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Identification of a combined apoptosis and hypoxia gene signature for predicting prognosis and immune infiltration in breast cancer
BACKGROUND: Breast cancer (BC) is the most common malignant tumor worldwide. Apoptosis and hypoxia are involved in the progression of BC, but reliable biomarkers for these have not been developed. We hope to explore a gene signature that combined apoptosis and hypoxia‐related genes (AHGs) to predict...
Autores principales: | , , , , , , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
John Wiley and Sons Inc.
2022
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9582692/ https://www.ncbi.nlm.nih.gov/pubmed/35441810 http://dx.doi.org/10.1002/cam4.4755 |
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author | Ren, Xueting Cui, Hanxiao Wu, Jianhua Zhou, Ruina Wang, Nan Liu, Dandan Xie, Xin Zhang, Hao Liu, Di Ma, Xiaobin Dang, Chengxue Kang, Huafeng Lin, Shuai |
author_facet | Ren, Xueting Cui, Hanxiao Wu, Jianhua Zhou, Ruina Wang, Nan Liu, Dandan Xie, Xin Zhang, Hao Liu, Di Ma, Xiaobin Dang, Chengxue Kang, Huafeng Lin, Shuai |
author_sort | Ren, Xueting |
collection | PubMed |
description | BACKGROUND: Breast cancer (BC) is the most common malignant tumor worldwide. Apoptosis and hypoxia are involved in the progression of BC, but reliable biomarkers for these have not been developed. We hope to explore a gene signature that combined apoptosis and hypoxia‐related genes (AHGs) to predict BC prognosis and immune infiltration. METHODS: We collected the mRNA expression profiles and clinical data information of BC patients from The Cancer Genome Atlas database. The gene signature based on AHGs was constructed using the univariate Cox regression, least absolute shrinkage and selection operator, and multivariate Cox regression analysis. The associations between risk scores, immune infiltration, and immune checkpoint gene expression were studied using single‐sample gene set enrichment analysis. Besides, gene signature and independent clinicopathological characteristics were combined to establish a nomogram. Finally, Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) were performed on the potential functions of AHGs. RESULTS: We identified a 16‐AHG signature (AGPAT1, BTBD6, EIF4EBP1, ERRFI1, FAM114A1, GRIP1, IRF2, JAK1, MAP2K6, MCTS1, NFKBIA, NFKBIZ, NUP43, PGK1, RCL1, and SGCE) that could independently predict BC prognosis. The median score of the risk model divided the patients into two subgroups. By contrast, patients in the high‐risk group had poorer prognosis, less abundance of immune cell infiltration, and expression of immune checkpoint genes. The gene signature and nomogram had good predictive effects on the overall survival of BC patients. GO and KEGG analyses revealed that the differential expression of AHGs may be closely related to tumor immunity. CONCLUSION: We established and verified a 16‐AHG BC signature which may help predict prognosis, assess potential immunotherapy benefits, and provide inspiration for future research on the functions and mechanisms of AHGs in BC. |
format | Online Article Text |
id | pubmed-9582692 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
publisher | John Wiley and Sons Inc. |
record_format | MEDLINE/PubMed |
spelling | pubmed-95826922022-10-21 Identification of a combined apoptosis and hypoxia gene signature for predicting prognosis and immune infiltration in breast cancer Ren, Xueting Cui, Hanxiao Wu, Jianhua Zhou, Ruina Wang, Nan Liu, Dandan Xie, Xin Zhang, Hao Liu, Di Ma, Xiaobin Dang, Chengxue Kang, Huafeng Lin, Shuai Cancer Med Research Articles BACKGROUND: Breast cancer (BC) is the most common malignant tumor worldwide. Apoptosis and hypoxia are involved in the progression of BC, but reliable biomarkers for these have not been developed. We hope to explore a gene signature that combined apoptosis and hypoxia‐related genes (AHGs) to predict BC prognosis and immune infiltration. METHODS: We collected the mRNA expression profiles and clinical data information of BC patients from The Cancer Genome Atlas database. The gene signature based on AHGs was constructed using the univariate Cox regression, least absolute shrinkage and selection operator, and multivariate Cox regression analysis. The associations between risk scores, immune infiltration, and immune checkpoint gene expression were studied using single‐sample gene set enrichment analysis. Besides, gene signature and independent clinicopathological characteristics were combined to establish a nomogram. Finally, Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) were performed on the potential functions of AHGs. RESULTS: We identified a 16‐AHG signature (AGPAT1, BTBD6, EIF4EBP1, ERRFI1, FAM114A1, GRIP1, IRF2, JAK1, MAP2K6, MCTS1, NFKBIA, NFKBIZ, NUP43, PGK1, RCL1, and SGCE) that could independently predict BC prognosis. The median score of the risk model divided the patients into two subgroups. By contrast, patients in the high‐risk group had poorer prognosis, less abundance of immune cell infiltration, and expression of immune checkpoint genes. The gene signature and nomogram had good predictive effects on the overall survival of BC patients. GO and KEGG analyses revealed that the differential expression of AHGs may be closely related to tumor immunity. CONCLUSION: We established and verified a 16‐AHG BC signature which may help predict prognosis, assess potential immunotherapy benefits, and provide inspiration for future research on the functions and mechanisms of AHGs in BC. John Wiley and Sons Inc. 2022-04-20 /pmc/articles/PMC9582692/ /pubmed/35441810 http://dx.doi.org/10.1002/cam4.4755 Text en © 2022 The Authors. Cancer Medicine published by John Wiley & Sons Ltd. https://creativecommons.org/licenses/by/4.0/This is an open access article under the terms of the http://creativecommons.org/licenses/by/4.0/ (https://creativecommons.org/licenses/by/4.0/) License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited. |
spellingShingle | Research Articles Ren, Xueting Cui, Hanxiao Wu, Jianhua Zhou, Ruina Wang, Nan Liu, Dandan Xie, Xin Zhang, Hao Liu, Di Ma, Xiaobin Dang, Chengxue Kang, Huafeng Lin, Shuai Identification of a combined apoptosis and hypoxia gene signature for predicting prognosis and immune infiltration in breast cancer |
title | Identification of a combined apoptosis and hypoxia gene signature for predicting prognosis and immune infiltration in breast cancer |
title_full | Identification of a combined apoptosis and hypoxia gene signature for predicting prognosis and immune infiltration in breast cancer |
title_fullStr | Identification of a combined apoptosis and hypoxia gene signature for predicting prognosis and immune infiltration in breast cancer |
title_full_unstemmed | Identification of a combined apoptosis and hypoxia gene signature for predicting prognosis and immune infiltration in breast cancer |
title_short | Identification of a combined apoptosis and hypoxia gene signature for predicting prognosis and immune infiltration in breast cancer |
title_sort | identification of a combined apoptosis and hypoxia gene signature for predicting prognosis and immune infiltration in breast cancer |
topic | Research Articles |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9582692/ https://www.ncbi.nlm.nih.gov/pubmed/35441810 http://dx.doi.org/10.1002/cam4.4755 |
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