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A Novel Prognostic Prediction Model Based on Pyroptosis-Related Clusters for Breast Cancer
Breast cancer (BC) is the most common cancer affecting women and the leading cause of cancer-related deaths worldwide. Compelling evidence indicates that pyroptosis is inextricably involved in the development of cancer and may activate tumor-specific immunity and/or enhance the effectiveness of exis...
Autores principales: | , , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
MDPI
2022
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9865451/ https://www.ncbi.nlm.nih.gov/pubmed/36675729 http://dx.doi.org/10.3390/jpm13010069 |
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author | Tian, Baoxing Yin, Kai Qiu, Xia Sun, Haidong Zhao, Ji Du, Yibao Gu, Yifan Wang, Xingyun Wang, Jie |
author_facet | Tian, Baoxing Yin, Kai Qiu, Xia Sun, Haidong Zhao, Ji Du, Yibao Gu, Yifan Wang, Xingyun Wang, Jie |
author_sort | Tian, Baoxing |
collection | PubMed |
description | Breast cancer (BC) is the most common cancer affecting women and the leading cause of cancer-related deaths worldwide. Compelling evidence indicates that pyroptosis is inextricably involved in the development of cancer and may activate tumor-specific immunity and/or enhance the effectiveness of existing therapies. We constructed a novel prognostic prediction model for BC, based on pyroptosis-related clusters, according to RNA-seq and clinical data downloaded from TCGA. The proportions of tumor-infiltrating immune cells differed significantly in the two pyroptosis clusters, which were determined according to 38 pyroptosis-related genes, and the immune-related pathways were activated according to GO and KEGG enrichment analysis. A 56-gene signature, constructed using univariate and multivariate Cox regression, was significantly associated with progression-free interval (PFI), disease-specific survival (DSS), and overall survival (OS) of patients with BC. Cox analysis revealed that the signature was significantly associated with the PFI and DSS of patients with BC. The signature could efficiently distinguish high- and low-risk patients and exhibited high sensitivity and specificity when predicting the prognosis of patients using KM and ROC analysis. Combined with clinical risk, patients in both the gene and clinical low-risk subgroup who received adjuvant chemotherapy had a significantly lower incidence of the clinical event than those who did not. This study presents a novel 56-gene prognostic signature significantly associated with PFI, DSS, and OS in patients with BC, which, combined with the TNM stage, might be a potential therapeutic strategy for individualized clinical decision-making. |
format | Online Article Text |
id | pubmed-9865451 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
publisher | MDPI |
record_format | MEDLINE/PubMed |
spelling | pubmed-98654512023-01-22 A Novel Prognostic Prediction Model Based on Pyroptosis-Related Clusters for Breast Cancer Tian, Baoxing Yin, Kai Qiu, Xia Sun, Haidong Zhao, Ji Du, Yibao Gu, Yifan Wang, Xingyun Wang, Jie J Pers Med Article Breast cancer (BC) is the most common cancer affecting women and the leading cause of cancer-related deaths worldwide. Compelling evidence indicates that pyroptosis is inextricably involved in the development of cancer and may activate tumor-specific immunity and/or enhance the effectiveness of existing therapies. We constructed a novel prognostic prediction model for BC, based on pyroptosis-related clusters, according to RNA-seq and clinical data downloaded from TCGA. The proportions of tumor-infiltrating immune cells differed significantly in the two pyroptosis clusters, which were determined according to 38 pyroptosis-related genes, and the immune-related pathways were activated according to GO and KEGG enrichment analysis. A 56-gene signature, constructed using univariate and multivariate Cox regression, was significantly associated with progression-free interval (PFI), disease-specific survival (DSS), and overall survival (OS) of patients with BC. Cox analysis revealed that the signature was significantly associated with the PFI and DSS of patients with BC. The signature could efficiently distinguish high- and low-risk patients and exhibited high sensitivity and specificity when predicting the prognosis of patients using KM and ROC analysis. Combined with clinical risk, patients in both the gene and clinical low-risk subgroup who received adjuvant chemotherapy had a significantly lower incidence of the clinical event than those who did not. This study presents a novel 56-gene prognostic signature significantly associated with PFI, DSS, and OS in patients with BC, which, combined with the TNM stage, might be a potential therapeutic strategy for individualized clinical decision-making. MDPI 2022-12-28 /pmc/articles/PMC9865451/ /pubmed/36675729 http://dx.doi.org/10.3390/jpm13010069 Text en © 2022 by the authors. https://creativecommons.org/licenses/by/4.0/Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/). |
spellingShingle | Article Tian, Baoxing Yin, Kai Qiu, Xia Sun, Haidong Zhao, Ji Du, Yibao Gu, Yifan Wang, Xingyun Wang, Jie A Novel Prognostic Prediction Model Based on Pyroptosis-Related Clusters for Breast Cancer |
title | A Novel Prognostic Prediction Model Based on Pyroptosis-Related Clusters for Breast Cancer |
title_full | A Novel Prognostic Prediction Model Based on Pyroptosis-Related Clusters for Breast Cancer |
title_fullStr | A Novel Prognostic Prediction Model Based on Pyroptosis-Related Clusters for Breast Cancer |
title_full_unstemmed | A Novel Prognostic Prediction Model Based on Pyroptosis-Related Clusters for Breast Cancer |
title_short | A Novel Prognostic Prediction Model Based on Pyroptosis-Related Clusters for Breast Cancer |
title_sort | novel prognostic prediction model based on pyroptosis-related clusters for breast cancer |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9865451/ https://www.ncbi.nlm.nih.gov/pubmed/36675729 http://dx.doi.org/10.3390/jpm13010069 |
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