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Identification of key pathways and genes in response to trastuzumab treatment in breast cancer using bioinformatics analysis
Breast cancer (BC) is one of the leading causes of death among women worldwide. The gene expression profile GSE22358 was downloaded from the Gene Expression Omnibus (GEO) database, which included 154 operable early-stage breast cancer samples treated with neoadjuvant capecitabine plus docetaxel, wit...
Autores principales: | , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Impact Journals LLC
2018
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6114942/ https://www.ncbi.nlm.nih.gov/pubmed/30181805 http://dx.doi.org/10.18632/oncotarget.24605 |
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author | Zeng, Fanxin Fu, Jiangping Hu, Fang Tang, Yani Fang, Xiangdong Zeng, Fanwei Chu, Yanpeng |
author_facet | Zeng, Fanxin Fu, Jiangping Hu, Fang Tang, Yani Fang, Xiangdong Zeng, Fanwei Chu, Yanpeng |
author_sort | Zeng, Fanxin |
collection | PubMed |
description | Breast cancer (BC) is one of the leading causes of death among women worldwide. The gene expression profile GSE22358 was downloaded from the Gene Expression Omnibus (GEO) database, which included 154 operable early-stage breast cancer samples treated with neoadjuvant capecitabine plus docetaxel, with (34) or without trastuzumab (120), to identify gene signatures during trastuzumab treatment and uncover their potential mechanisms. The gene ontology (GO) and Kyoto Encyclopedia of Genes and Genomes pathway (KEGG) enrichment analyses were performed, and a protein–protein interaction (PPI) network of the differentially expressed genes (DEGs) was constructed by Cytoscape software. There were 2284 DEGs, including 1231 up-regulated genes enriched in DNA replication, protein N-linked glycosylation via asparagine, and response to toxic substances, while 1053 down-regulated genes were enriched in axon guidance, protein localization to plasma membrane, protein stabilization, and protein glycosylation. Eight hub genes were identified from the PPI network, including GSK3B, RAC1, PXN, ERBB2, HSP90AA1, FGF2, PIK3R1 and RAC2. Our experimental results showed that GSK3B was also highly expressed in breast cancer tissues and was associated with poor survival, as was β-catenin. In conclusion, the present study indicated that the identified DEGs and hub genes further our understanding of the molecular mechanisms underlying trastuzumab treatment in BC and highlighted GSK3B, which might be used as a molecular target for the treatment of BC. |
format | Online Article Text |
id | pubmed-6114942 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2018 |
publisher | Impact Journals LLC |
record_format | MEDLINE/PubMed |
spelling | pubmed-61149422018-09-04 Identification of key pathways and genes in response to trastuzumab treatment in breast cancer using bioinformatics analysis Zeng, Fanxin Fu, Jiangping Hu, Fang Tang, Yani Fang, Xiangdong Zeng, Fanwei Chu, Yanpeng Oncotarget Research Paper Breast cancer (BC) is one of the leading causes of death among women worldwide. The gene expression profile GSE22358 was downloaded from the Gene Expression Omnibus (GEO) database, which included 154 operable early-stage breast cancer samples treated with neoadjuvant capecitabine plus docetaxel, with (34) or without trastuzumab (120), to identify gene signatures during trastuzumab treatment and uncover their potential mechanisms. The gene ontology (GO) and Kyoto Encyclopedia of Genes and Genomes pathway (KEGG) enrichment analyses were performed, and a protein–protein interaction (PPI) network of the differentially expressed genes (DEGs) was constructed by Cytoscape software. There were 2284 DEGs, including 1231 up-regulated genes enriched in DNA replication, protein N-linked glycosylation via asparagine, and response to toxic substances, while 1053 down-regulated genes were enriched in axon guidance, protein localization to plasma membrane, protein stabilization, and protein glycosylation. Eight hub genes were identified from the PPI network, including GSK3B, RAC1, PXN, ERBB2, HSP90AA1, FGF2, PIK3R1 and RAC2. Our experimental results showed that GSK3B was also highly expressed in breast cancer tissues and was associated with poor survival, as was β-catenin. In conclusion, the present study indicated that the identified DEGs and hub genes further our understanding of the molecular mechanisms underlying trastuzumab treatment in BC and highlighted GSK3B, which might be used as a molecular target for the treatment of BC. Impact Journals LLC 2018-03-05 /pmc/articles/PMC6114942/ /pubmed/30181805 http://dx.doi.org/10.18632/oncotarget.24605 Text en Copyright: © 2018 Zeng 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 (http://creativecommons.org/licenses/by/3.0/) 3.0 (CC BY 3.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. |
spellingShingle | Research Paper Zeng, Fanxin Fu, Jiangping Hu, Fang Tang, Yani Fang, Xiangdong Zeng, Fanwei Chu, Yanpeng Identification of key pathways and genes in response to trastuzumab treatment in breast cancer using bioinformatics analysis |
title | Identification of key pathways and genes in response to trastuzumab treatment in breast cancer using bioinformatics analysis |
title_full | Identification of key pathways and genes in response to trastuzumab treatment in breast cancer using bioinformatics analysis |
title_fullStr | Identification of key pathways and genes in response to trastuzumab treatment in breast cancer using bioinformatics analysis |
title_full_unstemmed | Identification of key pathways and genes in response to trastuzumab treatment in breast cancer using bioinformatics analysis |
title_short | Identification of key pathways and genes in response to trastuzumab treatment in breast cancer using bioinformatics analysis |
title_sort | identification of key pathways and genes in response to trastuzumab treatment in breast cancer using bioinformatics analysis |
topic | Research Paper |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6114942/ https://www.ncbi.nlm.nih.gov/pubmed/30181805 http://dx.doi.org/10.18632/oncotarget.24605 |
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