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Identification of a tumor immune-inflammation signature predicting prognosis and immune status in breast cancer

BACKGROUND: Breast cancer has become the malignancy with the highest mortality rate in female patients worldwide. The limited efficacy of immunotherapy as a breast cancer treatment has fueled the development of research on the tumor immune microenvironment. METHODS: In this study, data on breast can...

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Autores principales: Liu, Yajing, Ouyang, Wenhao, Huang, Hong, Tan, Yujie, Zhang, Zebang, Yu, Yunfang, Yao, Herui
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Frontiers Media S.A. 2023
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9881411/
https://www.ncbi.nlm.nih.gov/pubmed/36713514
http://dx.doi.org/10.3389/fonc.2022.960579
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author Liu, Yajing
Ouyang, Wenhao
Huang, Hong
Tan, Yujie
Zhang, Zebang
Yu, Yunfang
Yao, Herui
author_facet Liu, Yajing
Ouyang, Wenhao
Huang, Hong
Tan, Yujie
Zhang, Zebang
Yu, Yunfang
Yao, Herui
author_sort Liu, Yajing
collection PubMed
description BACKGROUND: Breast cancer has become the malignancy with the highest mortality rate in female patients worldwide. The limited efficacy of immunotherapy as a breast cancer treatment has fueled the development of research on the tumor immune microenvironment. METHODS: In this study, data on breast cancer patients were collected from The Cancer Genome Atlas Breast Invasive Carcinoma (TCGA-BRCA) and Molecular Taxonomy of Breast Cancer International Consortium (METABRIC) cohorts. Differential gene expression analysis, univariate Cox regression analysis, and least absolute shrinkage and selection operator (LASSO) Cox regression analysis were performed to select overall survival (OS)-related, tumor tissue highly expressed, and immune- and inflammation-related genes. A tumor immune-inflammation signature (TIIS) consisting of 18 genes was finally screened out in the LASSO Cox regression model. Model performance was assessed by time-dependent receiver operating characteristic (ROC) curves. In addition, the CIBERSORT algorithm and abundant expression of immune checkpoints were utilized to clarify the correlation between the risk signature and immune landscape in breast cancer. Furthermore, the association of IL27 with the immune signature was analyzed in pan-cancer and the effect of IL27 on the migration of breast cancer cells was investigated since the regression coefficient of IL27 was the highest. RESULTS: A TIIS based on 18 genes was constructed via LASSO Cox regression analysis. In the TCGA-BRCA training cohort, 10-year AUC reached 0.89, and prediction performance of this signature was also validated in the METABRIC set. The high-risk group was significantly correlated with less infiltration of tumor-killing immune cells and the lower expression level of the immune checkpoint. Furthermore, we recommended some small-molecule drugs as novel targeted drugs for new breast cancer types. Finally, the relationship between IL27, a significant prognostic immune and inflammation cytokine, and immune status was analyzed in pan-cancer. Expression of IL27 was significantly correlated with immune regulatory gene expression and immune cell infiltration in pan-cancer. Furthermore, IL27 treatment improved breast cancer cell migration. CONCLUSION: The TIIS represents a promising prognostic tool for estimating OS in patients with breast cancer and is correlated with immune status.
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spelling pubmed-98814112023-01-28 Identification of a tumor immune-inflammation signature predicting prognosis and immune status in breast cancer Liu, Yajing Ouyang, Wenhao Huang, Hong Tan, Yujie Zhang, Zebang Yu, Yunfang Yao, Herui Front Oncol Oncology BACKGROUND: Breast cancer has become the malignancy with the highest mortality rate in female patients worldwide. The limited efficacy of immunotherapy as a breast cancer treatment has fueled the development of research on the tumor immune microenvironment. METHODS: In this study, data on breast cancer patients were collected from The Cancer Genome Atlas Breast Invasive Carcinoma (TCGA-BRCA) and Molecular Taxonomy of Breast Cancer International Consortium (METABRIC) cohorts. Differential gene expression analysis, univariate Cox regression analysis, and least absolute shrinkage and selection operator (LASSO) Cox regression analysis were performed to select overall survival (OS)-related, tumor tissue highly expressed, and immune- and inflammation-related genes. A tumor immune-inflammation signature (TIIS) consisting of 18 genes was finally screened out in the LASSO Cox regression model. Model performance was assessed by time-dependent receiver operating characteristic (ROC) curves. In addition, the CIBERSORT algorithm and abundant expression of immune checkpoints were utilized to clarify the correlation between the risk signature and immune landscape in breast cancer. Furthermore, the association of IL27 with the immune signature was analyzed in pan-cancer and the effect of IL27 on the migration of breast cancer cells was investigated since the regression coefficient of IL27 was the highest. RESULTS: A TIIS based on 18 genes was constructed via LASSO Cox regression analysis. In the TCGA-BRCA training cohort, 10-year AUC reached 0.89, and prediction performance of this signature was also validated in the METABRIC set. The high-risk group was significantly correlated with less infiltration of tumor-killing immune cells and the lower expression level of the immune checkpoint. Furthermore, we recommended some small-molecule drugs as novel targeted drugs for new breast cancer types. Finally, the relationship between IL27, a significant prognostic immune and inflammation cytokine, and immune status was analyzed in pan-cancer. Expression of IL27 was significantly correlated with immune regulatory gene expression and immune cell infiltration in pan-cancer. Furthermore, IL27 treatment improved breast cancer cell migration. CONCLUSION: The TIIS represents a promising prognostic tool for estimating OS in patients with breast cancer and is correlated with immune status. Frontiers Media S.A. 2023-01-12 /pmc/articles/PMC9881411/ /pubmed/36713514 http://dx.doi.org/10.3389/fonc.2022.960579 Text en Copyright © 2023 Liu, Ouyang, Huang, Tan, Zhang, Yu and Yao 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 Oncology
Liu, Yajing
Ouyang, Wenhao
Huang, Hong
Tan, Yujie
Zhang, Zebang
Yu, Yunfang
Yao, Herui
Identification of a tumor immune-inflammation signature predicting prognosis and immune status in breast cancer
title Identification of a tumor immune-inflammation signature predicting prognosis and immune status in breast cancer
title_full Identification of a tumor immune-inflammation signature predicting prognosis and immune status in breast cancer
title_fullStr Identification of a tumor immune-inflammation signature predicting prognosis and immune status in breast cancer
title_full_unstemmed Identification of a tumor immune-inflammation signature predicting prognosis and immune status in breast cancer
title_short Identification of a tumor immune-inflammation signature predicting prognosis and immune status in breast cancer
title_sort identification of a tumor immune-inflammation signature predicting prognosis and immune status in breast cancer
topic Oncology
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9881411/
https://www.ncbi.nlm.nih.gov/pubmed/36713514
http://dx.doi.org/10.3389/fonc.2022.960579
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