters : –k2

PCR duplicates : Picard MarkDuplicates

peak calling :MACS2 (v 2.1.1.20160309)

–bdg –SPMR –nomodel –extsize 200 –q 0.01

Bedgraph files转bigwig用UCSC bedGraphToBigWig tool

Super enhancer识别：

ROSE (Rank Order of Super Enhancers)

Enhancer定义：

H3K27Ac peaks 2kb away from any TSS & within a distance of 12.5 kb

？Identify of differential peaks and & merging consensus peak ets：DiffBind

Identification of interconnected auto-regulatory circuitry :

1)1,253 TFs which were retrieved from the intersection of AnimalTFDB and TcoFdatabases
2)super-enhancer regions (500 bp both upstream and downstream) motif scanning with FIMO (v 4.12.0)
[要求 at least three binding motifs，candidate TFs occur in at least 60% of samples ]

metagene analysis

The matrix of signal distribution +/- 3Kb flanking the summits：
computeMatrix[18] in deepTools2.0 --skipZeros

Motif enrichment analysis

overlaps of their peak regions were parsed using bedtools intersect ，HOMER “findMotifsGenome.pl”

Computational analysis of TCGA DNA-methylation data

β-values ：SeSAMe Bioconductor package v1.0
TCGA original IDAT files were downloaded from the NCI Genomic Data Commons (https://gdc.cancer.gov).