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Establishment of a 6-signature risk model associated with cellular senescence for predicting the prognosis of breast cancer

This study focused on screening novel markers associated with cellular senescence for predicting the prognosis of breast cancer. The RNA-seq expression profile of BRCA and clinical data were obtained from TCGA. The pam algorithm was used to cluster patients based on senescence-related genes. The wei...

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Autores principales: Zhang, Xiu-Xia, Yu, Xin, Zhu, Li, Luo, Jun-Hua
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Lippincott Williams & Wilkins 2023
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10659633/
https://www.ncbi.nlm.nih.gov/pubmed/37986376
http://dx.doi.org/10.1097/MD.0000000000035923
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author Zhang, Xiu-Xia
Yu, Xin
Zhu, Li
Luo, Jun-Hua
author_facet Zhang, Xiu-Xia
Yu, Xin
Zhu, Li
Luo, Jun-Hua
author_sort Zhang, Xiu-Xia
collection PubMed
description This study focused on screening novel markers associated with cellular senescence for predicting the prognosis of breast cancer. The RNA-seq expression profile of BRCA and clinical data were obtained from TCGA. The pam algorithm was used to cluster patients based on senescence-related genes. The weighted gene co-expression network analysis was used to identify co-expressed genes, and LASSO-Cox analysis was performed to build a risk prognosis model. The performance of the model was also evaluated. We additionally explored the role of senescence in cancer development and possible regulatory mechanism. The patients were clustered into 2 subtypes. A total of 5259 genes significantly related to senescence were identified by weighted gene co-expression network analysis. LASSO-Cox finally established a 6-signature risk model (ADAMTS8, DCAF12L2, PCDHA10, PGK1, SLC16A2, and TMEM233) that exhibited favorable and stable performance in our training, validation, and whole BRCA datasets. Furthermore, the superiority of our model was also observed after comparing it to other published models. The 6-signature was proved to be an independent risk factor for prognosis. In addition, mechanism prediction implied the activation of glycometabolism processes such as glycolysis and TCA cycle under the condition of senescence. Glycometabolism pathways were further found to negatively correlate with the infiltration level of CD8 T-cells and natural killer cells but positively correlate with M2 macrophage infiltration and expressions of tissue degeneration biomarkers, which suggested the deficit immune surveillance and risk of tumor migration. The constructed 6-gene model based on cellular senescence could be an effective indicator for predicting the prognosis of BRCA.
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spelling pubmed-106596332023-11-17 Establishment of a 6-signature risk model associated with cellular senescence for predicting the prognosis of breast cancer Zhang, Xiu-Xia Yu, Xin Zhu, Li Luo, Jun-Hua Medicine (Baltimore) 5750 This study focused on screening novel markers associated with cellular senescence for predicting the prognosis of breast cancer. The RNA-seq expression profile of BRCA and clinical data were obtained from TCGA. The pam algorithm was used to cluster patients based on senescence-related genes. The weighted gene co-expression network analysis was used to identify co-expressed genes, and LASSO-Cox analysis was performed to build a risk prognosis model. The performance of the model was also evaluated. We additionally explored the role of senescence in cancer development and possible regulatory mechanism. The patients were clustered into 2 subtypes. A total of 5259 genes significantly related to senescence were identified by weighted gene co-expression network analysis. LASSO-Cox finally established a 6-signature risk model (ADAMTS8, DCAF12L2, PCDHA10, PGK1, SLC16A2, and TMEM233) that exhibited favorable and stable performance in our training, validation, and whole BRCA datasets. Furthermore, the superiority of our model was also observed after comparing it to other published models. The 6-signature was proved to be an independent risk factor for prognosis. In addition, mechanism prediction implied the activation of glycometabolism processes such as glycolysis and TCA cycle under the condition of senescence. Glycometabolism pathways were further found to negatively correlate with the infiltration level of CD8 T-cells and natural killer cells but positively correlate with M2 macrophage infiltration and expressions of tissue degeneration biomarkers, which suggested the deficit immune surveillance and risk of tumor migration. The constructed 6-gene model based on cellular senescence could be an effective indicator for predicting the prognosis of BRCA. Lippincott Williams & Wilkins 2023-11-17 /pmc/articles/PMC10659633/ /pubmed/37986376 http://dx.doi.org/10.1097/MD.0000000000035923 Text en Copyright © 2023 the Author(s). Published by Wolters Kluwer Health, Inc. https://creativecommons.org/licenses/by/4.0/This is an open access article distributed under the Creative Commons Attribution License 4.0 (CCBY) (https://creativecommons.org/licenses/by/4.0/) , which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
spellingShingle 5750
Zhang, Xiu-Xia
Yu, Xin
Zhu, Li
Luo, Jun-Hua
Establishment of a 6-signature risk model associated with cellular senescence for predicting the prognosis of breast cancer
title Establishment of a 6-signature risk model associated with cellular senescence for predicting the prognosis of breast cancer
title_full Establishment of a 6-signature risk model associated with cellular senescence for predicting the prognosis of breast cancer
title_fullStr Establishment of a 6-signature risk model associated with cellular senescence for predicting the prognosis of breast cancer
title_full_unstemmed Establishment of a 6-signature risk model associated with cellular senescence for predicting the prognosis of breast cancer
title_short Establishment of a 6-signature risk model associated with cellular senescence for predicting the prognosis of breast cancer
title_sort establishment of a 6-signature risk model associated with cellular senescence for predicting the prognosis of breast cancer
topic 5750
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10659633/
https://www.ncbi.nlm.nih.gov/pubmed/37986376
http://dx.doi.org/10.1097/MD.0000000000035923
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