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Integrated bioinformatics analysis reveals key candidate genes and pathways in breast cancer
Breast cancer (BC) is the leading malignancy in women worldwide, yet relatively little is known about the genes and signaling pathways involved in BC tumorigenesis and progression. The present study aimed to elucidate potential key candidate genes and pathways in BC. Five gene expression profile dat...
Autores principales: | , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
D.A. Spandidos
2018
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Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5983982/ https://www.ncbi.nlm.nih.gov/pubmed/29693125 http://dx.doi.org/10.3892/mmr.2018.8895 |
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author | Wang, Yuzhi Zhang, Yi Huang, Qian Li, Chengwen |
author_facet | Wang, Yuzhi Zhang, Yi Huang, Qian Li, Chengwen |
author_sort | Wang, Yuzhi |
collection | PubMed |
description | Breast cancer (BC) is the leading malignancy in women worldwide, yet relatively little is known about the genes and signaling pathways involved in BC tumorigenesis and progression. The present study aimed to elucidate potential key candidate genes and pathways in BC. Five gene expression profile data sets (GSE22035, GSE3744, GSE5764, GSE21422 and GSE26910) were downloaded from the Gene Expression Omnibus (GEO) database, which included data from 113 tumorous and 38 adjacent non-tumorous tissue samples. Differentially expressed genes (DEGs) were identified using t-tests in the limma R package. These DEGs were subsequently investigated by pathway enrichment analysis and a protein-protein interaction (PPI) network was constructed. The most significant module from the PPI network was selected for pathway enrichment analysis. In total, 227 DEGs were identified, of which 82 were upregulated and 145 were downregulated. Pathway enrichment analysis results revealed that the upregulated DEGs were mainly enriched in ‘cell division’, the ‘proteinaceous extracellular matrix (ECM)’, ‘ECM structural constituents’ and ‘ECM-receptor interaction’, whereas downregulated genes were mainly enriched in ‘response to drugs’, ‘extracellular space’, ‘transcriptional activator activity’ and the ‘peroxisome proliferator-activated receptor signaling pathway’. The PPI network contained 174 nodes and 1,257 edges. DNA topoisomerase 2-a, baculoviral inhibitor of apoptosis repeat-containing protein 5, cyclin-dependent kinase 1, G2/mitotic-specific cyclin-B1 and kinetochore protein NDC80 homolog were identified as the top 5 hub genes. Furthermore, the genes in the most significant module were predominantly involved in ‘mitotic nuclear division’, ‘mid-body’, ‘protein binding’ and ‘cell cycle’. In conclusion, the DEGs, relative pathways and hub genes identified in the present study may aid in understanding of the molecular mechanisms underlying BC progression and provide potential molecular targets and biomarkers for BC. |
format | Online Article Text |
id | pubmed-5983982 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2018 |
publisher | D.A. Spandidos |
record_format | MEDLINE/PubMed |
spelling | pubmed-59839822018-06-04 Integrated bioinformatics analysis reveals key candidate genes and pathways in breast cancer Wang, Yuzhi Zhang, Yi Huang, Qian Li, Chengwen Mol Med Rep Articles Breast cancer (BC) is the leading malignancy in women worldwide, yet relatively little is known about the genes and signaling pathways involved in BC tumorigenesis and progression. The present study aimed to elucidate potential key candidate genes and pathways in BC. Five gene expression profile data sets (GSE22035, GSE3744, GSE5764, GSE21422 and GSE26910) were downloaded from the Gene Expression Omnibus (GEO) database, which included data from 113 tumorous and 38 adjacent non-tumorous tissue samples. Differentially expressed genes (DEGs) were identified using t-tests in the limma R package. These DEGs were subsequently investigated by pathway enrichment analysis and a protein-protein interaction (PPI) network was constructed. The most significant module from the PPI network was selected for pathway enrichment analysis. In total, 227 DEGs were identified, of which 82 were upregulated and 145 were downregulated. Pathway enrichment analysis results revealed that the upregulated DEGs were mainly enriched in ‘cell division’, the ‘proteinaceous extracellular matrix (ECM)’, ‘ECM structural constituents’ and ‘ECM-receptor interaction’, whereas downregulated genes were mainly enriched in ‘response to drugs’, ‘extracellular space’, ‘transcriptional activator activity’ and the ‘peroxisome proliferator-activated receptor signaling pathway’. The PPI network contained 174 nodes and 1,257 edges. DNA topoisomerase 2-a, baculoviral inhibitor of apoptosis repeat-containing protein 5, cyclin-dependent kinase 1, G2/mitotic-specific cyclin-B1 and kinetochore protein NDC80 homolog were identified as the top 5 hub genes. Furthermore, the genes in the most significant module were predominantly involved in ‘mitotic nuclear division’, ‘mid-body’, ‘protein binding’ and ‘cell cycle’. In conclusion, the DEGs, relative pathways and hub genes identified in the present study may aid in understanding of the molecular mechanisms underlying BC progression and provide potential molecular targets and biomarkers for BC. D.A. Spandidos 2018-06 2018-04-19 /pmc/articles/PMC5983982/ /pubmed/29693125 http://dx.doi.org/10.3892/mmr.2018.8895 Text en Copyright: © Wang et al. This is an open access article distributed under the terms of the Creative Commons Attribution-NonCommercial-NoDerivs License (https://creativecommons.org/licenses/by-nc-nd/4.0/) , which permits use and distribution in any medium, provided the original work is properly cited, the use is non-commercial and no modifications or adaptations are made. |
spellingShingle | Articles Wang, Yuzhi Zhang, Yi Huang, Qian Li, Chengwen Integrated bioinformatics analysis reveals key candidate genes and pathways in breast cancer |
title | Integrated bioinformatics analysis reveals key candidate genes and pathways in breast cancer |
title_full | Integrated bioinformatics analysis reveals key candidate genes and pathways in breast cancer |
title_fullStr | Integrated bioinformatics analysis reveals key candidate genes and pathways in breast cancer |
title_full_unstemmed | Integrated bioinformatics analysis reveals key candidate genes and pathways in breast cancer |
title_short | Integrated bioinformatics analysis reveals key candidate genes and pathways in breast cancer |
title_sort | integrated bioinformatics analysis reveals key candidate genes and pathways in breast cancer |
topic | Articles |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5983982/ https://www.ncbi.nlm.nih.gov/pubmed/29693125 http://dx.doi.org/10.3892/mmr.2018.8895 |
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