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An EMT-Related Gene Signature to Predict the Prognosis of Triple-Negative Breast Cancer

INTRODUCTION: Epithelial–mesenchymal transition (EMT) is an important biological process in tumor invasion and metastasis, and thus a potential indicator of the progression and drug resistance of breast cancer. This study comprehensively analyzed EMT-related genes in triple-negative breast cancer (T...

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Autores principales: Zhang, Bo, Zhao, Rong, Wang, Qi, Zhang, Ya-Jing, Yang, Liu, Yuan, Zhou-Jun, Yang, Jun, Wang, Qian-Jun, Yao, Liang
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Springer Healthcare 2023
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10499992/
https://www.ncbi.nlm.nih.gov/pubmed/37462865
http://dx.doi.org/10.1007/s12325-023-02577-z
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author Zhang, Bo
Zhao, Rong
Wang, Qi
Zhang, Ya-Jing
Yang, Liu
Yuan, Zhou-Jun
Yang, Jun
Wang, Qian-Jun
Yao, Liang
author_facet Zhang, Bo
Zhao, Rong
Wang, Qi
Zhang, Ya-Jing
Yang, Liu
Yuan, Zhou-Jun
Yang, Jun
Wang, Qian-Jun
Yao, Liang
author_sort Zhang, Bo
collection PubMed
description INTRODUCTION: Epithelial–mesenchymal transition (EMT) is an important biological process in tumor invasion and metastasis, and thus a potential indicator of the progression and drug resistance of breast cancer. This study comprehensively analyzed EMT-related genes in triple-negative breast cancer (TNBC) to develop an EMT-related prognostic gene signature. METHODS: With the application of The Cancer Genome Atlas (TCGA) database, Molecular Taxonomy of Breast Cancer International Consortium (METABRIC), and the Genotype-Tissue Expression (GTEx) database, we identified EMT-related signature genes (EMGs) by Cox univariate regression and LASSO regression analysis. Risk scores were calculated and used to divide patients with TNBC into high-risk group and low-risk groups by the median value. Kaplan–Meier (K–M) and receiver operating characteristic (ROC) curve analyses were applied for model validation. Independent prognostic predictors were used to develop nomograms. Then, we assessed the risk model in terms of the immune microenvironment, genetic alteration and DNA methylation effects on prognosis, the probability of response to immunotherapy and chemotherapy, and small molecule drugs predicted by The Connectivity Map (Cmap) database. RESULTS: Thirteen EMT-related genes with independent prognostic value were identified and used to stratify the patients with TNBC into high- and low-risk groups. The survival analysis revealed that patients in the high-risk group had significantly poorer overall survival than patients in the low-risk group. Populations of immune cells, including CD4 memory resting T cells, CD4 memory activated T cells, and activated dendritic cells, significantly differed between the high- and low-risk groups. Moreover, some therapeutic drugs to which the high-risk group might show sensitivity were identified. CONCLUSIONS: Our research identified the significant impact of EMGs on prognosis in TNBC, providing new strategies for personalizing TNBC treatment and improving clinical outcomes. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1007/s12325-023-02577-z.
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spelling pubmed-104999922023-09-15 An EMT-Related Gene Signature to Predict the Prognosis of Triple-Negative Breast Cancer Zhang, Bo Zhao, Rong Wang, Qi Zhang, Ya-Jing Yang, Liu Yuan, Zhou-Jun Yang, Jun Wang, Qian-Jun Yao, Liang Adv Ther Original Research INTRODUCTION: Epithelial–mesenchymal transition (EMT) is an important biological process in tumor invasion and metastasis, and thus a potential indicator of the progression and drug resistance of breast cancer. This study comprehensively analyzed EMT-related genes in triple-negative breast cancer (TNBC) to develop an EMT-related prognostic gene signature. METHODS: With the application of The Cancer Genome Atlas (TCGA) database, Molecular Taxonomy of Breast Cancer International Consortium (METABRIC), and the Genotype-Tissue Expression (GTEx) database, we identified EMT-related signature genes (EMGs) by Cox univariate regression and LASSO regression analysis. Risk scores were calculated and used to divide patients with TNBC into high-risk group and low-risk groups by the median value. Kaplan–Meier (K–M) and receiver operating characteristic (ROC) curve analyses were applied for model validation. Independent prognostic predictors were used to develop nomograms. Then, we assessed the risk model in terms of the immune microenvironment, genetic alteration and DNA methylation effects on prognosis, the probability of response to immunotherapy and chemotherapy, and small molecule drugs predicted by The Connectivity Map (Cmap) database. RESULTS: Thirteen EMT-related genes with independent prognostic value were identified and used to stratify the patients with TNBC into high- and low-risk groups. The survival analysis revealed that patients in the high-risk group had significantly poorer overall survival than patients in the low-risk group. Populations of immune cells, including CD4 memory resting T cells, CD4 memory activated T cells, and activated dendritic cells, significantly differed between the high- and low-risk groups. Moreover, some therapeutic drugs to which the high-risk group might show sensitivity were identified. CONCLUSIONS: Our research identified the significant impact of EMGs on prognosis in TNBC, providing new strategies for personalizing TNBC treatment and improving clinical outcomes. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1007/s12325-023-02577-z. Springer Healthcare 2023-07-18 2023 /pmc/articles/PMC10499992/ /pubmed/37462865 http://dx.doi.org/10.1007/s12325-023-02577-z Text en © The Author(s) 2023 https://creativecommons.org/licenses/by-nc/4.0/Open Access This article is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License, which permits any non-commercial 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-nc/4.0/ (https://creativecommons.org/licenses/by-nc/4.0/) .
spellingShingle Original Research
Zhang, Bo
Zhao, Rong
Wang, Qi
Zhang, Ya-Jing
Yang, Liu
Yuan, Zhou-Jun
Yang, Jun
Wang, Qian-Jun
Yao, Liang
An EMT-Related Gene Signature to Predict the Prognosis of Triple-Negative Breast Cancer
title An EMT-Related Gene Signature to Predict the Prognosis of Triple-Negative Breast Cancer
title_full An EMT-Related Gene Signature to Predict the Prognosis of Triple-Negative Breast Cancer
title_fullStr An EMT-Related Gene Signature to Predict the Prognosis of Triple-Negative Breast Cancer
title_full_unstemmed An EMT-Related Gene Signature to Predict the Prognosis of Triple-Negative Breast Cancer
title_short An EMT-Related Gene Signature to Predict the Prognosis of Triple-Negative Breast Cancer
title_sort emt-related gene signature to predict the prognosis of triple-negative breast cancer
topic Original Research
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10499992/
https://www.ncbi.nlm.nih.gov/pubmed/37462865
http://dx.doi.org/10.1007/s12325-023-02577-z
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