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A Pyroptosis-Related Gene Signature Predicts Prognosis and Immune Microenvironment for Breast Cancer Based on Computational Biology Techniques

Breast cancer (BC) is a malignant tumor with high morbidity and mortality, which seriously threatens women’s health worldwide. Pyroptosis is closely correlated with immune landscape and the tumorigenesis and development of various cancers. However, studies about pyroptosis and immune microenvironmen...

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Autores principales: Wang, Zitao, Bao, Anyu, Liu, Shiyi, Dai, Fangfang, Gong, Yiping, Cheng, Yanxiang
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Frontiers Media S.A. 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9021921/
https://www.ncbi.nlm.nih.gov/pubmed/35464869
http://dx.doi.org/10.3389/fgene.2022.801056
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author Wang, Zitao
Bao, Anyu
Liu, Shiyi
Dai, Fangfang
Gong, Yiping
Cheng, Yanxiang
author_facet Wang, Zitao
Bao, Anyu
Liu, Shiyi
Dai, Fangfang
Gong, Yiping
Cheng, Yanxiang
author_sort Wang, Zitao
collection PubMed
description Breast cancer (BC) is a malignant tumor with high morbidity and mortality, which seriously threatens women’s health worldwide. Pyroptosis is closely correlated with immune landscape and the tumorigenesis and development of various cancers. However, studies about pyroptosis and immune microenvironment in BC are limited. Therefore, our study aimed to investigate the potential prognostic value of pyroptosis-related genes (PRGs) and their relationship to immune microenvironment in BC. First, we identified 38 differentially expressed PRGs between BC and normal tissues. Further on, the least absolute shrinkage and selection operator (LASSO) Cox regression and computational biology techniques were applied to construct a four-gene signature based on PRGs and patients in The Cancer Genome Atlas (TCGA) cohort were classified into high- and low-risk groups. Patients in the high-risk group showed significantly lower survival possibilities compared with the low-risk group, which was also verified in an external cohort. Furthermore, the risk model was characterized as an independent factor for predicting the overall survival (OS) of BC patients. What is more important, functional enrichment analyses demonstrated the robust correlation between risk score and immune infiltration, thereby we summarized genetic mutation variation of PRGs, evaluated the relationship between PRGs, different risk group and immune infiltration, tumor mutation burden (TMB), microsatellite instability (MSI), and immune checkpoint blockers (ICB), which indicated that the low-risk group was enriched in higher TMB, more abundant immune cells, and subsequently had a brighter prognosis. Except for that, the lower expression of PRGs such as GZMB, IL18, IRF1, and GZMA represented better survival, which verified the association between pyroptosis and immune landscape. In conclusion, we performed a comprehensive bioinformatics analysis and established a four-PRG signature consisting of GZMB, IL18, IRF1, and GZMA, which could robustly predict the prognosis of BC patients.
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spelling pubmed-90219212022-04-22 A Pyroptosis-Related Gene Signature Predicts Prognosis and Immune Microenvironment for Breast Cancer Based on Computational Biology Techniques Wang, Zitao Bao, Anyu Liu, Shiyi Dai, Fangfang Gong, Yiping Cheng, Yanxiang Front Genet Genetics Breast cancer (BC) is a malignant tumor with high morbidity and mortality, which seriously threatens women’s health worldwide. Pyroptosis is closely correlated with immune landscape and the tumorigenesis and development of various cancers. However, studies about pyroptosis and immune microenvironment in BC are limited. Therefore, our study aimed to investigate the potential prognostic value of pyroptosis-related genes (PRGs) and their relationship to immune microenvironment in BC. First, we identified 38 differentially expressed PRGs between BC and normal tissues. Further on, the least absolute shrinkage and selection operator (LASSO) Cox regression and computational biology techniques were applied to construct a four-gene signature based on PRGs and patients in The Cancer Genome Atlas (TCGA) cohort were classified into high- and low-risk groups. Patients in the high-risk group showed significantly lower survival possibilities compared with the low-risk group, which was also verified in an external cohort. Furthermore, the risk model was characterized as an independent factor for predicting the overall survival (OS) of BC patients. What is more important, functional enrichment analyses demonstrated the robust correlation between risk score and immune infiltration, thereby we summarized genetic mutation variation of PRGs, evaluated the relationship between PRGs, different risk group and immune infiltration, tumor mutation burden (TMB), microsatellite instability (MSI), and immune checkpoint blockers (ICB), which indicated that the low-risk group was enriched in higher TMB, more abundant immune cells, and subsequently had a brighter prognosis. Except for that, the lower expression of PRGs such as GZMB, IL18, IRF1, and GZMA represented better survival, which verified the association between pyroptosis and immune landscape. In conclusion, we performed a comprehensive bioinformatics analysis and established a four-PRG signature consisting of GZMB, IL18, IRF1, and GZMA, which could robustly predict the prognosis of BC patients. Frontiers Media S.A. 2022-04-07 /pmc/articles/PMC9021921/ /pubmed/35464869 http://dx.doi.org/10.3389/fgene.2022.801056 Text en Copyright © 2022 Wang, Bao, Liu, Dai, Gong and Cheng. 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 Genetics
Wang, Zitao
Bao, Anyu
Liu, Shiyi
Dai, Fangfang
Gong, Yiping
Cheng, Yanxiang
A Pyroptosis-Related Gene Signature Predicts Prognosis and Immune Microenvironment for Breast Cancer Based on Computational Biology Techniques
title A Pyroptosis-Related Gene Signature Predicts Prognosis and Immune Microenvironment for Breast Cancer Based on Computational Biology Techniques
title_full A Pyroptosis-Related Gene Signature Predicts Prognosis and Immune Microenvironment for Breast Cancer Based on Computational Biology Techniques
title_fullStr A Pyroptosis-Related Gene Signature Predicts Prognosis and Immune Microenvironment for Breast Cancer Based on Computational Biology Techniques
title_full_unstemmed A Pyroptosis-Related Gene Signature Predicts Prognosis and Immune Microenvironment for Breast Cancer Based on Computational Biology Techniques
title_short A Pyroptosis-Related Gene Signature Predicts Prognosis and Immune Microenvironment for Breast Cancer Based on Computational Biology Techniques
title_sort pyroptosis-related gene signature predicts prognosis and immune microenvironment for breast cancer based on computational biology techniques
topic Genetics
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9021921/
https://www.ncbi.nlm.nih.gov/pubmed/35464869
http://dx.doi.org/10.3389/fgene.2022.801056
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