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

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...

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Autores principales: Piryaei, Zeynab, Salehi, Zahra, Ebrahimie, Esmaeil, Ebrahimi, Mansour, Kavousi, Kaveh
Formato: Online Artículo Texto
Lenguaje:English
Publicado: BioMed Central 2023
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10503144/
https://www.ncbi.nlm.nih.gov/pubmed/37715225
http://dx.doi.org/10.1186/s12920-023-01655-z
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author Piryaei, Zeynab
Salehi, Zahra
Ebrahimie, Esmaeil
Ebrahimi, Mansour
Kavousi, Kaveh
author_facet Piryaei, Zeynab
Salehi, Zahra
Ebrahimie, Esmaeil
Ebrahimi, Mansour
Kavousi, Kaveh
author_sort Piryaei, Zeynab
collection PubMed
description 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. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1186/s12920-023-01655-z.
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spelling pubmed-105031442023-09-16 Meta-analysis of integrated ChIP-seq and transcriptome data revealed genomic regions affected by estrogen receptor alpha in breast cancer Piryaei, Zeynab Salehi, Zahra Ebrahimie, Esmaeil Ebrahimi, Mansour Kavousi, Kaveh BMC Med Genomics Research 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. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1186/s12920-023-01655-z. BioMed Central 2023-09-15 /pmc/articles/PMC10503144/ /pubmed/37715225 http://dx.doi.org/10.1186/s12920-023-01655-z Text en © The Author(s) 2023 https://creativecommons.org/licenses/by/4.0/Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/ (https://creativecommons.org/licenses/by/4.0/) . The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/ (https://creativecommons.org/publicdomain/zero/1.0/) ) applies to the data made available in this article, unless otherwise stated in a credit line to the data.
spellingShingle Research
Piryaei, Zeynab
Salehi, Zahra
Ebrahimie, Esmaeil
Ebrahimi, Mansour
Kavousi, Kaveh
Meta-analysis of integrated ChIP-seq and transcriptome data revealed genomic regions affected by estrogen receptor alpha in breast cancer
title Meta-analysis of integrated ChIP-seq and transcriptome data revealed genomic regions affected by estrogen receptor alpha in breast cancer
title_full Meta-analysis of integrated ChIP-seq and transcriptome data revealed genomic regions affected by estrogen receptor alpha in breast cancer
title_fullStr Meta-analysis of integrated ChIP-seq and transcriptome data revealed genomic regions affected by estrogen receptor alpha in breast cancer
title_full_unstemmed Meta-analysis of integrated ChIP-seq and transcriptome data revealed genomic regions affected by estrogen receptor alpha in breast cancer
title_short Meta-analysis of integrated ChIP-seq and transcriptome data revealed genomic regions affected by estrogen receptor alpha in breast cancer
title_sort meta-analysis of integrated chip-seq and transcriptome data revealed genomic regions affected by estrogen receptor alpha in breast cancer
topic Research
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10503144/
https://www.ncbi.nlm.nih.gov/pubmed/37715225
http://dx.doi.org/10.1186/s12920-023-01655-z
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