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A pyroptosis-related gene signature predicting survival and tumor immune microenvironment in breast cancer and validation
BACKGROUND: Pyroptosis is a newly discovered form of cell programmed necrosis, but its role and mechanism in cancer cells remain unclear. The aim of this study is to systematically analyze the transcriptional sequencing data of breast cancer (BC) to find a pyroptosis-related prognostic marker to pre...
Autores principales: | , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
BioMed Central
2022
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9494840/ https://www.ncbi.nlm.nih.gov/pubmed/36138348 http://dx.doi.org/10.1186/s12885-022-09856-y |
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author | Gong, Mingkai Liu, Xiangping Zhao, Xian Wang, Haibo |
author_facet | Gong, Mingkai Liu, Xiangping Zhao, Xian Wang, Haibo |
author_sort | Gong, Mingkai |
collection | PubMed |
description | BACKGROUND: Pyroptosis is a newly discovered form of cell programmed necrosis, but its role and mechanism in cancer cells remain unclear. The aim of this study is to systematically analyze the transcriptional sequencing data of breast cancer (BC) to find a pyroptosis-related prognostic marker to predict the survival of BC patients. METHODS: The original RNA sequencing (RNA-seq) expression data and corresponding clinical data of BC were downloaded from The Cancer Genome Atlas (TGCA) database, followed by differential analysis. The pyroptosis-related differentially expressed genes (DE-PRGs) were employed to perform a computational difference algorithm and Cox regression analysis. The least absolute shrinkage and selection operator (LASSO) was utilized to avoid overfitting. A total of 4 pyroptosis-related genes (PRGs) with potential prognostic value were identified, and a risk scoring formula was constructed based on these genes. According to the risk scores, the patients could be classified into high- and low-risk score groups. The potential molecular mechanisms and properties of PRGs were explored by computational biology and verified in Gene Expression Omnibus (GEO) datasets. In addition, the quantitative real time PCR (RT-qPCR) and Human Protein Atlas (HPA) were performed to validate the expression of the key genes. RESULTS: A PRGs signature, which was an independent prognostic factor, was constructed, and could divide patients into high- and low-risk groups. The results from the prognostic analysis indicated that the survival was significantly poorer in the high-risk group than in the low-risk group both in TCGA and in GEO, indicating that the signature is valuable for survival prediction and personalized immunotherapy of BC patients. CONCLUSIONS: The pyroptosis-related biomarkers were identified for BC prognosis. The findings of this study provide new insights into the development of the efficacy of personalized immunotherapy and accurate cancer treatment options. |
format | Online Article Text |
id | pubmed-9494840 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
publisher | BioMed Central |
record_format | MEDLINE/PubMed |
spelling | pubmed-94948402022-09-23 A pyroptosis-related gene signature predicting survival and tumor immune microenvironment in breast cancer and validation Gong, Mingkai Liu, Xiangping Zhao, Xian Wang, Haibo BMC Cancer Research BACKGROUND: Pyroptosis is a newly discovered form of cell programmed necrosis, but its role and mechanism in cancer cells remain unclear. The aim of this study is to systematically analyze the transcriptional sequencing data of breast cancer (BC) to find a pyroptosis-related prognostic marker to predict the survival of BC patients. METHODS: The original RNA sequencing (RNA-seq) expression data and corresponding clinical data of BC were downloaded from The Cancer Genome Atlas (TGCA) database, followed by differential analysis. The pyroptosis-related differentially expressed genes (DE-PRGs) were employed to perform a computational difference algorithm and Cox regression analysis. The least absolute shrinkage and selection operator (LASSO) was utilized to avoid overfitting. A total of 4 pyroptosis-related genes (PRGs) with potential prognostic value were identified, and a risk scoring formula was constructed based on these genes. According to the risk scores, the patients could be classified into high- and low-risk score groups. The potential molecular mechanisms and properties of PRGs were explored by computational biology and verified in Gene Expression Omnibus (GEO) datasets. In addition, the quantitative real time PCR (RT-qPCR) and Human Protein Atlas (HPA) were performed to validate the expression of the key genes. RESULTS: A PRGs signature, which was an independent prognostic factor, was constructed, and could divide patients into high- and low-risk groups. The results from the prognostic analysis indicated that the survival was significantly poorer in the high-risk group than in the low-risk group both in TCGA and in GEO, indicating that the signature is valuable for survival prediction and personalized immunotherapy of BC patients. CONCLUSIONS: The pyroptosis-related biomarkers were identified for BC prognosis. The findings of this study provide new insights into the development of the efficacy of personalized immunotherapy and accurate cancer treatment options. BioMed Central 2022-09-22 /pmc/articles/PMC9494840/ /pubmed/36138348 http://dx.doi.org/10.1186/s12885-022-09856-y Text en © The Author(s) 2022 https://creativecommons.org/licenses/by/4.0/ Open AccessThis article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/ (https://creativecommons.org/licenses/by/4.0/) . The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/ (https://creativecommons.org/publicdomain/zero/1.0/) ) applies to the data made available in this article, unless otherwise stated in a credit line to the data. |
spellingShingle | Research Gong, Mingkai Liu, Xiangping Zhao, Xian Wang, Haibo A pyroptosis-related gene signature predicting survival and tumor immune microenvironment in breast cancer and validation |
title | A pyroptosis-related gene signature predicting survival and tumor immune microenvironment in breast cancer and validation |
title_full | A pyroptosis-related gene signature predicting survival and tumor immune microenvironment in breast cancer and validation |
title_fullStr | A pyroptosis-related gene signature predicting survival and tumor immune microenvironment in breast cancer and validation |
title_full_unstemmed | A pyroptosis-related gene signature predicting survival and tumor immune microenvironment in breast cancer and validation |
title_short | A pyroptosis-related gene signature predicting survival and tumor immune microenvironment in breast cancer and validation |
title_sort | pyroptosis-related gene signature predicting survival and tumor immune microenvironment in breast cancer and validation |
topic | Research |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9494840/ https://www.ncbi.nlm.nih.gov/pubmed/36138348 http://dx.doi.org/10.1186/s12885-022-09856-y |
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