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Network-based approach to identify prognosis-related genes in tamoxifen-treated patients with estrogen receptor-positive breast cancer
Tamoxifen is an estrogen receptor (ER) antagonist that is most commonly used for the treatment of ER-positive breast cancer. However, tamoxifen resistance remains a major cause of cancer recurrence and progression. Here, we aimed to identify hub genes implicated in the progression and prognosis of E...
Autores principales: | , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Portland Press Ltd.
2021
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8485391/ https://www.ncbi.nlm.nih.gov/pubmed/34406386 http://dx.doi.org/10.1042/BSR20203020 |
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author | Wang, Yanyan Gong, Xiaonan Zhang, Yujie |
author_facet | Wang, Yanyan Gong, Xiaonan Zhang, Yujie |
author_sort | Wang, Yanyan |
collection | PubMed |
description | Tamoxifen is an estrogen receptor (ER) antagonist that is most commonly used for the treatment of ER-positive breast cancer. However, tamoxifen resistance remains a major cause of cancer recurrence and progression. Here, we aimed to identify hub genes implicated in the progression and prognosis of ER-positive breast cancer following tamoxifen treatment. Microarray data (GSE9893) for 155 tamoxifen-treated primary ER-positive breast cancer samples were obtained from the Gene Expression Omnibus database. In total, 1706 differentially expressed genes (DEGs), including 859 up-regulated and 847 down-regulated genes, were identified between relapse and relapse-free samples. Weighted correlation network analysis clustered genes into 13 modules, among which the tan and blue modules were the most significantly related to prognosis. From these two modules, we further identified and validated two prognosis-related hub genes (G-rich RNA sequence binding factor 1 (GRSF1) and microtubule-associated protein τ (MAPT)) via survival analysis based on several publicly available datasets. High expression of GRSF1 predicted poor prognosis, whereas MAPT indicated favorable outcomes in ER-positive breast cancer. Using breast cancer cell lines and tissue samples, we confirmed that GRSF1 was significantly up-regulated and MAPT was down-regulated in the tamoxifen-resistant group compared with the tamoxifen-sensitive group. The prognostic value of GRSF1 and MAPT was also verified in 48 tamoxifen-treated ER-positive breast cancer patients in our hospital. Gene set enrichment analysis (GSEA) suggested that GRSF1 was potentially involved in RNA degradation and cell cycle pathways, while MAPT was strongly linked to immune-related signaling pathways. Taken together, our findings established novel prognostic biomarkers to predict tamoxifen sensitivity, which may facilitate individualized management of breast cancer. |
format | Online Article Text |
id | pubmed-8485391 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2021 |
publisher | Portland Press Ltd. |
record_format | MEDLINE/PubMed |
spelling | pubmed-84853912021-10-08 Network-based approach to identify prognosis-related genes in tamoxifen-treated patients with estrogen receptor-positive breast cancer Wang, Yanyan Gong, Xiaonan Zhang, Yujie Biosci Rep Bioinformatics Tamoxifen is an estrogen receptor (ER) antagonist that is most commonly used for the treatment of ER-positive breast cancer. However, tamoxifen resistance remains a major cause of cancer recurrence and progression. Here, we aimed to identify hub genes implicated in the progression and prognosis of ER-positive breast cancer following tamoxifen treatment. Microarray data (GSE9893) for 155 tamoxifen-treated primary ER-positive breast cancer samples were obtained from the Gene Expression Omnibus database. In total, 1706 differentially expressed genes (DEGs), including 859 up-regulated and 847 down-regulated genes, were identified between relapse and relapse-free samples. Weighted correlation network analysis clustered genes into 13 modules, among which the tan and blue modules were the most significantly related to prognosis. From these two modules, we further identified and validated two prognosis-related hub genes (G-rich RNA sequence binding factor 1 (GRSF1) and microtubule-associated protein τ (MAPT)) via survival analysis based on several publicly available datasets. High expression of GRSF1 predicted poor prognosis, whereas MAPT indicated favorable outcomes in ER-positive breast cancer. Using breast cancer cell lines and tissue samples, we confirmed that GRSF1 was significantly up-regulated and MAPT was down-regulated in the tamoxifen-resistant group compared with the tamoxifen-sensitive group. The prognostic value of GRSF1 and MAPT was also verified in 48 tamoxifen-treated ER-positive breast cancer patients in our hospital. Gene set enrichment analysis (GSEA) suggested that GRSF1 was potentially involved in RNA degradation and cell cycle pathways, while MAPT was strongly linked to immune-related signaling pathways. Taken together, our findings established novel prognostic biomarkers to predict tamoxifen sensitivity, which may facilitate individualized management of breast cancer. Portland Press Ltd. 2021-09-03 /pmc/articles/PMC8485391/ /pubmed/34406386 http://dx.doi.org/10.1042/BSR20203020 Text en © 2021 The Author(s). https://creativecommons.org/licenses/by/4.0/This is an open access article published by Portland Press Limited on behalf of the Biochemical Society and distributed under the Creative Commons Attribution License 4.0 (CC BY) (https://creativecommons.org/licenses/by/4.0/) . |
spellingShingle | Bioinformatics Wang, Yanyan Gong, Xiaonan Zhang, Yujie Network-based approach to identify prognosis-related genes in tamoxifen-treated patients with estrogen receptor-positive breast cancer |
title | Network-based approach to identify prognosis-related genes in tamoxifen-treated patients with estrogen receptor-positive breast cancer |
title_full | Network-based approach to identify prognosis-related genes in tamoxifen-treated patients with estrogen receptor-positive breast cancer |
title_fullStr | Network-based approach to identify prognosis-related genes in tamoxifen-treated patients with estrogen receptor-positive breast cancer |
title_full_unstemmed | Network-based approach to identify prognosis-related genes in tamoxifen-treated patients with estrogen receptor-positive breast cancer |
title_short | Network-based approach to identify prognosis-related genes in tamoxifen-treated patients with estrogen receptor-positive breast cancer |
title_sort | network-based approach to identify prognosis-related genes in tamoxifen-treated patients with estrogen receptor-positive breast cancer |
topic | Bioinformatics |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8485391/ https://www.ncbi.nlm.nih.gov/pubmed/34406386 http://dx.doi.org/10.1042/BSR20203020 |
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