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Bioinformatics analysis of gene expression profiles to identify causal genes in luminal B2 breast cancer
Patients with the luminal B subtype of breast cancer exhibit a poor prognosis, high metastatic risk and high incidence of chemotherapy resistance. Luminal B breast cancer is sub-classified into B1 and B2. The pathophysiological mechanism of luminal B2 breast cancer (LB2BC) progression has yet to be...
Autores principales: | , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
D.A. Spandidos
2017
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Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5727610/ https://www.ncbi.nlm.nih.gov/pubmed/29250180 http://dx.doi.org/10.3892/ol.2017.7256 |
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author | Wang, Jinguang Du, Qi Li, Chen |
author_facet | Wang, Jinguang Du, Qi Li, Chen |
author_sort | Wang, Jinguang |
collection | PubMed |
description | Patients with the luminal B subtype of breast cancer exhibit a poor prognosis, high metastatic risk and high incidence of chemotherapy resistance. Luminal B breast cancer is sub-classified into B1 and B2. The pathophysiological mechanism of luminal B2 breast cancer (LB2BC) progression has yet to be characterized. Therefore, the present study aimed to identify the genes involved in the pathogenesis of LB2BC. The data of 117 LB2BC expression profiles were downloaded from The Cancer Genome Atlas (TCGA) and differentially expressed genes (DEGs) were identified by comparison with non-tumor tissue expression profiles. Gene Ontology enrichment analysis, Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analysis and protein-protein interaction (PPI) networks were used to obtain insight into the functions of DEGs. Reverse transcription-quantitative polymerase chain reaction (RT-qPCR) analysis was performed to validate the expression level of DEGs in tissue samples. A total of 2,251 DEGs, including 759 upregulated and 1,492 downregulated genes, were identified between LB2BC and non-tumor tissues. The top 15 upregulated and downregulated genes were used to construct a PPI network: Epidermal growth factor receptor (EGFR), fibronectin-1 (FN1) and Polo-like kinase-1 had the highest connectivity degrees. KEGG analysis identified that DEGs were most significantly enriched in ‘focal adhesion’, ‘pathways in cancer’ and ‘ECM-receptor interaction’ pathways. The results of RT-qPCR demonstrated that EGFR was significantly downregulated in LB2BC, whereas FN1 was significantly upregulated, whereas neurotrophic receptor tyrosine kinase 2 (NTRK2) trended towards downregulation. In conclusion, the DEGs identified in the present study, including NTRK2, FN1 and EGFR, may serve pivotal roles in the tumorigenesis of LB2BC by affecting the ‘focal adhesion’, ‘pathways in cancer’ and ‘ECM-receptor interaction’ KEGG pathways. |
format | Online Article Text |
id | pubmed-5727610 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2017 |
publisher | D.A. Spandidos |
record_format | MEDLINE/PubMed |
spelling | pubmed-57276102017-12-17 Bioinformatics analysis of gene expression profiles to identify causal genes in luminal B2 breast cancer Wang, Jinguang Du, Qi Li, Chen Oncol Lett Articles Patients with the luminal B subtype of breast cancer exhibit a poor prognosis, high metastatic risk and high incidence of chemotherapy resistance. Luminal B breast cancer is sub-classified into B1 and B2. The pathophysiological mechanism of luminal B2 breast cancer (LB2BC) progression has yet to be characterized. Therefore, the present study aimed to identify the genes involved in the pathogenesis of LB2BC. The data of 117 LB2BC expression profiles were downloaded from The Cancer Genome Atlas (TCGA) and differentially expressed genes (DEGs) were identified by comparison with non-tumor tissue expression profiles. Gene Ontology enrichment analysis, Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analysis and protein-protein interaction (PPI) networks were used to obtain insight into the functions of DEGs. Reverse transcription-quantitative polymerase chain reaction (RT-qPCR) analysis was performed to validate the expression level of DEGs in tissue samples. A total of 2,251 DEGs, including 759 upregulated and 1,492 downregulated genes, were identified between LB2BC and non-tumor tissues. The top 15 upregulated and downregulated genes were used to construct a PPI network: Epidermal growth factor receptor (EGFR), fibronectin-1 (FN1) and Polo-like kinase-1 had the highest connectivity degrees. KEGG analysis identified that DEGs were most significantly enriched in ‘focal adhesion’, ‘pathways in cancer’ and ‘ECM-receptor interaction’ pathways. The results of RT-qPCR demonstrated that EGFR was significantly downregulated in LB2BC, whereas FN1 was significantly upregulated, whereas neurotrophic receptor tyrosine kinase 2 (NTRK2) trended towards downregulation. In conclusion, the DEGs identified in the present study, including NTRK2, FN1 and EGFR, may serve pivotal roles in the tumorigenesis of LB2BC by affecting the ‘focal adhesion’, ‘pathways in cancer’ and ‘ECM-receptor interaction’ KEGG pathways. D.A. Spandidos 2017-12 2017-10-23 /pmc/articles/PMC5727610/ /pubmed/29250180 http://dx.doi.org/10.3892/ol.2017.7256 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, Jinguang Du, Qi Li, Chen Bioinformatics analysis of gene expression profiles to identify causal genes in luminal B2 breast cancer |
title | Bioinformatics analysis of gene expression profiles to identify causal genes in luminal B2 breast cancer |
title_full | Bioinformatics analysis of gene expression profiles to identify causal genes in luminal B2 breast cancer |
title_fullStr | Bioinformatics analysis of gene expression profiles to identify causal genes in luminal B2 breast cancer |
title_full_unstemmed | Bioinformatics analysis of gene expression profiles to identify causal genes in luminal B2 breast cancer |
title_short | Bioinformatics analysis of gene expression profiles to identify causal genes in luminal B2 breast cancer |
title_sort | bioinformatics analysis of gene expression profiles to identify causal genes in luminal b2 breast cancer |
topic | Articles |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5727610/ https://www.ncbi.nlm.nih.gov/pubmed/29250180 http://dx.doi.org/10.3892/ol.2017.7256 |
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