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Identification of novel dysregulated key genes in Breast cancer through high throughput ChIP-Seq data analysis

Breast cancer is the most common cancer in women both in the developed and less developed countries, and it imposes a considerable threat to human health. Therefore, in order to develop effective targeted therapies against Breast cancer, a deep understanding of its underlying molecular mechanisms is...

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Detalles Bibliográficos
Autores principales: Raj, Utkarsh, Aier, Imlimaong, Semwal, Rahul, Varadwaj, Pritish Kumar
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Nature Publishing Group UK 2017
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5468232/
https://www.ncbi.nlm.nih.gov/pubmed/28607444
http://dx.doi.org/10.1038/s41598-017-03534-x
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author Raj, Utkarsh
Aier, Imlimaong
Semwal, Rahul
Varadwaj, Pritish Kumar
author_facet Raj, Utkarsh
Aier, Imlimaong
Semwal, Rahul
Varadwaj, Pritish Kumar
author_sort Raj, Utkarsh
collection PubMed
description Breast cancer is the most common cancer in women both in the developed and less developed countries, and it imposes a considerable threat to human health. Therefore, in order to develop effective targeted therapies against Breast cancer, a deep understanding of its underlying molecular mechanisms is required. The application of deep transcriptional sequencing has been found to be reported to provide an efficient genomic assay to delve into the insights of the diseases and may prove to be useful in the study of Breast cancer. In this study, ChIP-Seq data for normal samples and Breast cancer were compared, and differential peaks identified, based upon fold enrichment (with P-values obtained via t-tests). The Protein–protein interaction (PPI) network analysis was carried out, following which the highly connected genes were screened and studied, and the most promising ones were selected. Biological pathway involved in the process were then identified. Our findings regarding potential Breast cancer-related genes enhances the understanding of the disease and provides prognostic information in addition to standard tumor prognostic factors for future research.
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spelling pubmed-54682322017-06-14 Identification of novel dysregulated key genes in Breast cancer through high throughput ChIP-Seq data analysis Raj, Utkarsh Aier, Imlimaong Semwal, Rahul Varadwaj, Pritish Kumar Sci Rep Article Breast cancer is the most common cancer in women both in the developed and less developed countries, and it imposes a considerable threat to human health. Therefore, in order to develop effective targeted therapies against Breast cancer, a deep understanding of its underlying molecular mechanisms is required. The application of deep transcriptional sequencing has been found to be reported to provide an efficient genomic assay to delve into the insights of the diseases and may prove to be useful in the study of Breast cancer. In this study, ChIP-Seq data for normal samples and Breast cancer were compared, and differential peaks identified, based upon fold enrichment (with P-values obtained via t-tests). The Protein–protein interaction (PPI) network analysis was carried out, following which the highly connected genes were screened and studied, and the most promising ones were selected. Biological pathway involved in the process were then identified. Our findings regarding potential Breast cancer-related genes enhances the understanding of the disease and provides prognostic information in addition to standard tumor prognostic factors for future research. Nature Publishing Group UK 2017-06-12 /pmc/articles/PMC5468232/ /pubmed/28607444 http://dx.doi.org/10.1038/s41598-017-03534-x Text en © The Author(s) 2017 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 license, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons license 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 license, visit http://creativecommons.org/licenses/by/4.0/.
spellingShingle Article
Raj, Utkarsh
Aier, Imlimaong
Semwal, Rahul
Varadwaj, Pritish Kumar
Identification of novel dysregulated key genes in Breast cancer through high throughput ChIP-Seq data analysis
title Identification of novel dysregulated key genes in Breast cancer through high throughput ChIP-Seq data analysis
title_full Identification of novel dysregulated key genes in Breast cancer through high throughput ChIP-Seq data analysis
title_fullStr Identification of novel dysregulated key genes in Breast cancer through high throughput ChIP-Seq data analysis
title_full_unstemmed Identification of novel dysregulated key genes in Breast cancer through high throughput ChIP-Seq data analysis
title_short Identification of novel dysregulated key genes in Breast cancer through high throughput ChIP-Seq data analysis
title_sort identification of novel dysregulated key genes in breast cancer through high throughput chip-seq data analysis
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5468232/
https://www.ncbi.nlm.nih.gov/pubmed/28607444
http://dx.doi.org/10.1038/s41598-017-03534-x
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