Combined Analysis of ChIP Sequencing and Gene Expression Dataset in Breast Cancer.

Abstract

Breast cancer is a common malignancy in women and contribute largely to the cancer related death. The purpose of this study is to confirm the roles of GATA3 and identify potential biomarkers of breast cancer. Chromatin Immunoprecipitation combined with high-throughput sequencing (ChIP-Seq) (GSM1642515) and gene expression profiles (GSE24249) were downloaded from the Gene Expression Omnibus (GEO) database. Bowtie2 and MACS2 were used for the mapping and peak calling of the ChIP-Seq data respectively. ChIPseeker, a R bioconductor package was adopted for the annotation of the enriched peaks. For the gene expression profiles, we used affy and limma package to do normalization and differential expression analysis. The genes with fold change >2 and adjusted P-Value <0.05 were screened out. Besides, BETA (Binding and Expression Target Analysis) was used to do the combined analysis of ChIP-Seq and gene expression profiles. The Database for Annotation, Visualization and Integrated Discovery (DAVID) was used for the functional enrichment analysis of overlapping genes between the target genes and differential expression genes (DEGs). What's more, the protein-protein interaction (PPI) network of the overlapping genes was obtained through the Human Protein Reference Database (HPRD). A total of 46,487 peaks were identified for GATA3 and out of which, 3256 ones were found to located at -3000~0bp from the transcription start sites (TSS) of their nearby gene. A total of 236 down- and 343 up-regulated genes were screened out in GATA3 overexpression breast cancer samples compared with those in control. The combined analysis of ChIP-Seq and gene expression dataset showed GATA3 act as a repressor in breast cancer. Besides, 68 overlaps were obtained between the DEGs and genes included in peaks located at -3000~0bp from TSS. Gene Ontology (GO) terms and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathways related to cancer progression and gene regulation were found to be enriched in those overlaps. In the PPI network, NDRG1, JUP and etc. were found to directly interact with large number of genes, which might indicate their important roles in the progression of breast cancer.