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Identification of necroptosis-related subtypes and prognosis model in triple negative breast cancer

BACKGROUND: Necroptosis is considered to be a new form of programmed necrotic cell death, which is associated with metastasis, progression and prognosis of various types of tumors. However, the potential role of necroptosis-related genes (NRGs) in the triple negative breast cancer (TNBC) is unclear....

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Autores principales: Pu, Shengyu, Zhou, Yudong, Xie, Peiling, Gao, Xiaoqian, Liu, Yang, Ren, Yu, He, Jianjun, Hao, Na
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Frontiers Media S.A. 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9437322/
https://www.ncbi.nlm.nih.gov/pubmed/36059470
http://dx.doi.org/10.3389/fimmu.2022.964118
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author Pu, Shengyu
Zhou, Yudong
Xie, Peiling
Gao, Xiaoqian
Liu, Yang
Ren, Yu
He, Jianjun
Hao, Na
author_facet Pu, Shengyu
Zhou, Yudong
Xie, Peiling
Gao, Xiaoqian
Liu, Yang
Ren, Yu
He, Jianjun
Hao, Na
author_sort Pu, Shengyu
collection PubMed
description BACKGROUND: Necroptosis is considered to be a new form of programmed necrotic cell death, which is associated with metastasis, progression and prognosis of various types of tumors. However, the potential role of necroptosis-related genes (NRGs) in the triple negative breast cancer (TNBC) is unclear. METHODS: We extracted the gene expression and relevant clinicopathological data of TNBC from The Cancer Genome Atlas (TCGA) databases and the Gene Expression Omnibus (GEO) databases. We analyzed the expression, somatic mutation, and copy number variation (CNV) of 67 NRGs in TNBC, and then observed their interaction, biological functions, and prognosis value. By performing Lasso and COX regression analysis, a NRGs-related risk model for predicting overall survival (OS) was constructed and its predictive capabilities were verified. Finally, the relationship between risk_score and immune cell infiltration, tumor microenvironment (TME), immune checkpoint, and tumor mutation burden (TMB), cancer stem cell (CSC) index, and drug sensitivity were analyzed. RESULTS: A total 67 NRGs were identified in our analysis. A small number of genes (23.81%) detected somatic mutation, most genes appeared to have a high frequency of CNV, and there was a close interaction between them. These genes were remarkably enriched in immune-related process. A seven-gene risk_score was generated, containing TPSG1, KRT6A, GPR19, EIF4EBP1, TLE1, SLC4A7, ESPN. The low-risk group has a better OS, higher immune score, TMB and CSC index, and lower IC50 value of common therapeutic agents in TNBC. To improve clinical practicability, we added age, stage_T and stage_N to the risk_score and construct a more comprehensive nomogram for predicting OS. It was verified that nomogram had good predictive capability, the AUC values for 1-, 3-, and 5-year OS were 0.847, 0.908, and 0.942. CONCLUSION: Our research identified the significant impact of NRGs on immunity and prognosis in TNBC. These findings were expected to provide a new strategy for personalize the treatment of TNBC and improve its clinical benefit.
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spelling pubmed-94373222022-09-03 Identification of necroptosis-related subtypes and prognosis model in triple negative breast cancer Pu, Shengyu Zhou, Yudong Xie, Peiling Gao, Xiaoqian Liu, Yang Ren, Yu He, Jianjun Hao, Na Front Immunol Immunology BACKGROUND: Necroptosis is considered to be a new form of programmed necrotic cell death, which is associated with metastasis, progression and prognosis of various types of tumors. However, the potential role of necroptosis-related genes (NRGs) in the triple negative breast cancer (TNBC) is unclear. METHODS: We extracted the gene expression and relevant clinicopathological data of TNBC from The Cancer Genome Atlas (TCGA) databases and the Gene Expression Omnibus (GEO) databases. We analyzed the expression, somatic mutation, and copy number variation (CNV) of 67 NRGs in TNBC, and then observed their interaction, biological functions, and prognosis value. By performing Lasso and COX regression analysis, a NRGs-related risk model for predicting overall survival (OS) was constructed and its predictive capabilities were verified. Finally, the relationship between risk_score and immune cell infiltration, tumor microenvironment (TME), immune checkpoint, and tumor mutation burden (TMB), cancer stem cell (CSC) index, and drug sensitivity were analyzed. RESULTS: A total 67 NRGs were identified in our analysis. A small number of genes (23.81%) detected somatic mutation, most genes appeared to have a high frequency of CNV, and there was a close interaction between them. These genes were remarkably enriched in immune-related process. A seven-gene risk_score was generated, containing TPSG1, KRT6A, GPR19, EIF4EBP1, TLE1, SLC4A7, ESPN. The low-risk group has a better OS, higher immune score, TMB and CSC index, and lower IC50 value of common therapeutic agents in TNBC. To improve clinical practicability, we added age, stage_T and stage_N to the risk_score and construct a more comprehensive nomogram for predicting OS. It was verified that nomogram had good predictive capability, the AUC values for 1-, 3-, and 5-year OS were 0.847, 0.908, and 0.942. CONCLUSION: Our research identified the significant impact of NRGs on immunity and prognosis in TNBC. These findings were expected to provide a new strategy for personalize the treatment of TNBC and improve its clinical benefit. Frontiers Media S.A. 2022-08-19 /pmc/articles/PMC9437322/ /pubmed/36059470 http://dx.doi.org/10.3389/fimmu.2022.964118 Text en Copyright © 2022 Pu, Zhou, Xie, Gao, Liu, Ren, He and Hao 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
Pu, Shengyu
Zhou, Yudong
Xie, Peiling
Gao, Xiaoqian
Liu, Yang
Ren, Yu
He, Jianjun
Hao, Na
Identification of necroptosis-related subtypes and prognosis model in triple negative breast cancer
title Identification of necroptosis-related subtypes and prognosis model in triple negative breast cancer
title_full Identification of necroptosis-related subtypes and prognosis model in triple negative breast cancer
title_fullStr Identification of necroptosis-related subtypes and prognosis model in triple negative breast cancer
title_full_unstemmed Identification of necroptosis-related subtypes and prognosis model in triple negative breast cancer
title_short Identification of necroptosis-related subtypes and prognosis model in triple negative breast cancer
title_sort identification of necroptosis-related subtypes and prognosis model in triple negative breast cancer
topic Immunology
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9437322/
https://www.ncbi.nlm.nih.gov/pubmed/36059470
http://dx.doi.org/10.3389/fimmu.2022.964118
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