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Recognition of Immune Microenvironment Landscape and Immune-Related Prognostic Genes in Breast Cancer

BACKGROUND: Breast cancer (BC) is the most common malignant tumor in women. The immunophenotype of tumor microenvironment (TME) has shown great therapeutic potential in tumor. METHOD: The transcriptome was obtained from TCGA and GEO data. Immune infiltration was analyzed by single-sample gene set en...

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Autores principales: Wang, Huiling, You, Shuo, Fang, Meng, Fang, Qian
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Hindawi 2020
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7683123/
https://www.ncbi.nlm.nih.gov/pubmed/33274208
http://dx.doi.org/10.1155/2020/3909416
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author Wang, Huiling
You, Shuo
Fang, Meng
Fang, Qian
author_facet Wang, Huiling
You, Shuo
Fang, Meng
Fang, Qian
author_sort Wang, Huiling
collection PubMed
description BACKGROUND: Breast cancer (BC) is the most common malignant tumor in women. The immunophenotype of tumor microenvironment (TME) has shown great therapeutic potential in tumor. METHOD: The transcriptome was obtained from TCGA and GEO data. Immune infiltration was analyzed by single-sample gene set enrichment (ssGSEA). The immune feature was constructed by Cox regression analysis. In addition, the coexpression of differential expression genes (DEGs) was identified. Through enrichment analysis, the function and pathway of module genes were identified. The somatic mutations related to immune characteristics were analyzed by Maftools. By using the consistency clustering algorithm, the molecular subtypes were constructed, and the overall survival time (OS) was predicted. RESULTS: Immune landscape can be divided into low immune infiltration and high immune infiltration. Cox regression analysis identified seven immune cells as protective factors of BC. In the coexpression modules for DEGs of high and low immune infiltration, module 1 was related to T cells and high immune infiltration. In particular, the area under the curve (AUC) value of hub gene SASH3 was the highest, and the correlation with T cells was stronger in the high immune infiltration. Enrichment analysis found that oxidative stress, T cell aggregation, and apoptosis were observed in high immune infiltration. In addition, TP53 was identified as the most important somatic gene mutation related to immune characteristics. Importantly, we also constructed seven immune cell-based breast cancer subtypes to predict OS. CONCLUSION: We evaluated the immune landscape of BC and constructed the gene characteristics related to the immune landscape. The potential of T cells and SASH3 in immunotherapy of BC was revealed, which may guide the development of new clinical treatment strategies.
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spelling pubmed-76831232020-12-02 Recognition of Immune Microenvironment Landscape and Immune-Related Prognostic Genes in Breast Cancer Wang, Huiling You, Shuo Fang, Meng Fang, Qian Biomed Res Int Research Article BACKGROUND: Breast cancer (BC) is the most common malignant tumor in women. The immunophenotype of tumor microenvironment (TME) has shown great therapeutic potential in tumor. METHOD: The transcriptome was obtained from TCGA and GEO data. Immune infiltration was analyzed by single-sample gene set enrichment (ssGSEA). The immune feature was constructed by Cox regression analysis. In addition, the coexpression of differential expression genes (DEGs) was identified. Through enrichment analysis, the function and pathway of module genes were identified. The somatic mutations related to immune characteristics were analyzed by Maftools. By using the consistency clustering algorithm, the molecular subtypes were constructed, and the overall survival time (OS) was predicted. RESULTS: Immune landscape can be divided into low immune infiltration and high immune infiltration. Cox regression analysis identified seven immune cells as protective factors of BC. In the coexpression modules for DEGs of high and low immune infiltration, module 1 was related to T cells and high immune infiltration. In particular, the area under the curve (AUC) value of hub gene SASH3 was the highest, and the correlation with T cells was stronger in the high immune infiltration. Enrichment analysis found that oxidative stress, T cell aggregation, and apoptosis were observed in high immune infiltration. In addition, TP53 was identified as the most important somatic gene mutation related to immune characteristics. Importantly, we also constructed seven immune cell-based breast cancer subtypes to predict OS. CONCLUSION: We evaluated the immune landscape of BC and constructed the gene characteristics related to the immune landscape. The potential of T cells and SASH3 in immunotherapy of BC was revealed, which may guide the development of new clinical treatment strategies. Hindawi 2020-11-15 /pmc/articles/PMC7683123/ /pubmed/33274208 http://dx.doi.org/10.1155/2020/3909416 Text en Copyright © 2020 Huiling Wang et al. https://creativecommons.org/licenses/by/4.0/ This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
spellingShingle Research Article
Wang, Huiling
You, Shuo
Fang, Meng
Fang, Qian
Recognition of Immune Microenvironment Landscape and Immune-Related Prognostic Genes in Breast Cancer
title Recognition of Immune Microenvironment Landscape and Immune-Related Prognostic Genes in Breast Cancer
title_full Recognition of Immune Microenvironment Landscape and Immune-Related Prognostic Genes in Breast Cancer
title_fullStr Recognition of Immune Microenvironment Landscape and Immune-Related Prognostic Genes in Breast Cancer
title_full_unstemmed Recognition of Immune Microenvironment Landscape and Immune-Related Prognostic Genes in Breast Cancer
title_short Recognition of Immune Microenvironment Landscape and Immune-Related Prognostic Genes in Breast Cancer
title_sort recognition of immune microenvironment landscape and immune-related prognostic genes in breast cancer
topic Research Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7683123/
https://www.ncbi.nlm.nih.gov/pubmed/33274208
http://dx.doi.org/10.1155/2020/3909416
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AT youshuo recognitionofimmunemicroenvironmentlandscapeandimmunerelatedprognosticgenesinbreastcancer
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AT fangqian recognitionofimmunemicroenvironmentlandscapeandimmunerelatedprognosticgenesinbreastcancer