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Comprehensive tissue-specific gene set enrichment analysis and transcription factor analysis of breast cancer by integrating 14 gene expression datasets

Breast cancer is the most commonly diagnosed malignancy in women. Several key genes and pathways have been proven to correlate with breast cancer pathology. This study sought to explore the differences in key transcription factors (TFs), transcriptional regulation networks and dysregulated pathways...

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Autores principales: Li, Wen-Xing, He, Kan, Tang, Ling, Dai, Shao-Xing, Li, Gong-Hua, Lv, Wen-Wen, Guo, Yi-Cheng, An, San-Qi, Wu, Guo-Ying, Liu, Dahai, Huang, Jing-Fei
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Impact Journals LLC 2016
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5351668/
https://www.ncbi.nlm.nih.gov/pubmed/28036274
http://dx.doi.org/10.18632/oncotarget.14286
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author Li, Wen-Xing
He, Kan
Tang, Ling
Dai, Shao-Xing
Li, Gong-Hua
Lv, Wen-Wen
Guo, Yi-Cheng
An, San-Qi
Wu, Guo-Ying
Liu, Dahai
Huang, Jing-Fei
author_facet Li, Wen-Xing
He, Kan
Tang, Ling
Dai, Shao-Xing
Li, Gong-Hua
Lv, Wen-Wen
Guo, Yi-Cheng
An, San-Qi
Wu, Guo-Ying
Liu, Dahai
Huang, Jing-Fei
author_sort Li, Wen-Xing
collection PubMed
description Breast cancer is the most commonly diagnosed malignancy in women. Several key genes and pathways have been proven to correlate with breast cancer pathology. This study sought to explore the differences in key transcription factors (TFs), transcriptional regulation networks and dysregulated pathways in different tissues in breast cancer. We employed 14 breast cancer datasets from NCBI-GEO and performed an integrated analysis in three different tissues including breast, blood and saliva. The results showed that there were eight genes (CEBPD, EGR1, EGR2, EGR3, FOS, FOSB, ID1 and NFIL3) down-regulated in breast tissue but up-regulated in blood tissue. Furthermore, we identified several unreported tissue-specific TFs that may contribute to breast cancer, including ATOH8, DMRT2, TBX15 and ZNF367. The dysregulation of these TFs damaged lipid metabolism, development, cell adhesion, proliferation, differentiation and metastasis processes. Among these pathways, the breast tissue showed the most serious impairment and the blood tissue showed a relatively moderate damage, whereas the saliva tissue was almost unaffected. This study could be helpful for future biomarker discovery, drug design, and therapeutic and predictive applications in breast cancers.
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spelling pubmed-53516682017-04-13 Comprehensive tissue-specific gene set enrichment analysis and transcription factor analysis of breast cancer by integrating 14 gene expression datasets Li, Wen-Xing He, Kan Tang, Ling Dai, Shao-Xing Li, Gong-Hua Lv, Wen-Wen Guo, Yi-Cheng An, San-Qi Wu, Guo-Ying Liu, Dahai Huang, Jing-Fei Oncotarget Research Paper Breast cancer is the most commonly diagnosed malignancy in women. Several key genes and pathways have been proven to correlate with breast cancer pathology. This study sought to explore the differences in key transcription factors (TFs), transcriptional regulation networks and dysregulated pathways in different tissues in breast cancer. We employed 14 breast cancer datasets from NCBI-GEO and performed an integrated analysis in three different tissues including breast, blood and saliva. The results showed that there were eight genes (CEBPD, EGR1, EGR2, EGR3, FOS, FOSB, ID1 and NFIL3) down-regulated in breast tissue but up-regulated in blood tissue. Furthermore, we identified several unreported tissue-specific TFs that may contribute to breast cancer, including ATOH8, DMRT2, TBX15 and ZNF367. The dysregulation of these TFs damaged lipid metabolism, development, cell adhesion, proliferation, differentiation and metastasis processes. Among these pathways, the breast tissue showed the most serious impairment and the blood tissue showed a relatively moderate damage, whereas the saliva tissue was almost unaffected. This study could be helpful for future biomarker discovery, drug design, and therapeutic and predictive applications in breast cancers. Impact Journals LLC 2016-12-21 /pmc/articles/PMC5351668/ /pubmed/28036274 http://dx.doi.org/10.18632/oncotarget.14286 Text en Copyright: © 2017 Li et al. http://creativecommons.org/licenses/by/3.0/ This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
spellingShingle Research Paper
Li, Wen-Xing
He, Kan
Tang, Ling
Dai, Shao-Xing
Li, Gong-Hua
Lv, Wen-Wen
Guo, Yi-Cheng
An, San-Qi
Wu, Guo-Ying
Liu, Dahai
Huang, Jing-Fei
Comprehensive tissue-specific gene set enrichment analysis and transcription factor analysis of breast cancer by integrating 14 gene expression datasets
title Comprehensive tissue-specific gene set enrichment analysis and transcription factor analysis of breast cancer by integrating 14 gene expression datasets
title_full Comprehensive tissue-specific gene set enrichment analysis and transcription factor analysis of breast cancer by integrating 14 gene expression datasets
title_fullStr Comprehensive tissue-specific gene set enrichment analysis and transcription factor analysis of breast cancer by integrating 14 gene expression datasets
title_full_unstemmed Comprehensive tissue-specific gene set enrichment analysis and transcription factor analysis of breast cancer by integrating 14 gene expression datasets
title_short Comprehensive tissue-specific gene set enrichment analysis and transcription factor analysis of breast cancer by integrating 14 gene expression datasets
title_sort comprehensive tissue-specific gene set enrichment analysis and transcription factor analysis of breast cancer by integrating 14 gene expression datasets
topic Research Paper
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5351668/
https://www.ncbi.nlm.nih.gov/pubmed/28036274
http://dx.doi.org/10.18632/oncotarget.14286
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