Meta-analysis of integrated ChIP-seq and transcriptome data revealed genomic regions affected by estrogen receptor alpha in breast cancer

Abstract

Background: The largest group of patients with breast cancer are estrogen receptor-positive (ER⁺) type. The estrogen receptor acts as a transcription factor and triggers cell proliferation and differentiation. Hence, investigating ER-DNA interaction genomic regions can help identify genes directly regulated by ER and understand the mechanism of ER action in cancer progression.

Methods: In the present study, we employed a workflow to do a meta-analysis of ChIP-seq data of ER⁺ cell lines stimulated with 10 nM and 100 nM of E2. All publicly available data sets were re-analyzed with the same platform. Then, the known and unknown batch effects were removed. Finally, the meta-analysis was performed to obtain meta-differentially bound sites in estrogen-treated MCF7 cell lines compared to vehicles (as control). Also, the meta-analysis results were compared with the results of T47D cell lines for more precision. Enrichment analyses were also employed to find the functional importance of common meta-differentially bound sites and associated genes among both cell lines.

Results: Remarkably, POU5F1B, ZNF662, ZNF442, KIN, ZNF410, and SGSM2 transcription factors were recognized in the meta-analysis but not in individual studies. Enrichment of the meta-differentially bound sites resulted in the candidacy of pathways not previously reported in breast cancer. PCGF2, HNF1B, and ZBED6 transcription factors were also predicted through the enrichment analysis of associated genes. In addition, comparing the meta-analysis results of both ChIP-seq and RNA-seq data showed that many transcription factors affected by ER were up-regulated.

Conclusion: The meta-analysis of ChIP-seq data of estrogen-treated MCF7 cell line leads to the identification of new binding sites of ER that have not been previously reported. Also, enrichment of the meta-differentially bound sites and their associated genes revealed new terms and pathways involved in the development of breast cancer which should be examined in future in vitro and in vivo studies.

Keywords: Breast cancer; ChIP-seq; Estrogen receptor-positive; MCF7/T47D cell lines; Meta-analysis; RNA-seq.

Fig. 1

An overview of the meta-analysis steps performed in the present study. ChIP-seq datasets were retrieved from SRA-NCBI and ENA-EBI databases. Pre-processing and re-analyzing steps of datasets were conducted. Then, batch effects removal and meta-analysis were performed. Subsequently, peak set functional enrichment analysis (PSFEA) and ChIP Enrichment Analysis for meta-DBSs-associated common genes were performed using the Cistrome-GO database and ChEA3 database, respectively. The packages and methods employed are in bold form. TFBSs Transcription factor binding sites, DBSs Differentially bound sites, Meta-DBSs Meta-differentially bound sites

Fig. 2

The flowchart to select datasets. A total of 351 datasets from SRA-NCBI and ENA-EBI were evaluated. Finally, based on the four criteria described, eight studies on MCF7 and T47D cell lines treated with E2 were used in the present study

Fig. 3

A workflow for integration and meta-analysis of ChIP-seq TFBSs scores from several studies. Datasets were selected based on the same criteria and re-analyzed with the same platform. Then samples were integrated, normalized, and meta-analyzed. This process was performed for MCF7 cell lines, and meta-DBSs were obtained. Next, meta-DBSs-associated genes were identified with peak annotation. The packages used in each step are marked in blue. TFBSs Transcription factor binding sites, DBSs Differentially bound sites, Meta-DBSs Meta-differentially bound sites

Fig. 4

Shared meta-DBSs between meta-analysis and individual studies based on TFBSs associated genes in MCF7 and T47D cell lines treated with E2. Among the peaks associated genes, only TFs are displayed. Nine of these TFs, which are also among the top 50 TFs obtained from ChEA3, are shown in purple. The six TFs only identified through meta-analysis are shown in orange. The marked blue TFs are mitochondrial TFs. TFs Transcription factors, DBSs Differentially bound sites, Meta-DBSs Meta-differentially bound sites

Fig. 5

Genome-wide annotation of 7,308 meta-DBSs correlated with 617 common genes and response elements of ER between MCF7 and T47D cell lines treated with E2. A Distribution of ER-meta-DBSs relative to the nearest TSS across the human genome. B Pie plot of the ER-meta-DBSs percentages according to peak location across different genomic regions of the human genome. C Visualization of ER-meta-DBSs obtained from the UCSC genome browser (version hg38). D Venn pie of annotations and their overlap. TSS transcription start site

Fig. 6

ChIP Enrichment Analysis (ChEA) for 617 meta-DBSs-associated common genes. The bar graph integrated_meanRank for the top 50 TFs using ChEA3 in MCF7 and T47D cell lines treated with E2. Each color represents a library, and each bar's length indicates the weight of that TF in each library. TFs transcription Factors

Fig. 7

Peak set functional enrichment analysis (PSFEA) for 7,308 meta-DBSs, including GO and KEGG pathways. (A) The chart of the top 20 GO terms and (B) nine KEGG pathways was obtained from 7,308 meta-DBSs in MCF7 and T47D cell lines treated with E2. Also, TFs that enriched GO terms and KEGG pathways are shown along with the number of peaks. TFs Transcription factors, DBSs Differentially bound sites, Meta-DBSs Meta-differentially bound sites

Fig. 8

Sankey diagram for categories of KEGG pathways