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A risk scoring system based on tumor microenvironment cells to predict prognosis and immune activity in triple-negative breast cancer
The tumor microenvironment (TME) interacting with the malignant cells plays a vital role in cancer development. Herein, we aim to establish and verify a scoring system based on the characteristics of TME cells for prognosis prediction and personalized treatment guidance in patients with triple-negat...
Autores principales: | , , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Springer Nature Singapore
2022
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9021102/ https://www.ncbi.nlm.nih.gov/pubmed/35061208 http://dx.doi.org/10.1007/s12282-021-01326-w |
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author | Yang, Anli Wu, Minqing Ni, Mengqian Zhang, Lijuan Li, Mingyue Wei, Peijun Yang, Yonggang Xiao, Weikai An, Xin |
author_facet | Yang, Anli Wu, Minqing Ni, Mengqian Zhang, Lijuan Li, Mingyue Wei, Peijun Yang, Yonggang Xiao, Weikai An, Xin |
author_sort | Yang, Anli |
collection | PubMed |
description | The tumor microenvironment (TME) interacting with the malignant cells plays a vital role in cancer development. Herein, we aim to establish and verify a scoring system based on the characteristics of TME cells for prognosis prediction and personalized treatment guidance in patients with triple-negative breast cancer (TNBC). 158 TNBC samples from The Cancer Genome Atlas (TCGA) were included as the training cohort, and Molecular Taxonomy of Breast Cancer International Consortium (METABRIC) (N = 297), as well as GSE58812 (N = 107), were included as the validation cohort. The enrichment scores of 64 immune and stromal cells were estimated by the xCell algorithm. In the training cohort, cells with prognostic significance were found out using univariate Cox regression analysis and further applied to the random survival forest (RSF) model. Based on the scores of M2 macrophages, CD8(+) T cells, and CD4(+) memory T cells, a risk scoring system was constructed, which divided TNBC patients into 4 phenotypes (M2(low), M2(high)CD8(+)T(high)CD4(+)T(high), M2(high)CD8(+)T(high)CD4(+)T(low), and M2(high)CD8(+)T(low)). Furthermore, types 1 and 2 patients were merged into the low-risk group, while types 3 and 4 patients were in the high-risk group. The low-risk group had superior survival outcomes than the high-risk one, which was further confirmed in the validation cohort. Moreover, in the low-risk group, immune-related pathways were significantly enriched, and a higher level of antitumoral immune cells and immune checkpoint molecules, including PD-L1, PD-1, and CTLA-4, could be observed. Additionally, consistent results were achieved in the SYSUCC cohort when the scoring system was applied. In summary, this novel scoring system might predict the survival and immune activity of patients and might serve as a potential index for immunotherapy. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1007/s12282-021-01326-w. |
format | Online Article Text |
id | pubmed-9021102 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
publisher | Springer Nature Singapore |
record_format | MEDLINE/PubMed |
spelling | pubmed-90211022022-05-04 A risk scoring system based on tumor microenvironment cells to predict prognosis and immune activity in triple-negative breast cancer Yang, Anli Wu, Minqing Ni, Mengqian Zhang, Lijuan Li, Mingyue Wei, Peijun Yang, Yonggang Xiao, Weikai An, Xin Breast Cancer Original Article The tumor microenvironment (TME) interacting with the malignant cells plays a vital role in cancer development. Herein, we aim to establish and verify a scoring system based on the characteristics of TME cells for prognosis prediction and personalized treatment guidance in patients with triple-negative breast cancer (TNBC). 158 TNBC samples from The Cancer Genome Atlas (TCGA) were included as the training cohort, and Molecular Taxonomy of Breast Cancer International Consortium (METABRIC) (N = 297), as well as GSE58812 (N = 107), were included as the validation cohort. The enrichment scores of 64 immune and stromal cells were estimated by the xCell algorithm. In the training cohort, cells with prognostic significance were found out using univariate Cox regression analysis and further applied to the random survival forest (RSF) model. Based on the scores of M2 macrophages, CD8(+) T cells, and CD4(+) memory T cells, a risk scoring system was constructed, which divided TNBC patients into 4 phenotypes (M2(low), M2(high)CD8(+)T(high)CD4(+)T(high), M2(high)CD8(+)T(high)CD4(+)T(low), and M2(high)CD8(+)T(low)). Furthermore, types 1 and 2 patients were merged into the low-risk group, while types 3 and 4 patients were in the high-risk group. The low-risk group had superior survival outcomes than the high-risk one, which was further confirmed in the validation cohort. Moreover, in the low-risk group, immune-related pathways were significantly enriched, and a higher level of antitumoral immune cells and immune checkpoint molecules, including PD-L1, PD-1, and CTLA-4, could be observed. Additionally, consistent results were achieved in the SYSUCC cohort when the scoring system was applied. In summary, this novel scoring system might predict the survival and immune activity of patients and might serve as a potential index for immunotherapy. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1007/s12282-021-01326-w. Springer Nature Singapore 2022-01-21 2022 /pmc/articles/PMC9021102/ /pubmed/35061208 http://dx.doi.org/10.1007/s12282-021-01326-w Text en © The Author(s) 2022 https://creativecommons.org/licenses/by/4.0/Open AccessThis article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/ (https://creativecommons.org/licenses/by/4.0/) . |
spellingShingle | Original Article Yang, Anli Wu, Minqing Ni, Mengqian Zhang, Lijuan Li, Mingyue Wei, Peijun Yang, Yonggang Xiao, Weikai An, Xin A risk scoring system based on tumor microenvironment cells to predict prognosis and immune activity in triple-negative breast cancer |
title | A risk scoring system based on tumor microenvironment cells to predict prognosis and immune activity in triple-negative breast cancer |
title_full | A risk scoring system based on tumor microenvironment cells to predict prognosis and immune activity in triple-negative breast cancer |
title_fullStr | A risk scoring system based on tumor microenvironment cells to predict prognosis and immune activity in triple-negative breast cancer |
title_full_unstemmed | A risk scoring system based on tumor microenvironment cells to predict prognosis and immune activity in triple-negative breast cancer |
title_short | A risk scoring system based on tumor microenvironment cells to predict prognosis and immune activity in triple-negative breast cancer |
title_sort | risk scoring system based on tumor microenvironment cells to predict prognosis and immune activity in triple-negative breast cancer |
topic | Original Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9021102/ https://www.ncbi.nlm.nih.gov/pubmed/35061208 http://dx.doi.org/10.1007/s12282-021-01326-w |
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