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Leveraging senescence-oxidative stress co-relation to predict prognosis and drug sensitivity in breast invasive carcinoma

INTRODUCTION: Female breast cancer has risen to be the most common malignancy worldwide, causing a huge disease burden for both patients and society. Both senescence and oxidative stress attach importance to cancer development and progression. However, the prognostic roles of senescence and oxidativ...

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Autores principales: Ye, Yinghui, Luo, Yulou, Guo, Tong, Zhang, Chenguang, Sun, Yutian, Xu, Anping, Ji, Ling, Ou, Jianghua, Wu, Shang Ying
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Frontiers Media S.A. 2023
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10437062/
https://www.ncbi.nlm.nih.gov/pubmed/37600707
http://dx.doi.org/10.3389/fendo.2023.1179050
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author Ye, Yinghui
Luo, Yulou
Guo, Tong
Zhang, Chenguang
Sun, Yutian
Xu, Anping
Ji, Ling
Ou, Jianghua
Wu, Shang Ying
author_facet Ye, Yinghui
Luo, Yulou
Guo, Tong
Zhang, Chenguang
Sun, Yutian
Xu, Anping
Ji, Ling
Ou, Jianghua
Wu, Shang Ying
author_sort Ye, Yinghui
collection PubMed
description INTRODUCTION: Female breast cancer has risen to be the most common malignancy worldwide, causing a huge disease burden for both patients and society. Both senescence and oxidative stress attach importance to cancer development and progression. However, the prognostic roles of senescence and oxidative stress remain obscure in breast cancer. In this present study, we attempted to establish a predictive model based on senescence-oxidative stress co-relation genes (SOSCRGs) and evaluate its clinical utility in multiple dimensions. METHODS: SOSCRGs were identified via correlation analysis. Transcriptome data and clinical information of patients with breast invasive carcinoma (BRCA) were accessed from The Cancer Genome Atlas (TCGA) and GSE96058. SVM algorithm was employed to process subtype classification of patients with BRCA based on SOSCRGs. LASSO regression analysis was utilized to establish the predictive model based on SOSCRGs. Analyses of the predictive model with regards to efficacy evaluation, subgroup analysis, clinical association, immune infiltration, functional strength, mutation feature, and drug sensitivity were organized. Single-cell analysis was applied to decipher the expression pattern of key SOSCRGs in the tumor microenvironment. Additionally, qPCR was conducted to check the expression levels of key SOSCRGs in five different breast cancer cell lines. RESULTS: A total of 246 SOSCRGs were identified. Two breast cancer subtypes were determined based on SOSCRGs and subtype 1 showed an active immune landscape. A SOSCRGs-based predictive model was subsequently developed and the risk score was clarified as independent prognostic predictors in breast cancer. A novel nomogram was constructed and exhibited favorable predictive capability. We further ascertained that the infiltration levels of immune cells and expressions of immune checkpoints were significantly influenced by the risk score. The two risk groups were characterized by distinct functional strengths. Sugar metabolism and glycolysis were significantly upregulated in the high risk group. The low risk group was deciphered to harbor PIK3CA mutation-driven tumorigenesis, while TP53 mutation was dominant in the high risk group. The analysis further revealed a significantly positive correlation between risk score and TMB. Patients in the low risk group may also sensitively respond to several drug agents. Single-cell analysis dissected that ERRFI1, ETS1, NDRG1, and ZMAT3 were expressed in the tumor microenvironment. Moreover, the expression levels of the seven SOSCRGs in five different breast cancer cell lines were quantified and compared by qPCR respectively. CONCLUSION: Multidimensional evaluations verified the clinical utility of the SOSCRGs-based predictive model to predict prognosis, aid clinical decision, and risk stratification for patients with breast cancer.
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spelling pubmed-104370622023-08-19 Leveraging senescence-oxidative stress co-relation to predict prognosis and drug sensitivity in breast invasive carcinoma Ye, Yinghui Luo, Yulou Guo, Tong Zhang, Chenguang Sun, Yutian Xu, Anping Ji, Ling Ou, Jianghua Wu, Shang Ying Front Endocrinol (Lausanne) Endocrinology INTRODUCTION: Female breast cancer has risen to be the most common malignancy worldwide, causing a huge disease burden for both patients and society. Both senescence and oxidative stress attach importance to cancer development and progression. However, the prognostic roles of senescence and oxidative stress remain obscure in breast cancer. In this present study, we attempted to establish a predictive model based on senescence-oxidative stress co-relation genes (SOSCRGs) and evaluate its clinical utility in multiple dimensions. METHODS: SOSCRGs were identified via correlation analysis. Transcriptome data and clinical information of patients with breast invasive carcinoma (BRCA) were accessed from The Cancer Genome Atlas (TCGA) and GSE96058. SVM algorithm was employed to process subtype classification of patients with BRCA based on SOSCRGs. LASSO regression analysis was utilized to establish the predictive model based on SOSCRGs. Analyses of the predictive model with regards to efficacy evaluation, subgroup analysis, clinical association, immune infiltration, functional strength, mutation feature, and drug sensitivity were organized. Single-cell analysis was applied to decipher the expression pattern of key SOSCRGs in the tumor microenvironment. Additionally, qPCR was conducted to check the expression levels of key SOSCRGs in five different breast cancer cell lines. RESULTS: A total of 246 SOSCRGs were identified. Two breast cancer subtypes were determined based on SOSCRGs and subtype 1 showed an active immune landscape. A SOSCRGs-based predictive model was subsequently developed and the risk score was clarified as independent prognostic predictors in breast cancer. A novel nomogram was constructed and exhibited favorable predictive capability. We further ascertained that the infiltration levels of immune cells and expressions of immune checkpoints were significantly influenced by the risk score. The two risk groups were characterized by distinct functional strengths. Sugar metabolism and glycolysis were significantly upregulated in the high risk group. The low risk group was deciphered to harbor PIK3CA mutation-driven tumorigenesis, while TP53 mutation was dominant in the high risk group. The analysis further revealed a significantly positive correlation between risk score and TMB. Patients in the low risk group may also sensitively respond to several drug agents. Single-cell analysis dissected that ERRFI1, ETS1, NDRG1, and ZMAT3 were expressed in the tumor microenvironment. Moreover, the expression levels of the seven SOSCRGs in five different breast cancer cell lines were quantified and compared by qPCR respectively. CONCLUSION: Multidimensional evaluations verified the clinical utility of the SOSCRGs-based predictive model to predict prognosis, aid clinical decision, and risk stratification for patients with breast cancer. Frontiers Media S.A. 2023-08-04 /pmc/articles/PMC10437062/ /pubmed/37600707 http://dx.doi.org/10.3389/fendo.2023.1179050 Text en Copyright © 2023 Ye, Luo, Guo, Zhang, Sun, Xu, Ji, Ou and Wu 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 Endocrinology
Ye, Yinghui
Luo, Yulou
Guo, Tong
Zhang, Chenguang
Sun, Yutian
Xu, Anping
Ji, Ling
Ou, Jianghua
Wu, Shang Ying
Leveraging senescence-oxidative stress co-relation to predict prognosis and drug sensitivity in breast invasive carcinoma
title Leveraging senescence-oxidative stress co-relation to predict prognosis and drug sensitivity in breast invasive carcinoma
title_full Leveraging senescence-oxidative stress co-relation to predict prognosis and drug sensitivity in breast invasive carcinoma
title_fullStr Leveraging senescence-oxidative stress co-relation to predict prognosis and drug sensitivity in breast invasive carcinoma
title_full_unstemmed Leveraging senescence-oxidative stress co-relation to predict prognosis and drug sensitivity in breast invasive carcinoma
title_short Leveraging senescence-oxidative stress co-relation to predict prognosis and drug sensitivity in breast invasive carcinoma
title_sort leveraging senescence-oxidative stress co-relation to predict prognosis and drug sensitivity in breast invasive carcinoma
topic Endocrinology
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10437062/
https://www.ncbi.nlm.nih.gov/pubmed/37600707
http://dx.doi.org/10.3389/fendo.2023.1179050
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