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Immune subtype identification and multi-layer perceptron classifier construction for breast cancer
INTRODUCTION: Breast cancer is a heterogeneous tumor. Tumor microenvironment (TME) has an important effect on the proliferation, metastasis, treatment, and prognosis of breast cancer. METHODS: In this study, we calculated the relative proportion of tumor infiltrating immune cells (TIICs) in the brea...
Autores principales: | , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
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Frontiers Media S.A.
2022
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Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9780074/ https://www.ncbi.nlm.nih.gov/pubmed/36568197 http://dx.doi.org/10.3389/fonc.2022.943874 |
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author | Yang, Xinbo Zheng, Yuanjie Xing, Xianrong Sui, Xiaodan Jia, Weikuan Pan, Huali |
author_facet | Yang, Xinbo Zheng, Yuanjie Xing, Xianrong Sui, Xiaodan Jia, Weikuan Pan, Huali |
author_sort | Yang, Xinbo |
collection | PubMed |
description | INTRODUCTION: Breast cancer is a heterogeneous tumor. Tumor microenvironment (TME) has an important effect on the proliferation, metastasis, treatment, and prognosis of breast cancer. METHODS: In this study, we calculated the relative proportion of tumor infiltrating immune cells (TIICs) in the breast cancer TME, and used the consensus clustering algorithm to cluster the breast cancer subtypes. We also developed a multi-layer perceptron (MLP) classifier based on a deep learning framework to detect breast cancer subtypes, which 70% of the breast cancer research cohort was used for the model training and 30% for validation. RESULTS: By performing the K-means clustering algorithm, the research cohort was clustered into two subtypes. The Kaplan-Meier survival estimate analysis showed significant differences in the overall survival (OS) between the two identified subtypes. Estimating the difference in the relative proportion of TIICs showed that the two subtypes had significant differences in multiple immune cells, such as CD8, CD4, and regulatory T cells. Further, the expression level of immune checkpoint molecules (PDL1, CTLA4, LAG3, TIGIT, CD27, IDO1, ICOS) and tumor mutational burden (TMB) also showed significant differences between the two subtypes, indicating the clinical value of the two subtypes. Finally, we identified a 38-gene signature and developed a multilayer perceptron (MLP) classifier that combined multi-gene signature to identify breast cancer subtypes. The results showed that the classifier had an accuracy rate of 93.56% and can be robustly used for the breast cancer subtype diagnosis. CONCLUSION: Identification of breast cancer subtypes based on the immune signature in the tumor microenvironment can assist clinicians to effectively and accurately assess the progression of breast cancer and formulate different treatment strategies for different subtypes. |
format | Online Article Text |
id | pubmed-9780074 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
publisher | Frontiers Media S.A. |
record_format | MEDLINE/PubMed |
spelling | pubmed-97800742022-12-24 Immune subtype identification and multi-layer perceptron classifier construction for breast cancer Yang, Xinbo Zheng, Yuanjie Xing, Xianrong Sui, Xiaodan Jia, Weikuan Pan, Huali Front Oncol Oncology INTRODUCTION: Breast cancer is a heterogeneous tumor. Tumor microenvironment (TME) has an important effect on the proliferation, metastasis, treatment, and prognosis of breast cancer. METHODS: In this study, we calculated the relative proportion of tumor infiltrating immune cells (TIICs) in the breast cancer TME, and used the consensus clustering algorithm to cluster the breast cancer subtypes. We also developed a multi-layer perceptron (MLP) classifier based on a deep learning framework to detect breast cancer subtypes, which 70% of the breast cancer research cohort was used for the model training and 30% for validation. RESULTS: By performing the K-means clustering algorithm, the research cohort was clustered into two subtypes. The Kaplan-Meier survival estimate analysis showed significant differences in the overall survival (OS) between the two identified subtypes. Estimating the difference in the relative proportion of TIICs showed that the two subtypes had significant differences in multiple immune cells, such as CD8, CD4, and regulatory T cells. Further, the expression level of immune checkpoint molecules (PDL1, CTLA4, LAG3, TIGIT, CD27, IDO1, ICOS) and tumor mutational burden (TMB) also showed significant differences between the two subtypes, indicating the clinical value of the two subtypes. Finally, we identified a 38-gene signature and developed a multilayer perceptron (MLP) classifier that combined multi-gene signature to identify breast cancer subtypes. The results showed that the classifier had an accuracy rate of 93.56% and can be robustly used for the breast cancer subtype diagnosis. CONCLUSION: Identification of breast cancer subtypes based on the immune signature in the tumor microenvironment can assist clinicians to effectively and accurately assess the progression of breast cancer and formulate different treatment strategies for different subtypes. Frontiers Media S.A. 2022-12-08 /pmc/articles/PMC9780074/ /pubmed/36568197 http://dx.doi.org/10.3389/fonc.2022.943874 Text en Copyright © 2022 Yang, Zheng, Xing, Sui, Jia and Pan https://creativecommons.org/licenses/by/4.0/This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms. |
spellingShingle | Oncology Yang, Xinbo Zheng, Yuanjie Xing, Xianrong Sui, Xiaodan Jia, Weikuan Pan, Huali Immune subtype identification and multi-layer perceptron classifier construction for breast cancer |
title | Immune subtype identification and multi-layer perceptron classifier construction for breast cancer |
title_full | Immune subtype identification and multi-layer perceptron classifier construction for breast cancer |
title_fullStr | Immune subtype identification and multi-layer perceptron classifier construction for breast cancer |
title_full_unstemmed | Immune subtype identification and multi-layer perceptron classifier construction for breast cancer |
title_short | Immune subtype identification and multi-layer perceptron classifier construction for breast cancer |
title_sort | immune subtype identification and multi-layer perceptron classifier construction for breast cancer |
topic | Oncology |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9780074/ https://www.ncbi.nlm.nih.gov/pubmed/36568197 http://dx.doi.org/10.3389/fonc.2022.943874 |
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