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Immunometabolism characteristics and a potential prognostic risk model associated with TP53 mutations in breast cancer

TP53, a gene with high-frequency mutations, plays an important role in breast cancer (BC) development through metabolic regulation, but the relationship between TP53 mutation and metabolism in BC remains to be explored. Our study included 1,066 BC samples from The Cancer Genome Atlas (TCGA) database...

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Autores principales: Jiang, Mengping, Wu, Xiangyan, Bao, Shengnan, Wang, Xi, Qu, Fei, Liu, Qian, Huang, Xiang, Li, Wei, Tang, Jinhai, Yin, Yongmei
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Frontiers Media S.A. 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9353309/
https://www.ncbi.nlm.nih.gov/pubmed/35935965
http://dx.doi.org/10.3389/fimmu.2022.946468
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author Jiang, Mengping
Wu, Xiangyan
Bao, Shengnan
Wang, Xi
Qu, Fei
Liu, Qian
Huang, Xiang
Li, Wei
Tang, Jinhai
Yin, Yongmei
author_facet Jiang, Mengping
Wu, Xiangyan
Bao, Shengnan
Wang, Xi
Qu, Fei
Liu, Qian
Huang, Xiang
Li, Wei
Tang, Jinhai
Yin, Yongmei
author_sort Jiang, Mengping
collection PubMed
description TP53, a gene with high-frequency mutations, plays an important role in breast cancer (BC) development through metabolic regulation, but the relationship between TP53 mutation and metabolism in BC remains to be explored. Our study included 1,066 BC samples from The Cancer Genome Atlas (TCGA) database, 415 BC cases from the Gene Expression Omnibus (GEO) database, and two immunotherapy cohorts. We identified 92 metabolic genes associated with TP53 mutations by differential expression analysis between TP53 mutant and wild-type groups. Univariate Cox analysis was performed to evaluate the prognostic effects of 24 TP53 mutation-related metabolic genes. By unsupervised clustering and other bioinformatics methods, the survival differences and immunometabolism characteristics of the distinct clusters were illustrated. In a training set from TCGA cohort, we employed the least absolute shrinkage and selection operator (LASSO) regression method to construct a metabolic gene prognostic model associated with TP53 mutations, and the GEO cohort served as an external validation set. Based on bioinformatics, the connections between risk score and survival prognosis, tumor microenvironment (TME), immunotherapy response, metabolic activity, clinical characteristics, and gene characteristics were further analyzed. It is imperative to note that our model is a powerful and robust prognosis factor in comparison to other traditional clinical features and also has high accuracy and clinical usefulness validated by receiver operating characteristic (ROC) and decision curve analysis (DCA). Our findings deepen our understanding of the immune and metabolic characteristics underlying the TP53 mutant metabolic gene profile in BC, laying a foundation for the exploration of potential therapies targeting metabolic pathways. In addition, our model has promising predictive value in the prognosis of BC.
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spelling pubmed-93533092022-08-06 Immunometabolism characteristics and a potential prognostic risk model associated with TP53 mutations in breast cancer Jiang, Mengping Wu, Xiangyan Bao, Shengnan Wang, Xi Qu, Fei Liu, Qian Huang, Xiang Li, Wei Tang, Jinhai Yin, Yongmei Front Immunol Immunology TP53, a gene with high-frequency mutations, plays an important role in breast cancer (BC) development through metabolic regulation, but the relationship between TP53 mutation and metabolism in BC remains to be explored. Our study included 1,066 BC samples from The Cancer Genome Atlas (TCGA) database, 415 BC cases from the Gene Expression Omnibus (GEO) database, and two immunotherapy cohorts. We identified 92 metabolic genes associated with TP53 mutations by differential expression analysis between TP53 mutant and wild-type groups. Univariate Cox analysis was performed to evaluate the prognostic effects of 24 TP53 mutation-related metabolic genes. By unsupervised clustering and other bioinformatics methods, the survival differences and immunometabolism characteristics of the distinct clusters were illustrated. In a training set from TCGA cohort, we employed the least absolute shrinkage and selection operator (LASSO) regression method to construct a metabolic gene prognostic model associated with TP53 mutations, and the GEO cohort served as an external validation set. Based on bioinformatics, the connections between risk score and survival prognosis, tumor microenvironment (TME), immunotherapy response, metabolic activity, clinical characteristics, and gene characteristics were further analyzed. It is imperative to note that our model is a powerful and robust prognosis factor in comparison to other traditional clinical features and also has high accuracy and clinical usefulness validated by receiver operating characteristic (ROC) and decision curve analysis (DCA). Our findings deepen our understanding of the immune and metabolic characteristics underlying the TP53 mutant metabolic gene profile in BC, laying a foundation for the exploration of potential therapies targeting metabolic pathways. In addition, our model has promising predictive value in the prognosis of BC. Frontiers Media S.A. 2022-07-22 /pmc/articles/PMC9353309/ /pubmed/35935965 http://dx.doi.org/10.3389/fimmu.2022.946468 Text en Copyright © 2022 Jiang, Wu, Bao, Wang, Qu, Liu, Huang, Li, Tang and Yin 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 Immunology
Jiang, Mengping
Wu, Xiangyan
Bao, Shengnan
Wang, Xi
Qu, Fei
Liu, Qian
Huang, Xiang
Li, Wei
Tang, Jinhai
Yin, Yongmei
Immunometabolism characteristics and a potential prognostic risk model associated with TP53 mutations in breast cancer
title Immunometabolism characteristics and a potential prognostic risk model associated with TP53 mutations in breast cancer
title_full Immunometabolism characteristics and a potential prognostic risk model associated with TP53 mutations in breast cancer
title_fullStr Immunometabolism characteristics and a potential prognostic risk model associated with TP53 mutations in breast cancer
title_full_unstemmed Immunometabolism characteristics and a potential prognostic risk model associated with TP53 mutations in breast cancer
title_short Immunometabolism characteristics and a potential prognostic risk model associated with TP53 mutations in breast cancer
title_sort immunometabolism characteristics and a potential prognostic risk model associated with tp53 mutations in breast cancer
topic Immunology
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9353309/
https://www.ncbi.nlm.nih.gov/pubmed/35935965
http://dx.doi.org/10.3389/fimmu.2022.946468
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