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Construction of prediction model of inflammation related genes in idiopathic pulmonary fibrosis and its correlation with immune microenvironment

BACKGROUND: The role of inflammation in the formation of idiopathic pulmonary fibrosis (IPF) has gained a lot of attention recently. However, the involvement of genes related to inflammation and immune exchange environment status in the prognosis of IPF remains to be further clarified. The objective...

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Autores principales: Yin, Ying-Qiu, Peng, Feng, Situ, Hui-Jing, Xie, Jun-Ling, Tan, Liming, Wei, Jie, Jiang, Fang-fang, Zhang, Shan-Qiang, Liu, Jun
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Frontiers Media S.A. 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9806212/
https://www.ncbi.nlm.nih.gov/pubmed/36601116
http://dx.doi.org/10.3389/fimmu.2022.1010345
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author Yin, Ying-Qiu
Peng, Feng
Situ, Hui-Jing
Xie, Jun-Ling
Tan, Liming
Wei, Jie
Jiang, Fang-fang
Zhang, Shan-Qiang
Liu, Jun
author_facet Yin, Ying-Qiu
Peng, Feng
Situ, Hui-Jing
Xie, Jun-Ling
Tan, Liming
Wei, Jie
Jiang, Fang-fang
Zhang, Shan-Qiang
Liu, Jun
author_sort Yin, Ying-Qiu
collection PubMed
description BACKGROUND: The role of inflammation in the formation of idiopathic pulmonary fibrosis (IPF) has gained a lot of attention recently. However, the involvement of genes related to inflammation and immune exchange environment status in the prognosis of IPF remains to be further clarified. The objective of this research is to establish a new model for the prediction of the overall survival (OS) rate of inflammation-related IPF. METHODS: Gene Expression Omnibus (GEO) was employed to obtain the three expression microarrays of IPF, including two from alveolar lavage fluid cells and one from peripheral blood mononuclear cells. To construct the risk assessment model of inflammation-linked genes, least absolute shrinkage and selection operator (lasso), univariate cox and multivariate stepwise regression, and random forest method were used. The proportion of immune cell infiltration was evaluated by single sample Gene Set Enrichment Analysis (ssGSEA) algorithm. RESULTS: The value of genes linked with inflammation in the prognosis of IPF was analyzed, and a four-genes risk model was constructed, including tpbg, Myc, ffar2, and CCL2. It was highlighted by Kaplan Meier (K-M) survival analysis that patients with high-risk scores had worse overall survival time in all training and validation sets, and univariate and multivariate analysis highlighted that it has the potential to act as an independent risk indicator for poor prognosis. ROC analysis showed that the prediction efficiency of 1-, 3-, and 5-year OS time in the training set reached 0.784, 0.835, and 0.921, respectively. Immune infiltration analysis showed that Myeloid-Derived Suppressor Cells (MDSC), macrophages, regulatory T cells, cd4+ t cells, neutrophils, and dendritic cells were more infiltrated in the high-risk group than in the low-risk group. CONCLUSION: Inflammation-related genes can be well used to evaluate the IPF prognosis and impart a new idea for the treatment and follow-up management of IPF patients.
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spelling pubmed-98062122023-01-03 Construction of prediction model of inflammation related genes in idiopathic pulmonary fibrosis and its correlation with immune microenvironment Yin, Ying-Qiu Peng, Feng Situ, Hui-Jing Xie, Jun-Ling Tan, Liming Wei, Jie Jiang, Fang-fang Zhang, Shan-Qiang Liu, Jun Front Immunol Immunology BACKGROUND: The role of inflammation in the formation of idiopathic pulmonary fibrosis (IPF) has gained a lot of attention recently. However, the involvement of genes related to inflammation and immune exchange environment status in the prognosis of IPF remains to be further clarified. The objective of this research is to establish a new model for the prediction of the overall survival (OS) rate of inflammation-related IPF. METHODS: Gene Expression Omnibus (GEO) was employed to obtain the three expression microarrays of IPF, including two from alveolar lavage fluid cells and one from peripheral blood mononuclear cells. To construct the risk assessment model of inflammation-linked genes, least absolute shrinkage and selection operator (lasso), univariate cox and multivariate stepwise regression, and random forest method were used. The proportion of immune cell infiltration was evaluated by single sample Gene Set Enrichment Analysis (ssGSEA) algorithm. RESULTS: The value of genes linked with inflammation in the prognosis of IPF was analyzed, and a four-genes risk model was constructed, including tpbg, Myc, ffar2, and CCL2. It was highlighted by Kaplan Meier (K-M) survival analysis that patients with high-risk scores had worse overall survival time in all training and validation sets, and univariate and multivariate analysis highlighted that it has the potential to act as an independent risk indicator for poor prognosis. ROC analysis showed that the prediction efficiency of 1-, 3-, and 5-year OS time in the training set reached 0.784, 0.835, and 0.921, respectively. Immune infiltration analysis showed that Myeloid-Derived Suppressor Cells (MDSC), macrophages, regulatory T cells, cd4+ t cells, neutrophils, and dendritic cells were more infiltrated in the high-risk group than in the low-risk group. CONCLUSION: Inflammation-related genes can be well used to evaluate the IPF prognosis and impart a new idea for the treatment and follow-up management of IPF patients. Frontiers Media S.A. 2022-12-19 /pmc/articles/PMC9806212/ /pubmed/36601116 http://dx.doi.org/10.3389/fimmu.2022.1010345 Text en Copyright © 2022 Yin, Peng, Situ, Xie, Tan, Wei, Jiang, Zhang and Liu 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 Immunology
Yin, Ying-Qiu
Peng, Feng
Situ, Hui-Jing
Xie, Jun-Ling
Tan, Liming
Wei, Jie
Jiang, Fang-fang
Zhang, Shan-Qiang
Liu, Jun
Construction of prediction model of inflammation related genes in idiopathic pulmonary fibrosis and its correlation with immune microenvironment
title Construction of prediction model of inflammation related genes in idiopathic pulmonary fibrosis and its correlation with immune microenvironment
title_full Construction of prediction model of inflammation related genes in idiopathic pulmonary fibrosis and its correlation with immune microenvironment
title_fullStr Construction of prediction model of inflammation related genes in idiopathic pulmonary fibrosis and its correlation with immune microenvironment
title_full_unstemmed Construction of prediction model of inflammation related genes in idiopathic pulmonary fibrosis and its correlation with immune microenvironment
title_short Construction of prediction model of inflammation related genes in idiopathic pulmonary fibrosis and its correlation with immune microenvironment
title_sort construction of prediction model of inflammation related genes in idiopathic pulmonary fibrosis and its correlation with immune microenvironment
topic Immunology
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9806212/
https://www.ncbi.nlm.nih.gov/pubmed/36601116
http://dx.doi.org/10.3389/fimmu.2022.1010345
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