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A six-gene-based signature for breast cancer radiotherapy sensitivity estimation
Breast cancer (BRCA) represents the most common malignancy among women worldwide with high mortality. Radiotherapy is a prevalent therapeutic for BRCA that with heterogeneous effectiveness among patients. Here, we proposed to develop a gene expression-based signature for BRCA radiotherapy sensitivit...
Autores principales: | , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Portland Press Ltd.
2020
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7711058/ https://www.ncbi.nlm.nih.gov/pubmed/33179733 http://dx.doi.org/10.1042/BSR20202376 |
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author | Chen, Xing Zheng, Junjie Zhuo, Min ling Zhang, Ailong You, Zhenhui |
author_facet | Chen, Xing Zheng, Junjie Zhuo, Min ling Zhang, Ailong You, Zhenhui |
author_sort | Chen, Xing |
collection | PubMed |
description | Breast cancer (BRCA) represents the most common malignancy among women worldwide with high mortality. Radiotherapy is a prevalent therapeutic for BRCA that with heterogeneous effectiveness among patients. Here, we proposed to develop a gene expression-based signature for BRCA radiotherapy sensitivity estimation. Gene expression profiles of BRCA samples from the Cancer Genome Atlas (TCGA) and International Cancer Genome Consortium (ICGC) were obtained and used as training and independent testing dataset, respectively. Differential expression genes (DEGs) in BRCA samples compared with their paracancerous samples in the training set were identified by using the edgeR Bioconductor package. Univariate Cox regression analysis and LASSO Cox regression method were applied to screen optimal genes for constructing a radiotherapy sensitivity estimation signature. Nomogram combining independent prognostic factors was used to predict 1-, 3-, and 5-year OS of radiation-treated BRCA patients. Relative proportions of tumor infiltrating immune cells (TIICs) calculated by CIBERSORT and mRNA levels of key immune checkpoint receptors was adopted to explore the relation between the signature and tumor immune response. As a result, 603 DEGs were obtained in BRCA tumor samples, six of which were retained and used to construct the radiotherapy sensitivity prediction model. The signature was proved to be robust in both training and testing sets. In addition, the signature was closely related to the immune microenvironment of BRCA in the context of TIICs and immune checkpoint receptors’ mRNA levels. In conclusion, the present study obtained a radiotherapy sensitivity estimation signature for BRCA, which should shed new light in clinical and experimental research. |
format | Online Article Text |
id | pubmed-7711058 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2020 |
publisher | Portland Press Ltd. |
record_format | MEDLINE/PubMed |
spelling | pubmed-77110582020-12-08 A six-gene-based signature for breast cancer radiotherapy sensitivity estimation Chen, Xing Zheng, Junjie Zhuo, Min ling Zhang, Ailong You, Zhenhui Biosci Rep Cancer Breast cancer (BRCA) represents the most common malignancy among women worldwide with high mortality. Radiotherapy is a prevalent therapeutic for BRCA that with heterogeneous effectiveness among patients. Here, we proposed to develop a gene expression-based signature for BRCA radiotherapy sensitivity estimation. Gene expression profiles of BRCA samples from the Cancer Genome Atlas (TCGA) and International Cancer Genome Consortium (ICGC) were obtained and used as training and independent testing dataset, respectively. Differential expression genes (DEGs) in BRCA samples compared with their paracancerous samples in the training set were identified by using the edgeR Bioconductor package. Univariate Cox regression analysis and LASSO Cox regression method were applied to screen optimal genes for constructing a radiotherapy sensitivity estimation signature. Nomogram combining independent prognostic factors was used to predict 1-, 3-, and 5-year OS of radiation-treated BRCA patients. Relative proportions of tumor infiltrating immune cells (TIICs) calculated by CIBERSORT and mRNA levels of key immune checkpoint receptors was adopted to explore the relation between the signature and tumor immune response. As a result, 603 DEGs were obtained in BRCA tumor samples, six of which were retained and used to construct the radiotherapy sensitivity prediction model. The signature was proved to be robust in both training and testing sets. In addition, the signature was closely related to the immune microenvironment of BRCA in the context of TIICs and immune checkpoint receptors’ mRNA levels. In conclusion, the present study obtained a radiotherapy sensitivity estimation signature for BRCA, which should shed new light in clinical and experimental research. Portland Press Ltd. 2020-12-02 /pmc/articles/PMC7711058/ /pubmed/33179733 http://dx.doi.org/10.1042/BSR20202376 Text en © 2020 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 . |
spellingShingle | Cancer Chen, Xing Zheng, Junjie Zhuo, Min ling Zhang, Ailong You, Zhenhui A six-gene-based signature for breast cancer radiotherapy sensitivity estimation |
title | A six-gene-based signature for breast cancer radiotherapy sensitivity estimation |
title_full | A six-gene-based signature for breast cancer radiotherapy sensitivity estimation |
title_fullStr | A six-gene-based signature for breast cancer radiotherapy sensitivity estimation |
title_full_unstemmed | A six-gene-based signature for breast cancer radiotherapy sensitivity estimation |
title_short | A six-gene-based signature for breast cancer radiotherapy sensitivity estimation |
title_sort | six-gene-based signature for breast cancer radiotherapy sensitivity estimation |
topic | Cancer |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7711058/ https://www.ncbi.nlm.nih.gov/pubmed/33179733 http://dx.doi.org/10.1042/BSR20202376 |
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