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The Predictive Value of Monocytes in Immune Microenvironment and Prognosis of Glioma Patients Based on Machine Learning
Gliomas are primary malignant brain tumors. Monocytes have been proved to actively participate in tumor growth. Weighted gene co-expression network analysis was used to identify meaningful monocyte-related genes for clustering. Neural network and SVM were applied for validating clustering results. S...
Autores principales: | , , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Frontiers Media S.A.
2021
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8095378/ https://www.ncbi.nlm.nih.gov/pubmed/33959130 http://dx.doi.org/10.3389/fimmu.2021.656541 |
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author | Zhang, Nan Dai, Ziyu Wu, Wantao Wang, Zeyu Cao, Hui Zhang, Yakun Wang, Zhanchao Zhang, Hao Cheng, Quan |
author_facet | Zhang, Nan Dai, Ziyu Wu, Wantao Wang, Zeyu Cao, Hui Zhang, Yakun Wang, Zhanchao Zhang, Hao Cheng, Quan |
author_sort | Zhang, Nan |
collection | PubMed |
description | Gliomas are primary malignant brain tumors. Monocytes have been proved to actively participate in tumor growth. Weighted gene co-expression network analysis was used to identify meaningful monocyte-related genes for clustering. Neural network and SVM were applied for validating clustering results. Somatic mutation and copy number variation were used for defining the features of identified clusters. Differentially expressed genes (DEGs) between the stratified groups after performing elastic regression and principal component analyses were used for the construction of risk scores. Monocytes were associated with glioma patients’ survival and exhibited high predictive value. The prognostic value of risk score in glioma was validated by the abundant expression of immune checkpoint and metabolic profile. Additionally, high risk score was positively associated with the expression of immunogenic and antigen presenting factors, which indicated high immune infiltration. A prognostic model based on risk score demonstrated high accuracy rate of receiver operating characteristic curves. Compared with previous studies, our research dissected functional roles of monocytes from large-scale analysis. Findings of our analyses strongly support an immune modulatory and prognostic role of monocytes in glioma progression. Notably, monocyte could be an effective predictor for therapy responses of glioma patients. |
format | Online Article Text |
id | pubmed-8095378 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2021 |
publisher | Frontiers Media S.A. |
record_format | MEDLINE/PubMed |
spelling | pubmed-80953782021-05-05 The Predictive Value of Monocytes in Immune Microenvironment and Prognosis of Glioma Patients Based on Machine Learning Zhang, Nan Dai, Ziyu Wu, Wantao Wang, Zeyu Cao, Hui Zhang, Yakun Wang, Zhanchao Zhang, Hao Cheng, Quan Front Immunol Immunology Gliomas are primary malignant brain tumors. Monocytes have been proved to actively participate in tumor growth. Weighted gene co-expression network analysis was used to identify meaningful monocyte-related genes for clustering. Neural network and SVM were applied for validating clustering results. Somatic mutation and copy number variation were used for defining the features of identified clusters. Differentially expressed genes (DEGs) between the stratified groups after performing elastic regression and principal component analyses were used for the construction of risk scores. Monocytes were associated with glioma patients’ survival and exhibited high predictive value. The prognostic value of risk score in glioma was validated by the abundant expression of immune checkpoint and metabolic profile. Additionally, high risk score was positively associated with the expression of immunogenic and antigen presenting factors, which indicated high immune infiltration. A prognostic model based on risk score demonstrated high accuracy rate of receiver operating characteristic curves. Compared with previous studies, our research dissected functional roles of monocytes from large-scale analysis. Findings of our analyses strongly support an immune modulatory and prognostic role of monocytes in glioma progression. Notably, monocyte could be an effective predictor for therapy responses of glioma patients. Frontiers Media S.A. 2021-04-16 /pmc/articles/PMC8095378/ /pubmed/33959130 http://dx.doi.org/10.3389/fimmu.2021.656541 Text en Copyright © 2021 Zhang, Dai, Wu, Wang, Cao, Zhang, Wang, Zhang 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 | Immunology Zhang, Nan Dai, Ziyu Wu, Wantao Wang, Zeyu Cao, Hui Zhang, Yakun Wang, Zhanchao Zhang, Hao Cheng, Quan The Predictive Value of Monocytes in Immune Microenvironment and Prognosis of Glioma Patients Based on Machine Learning |
title | The Predictive Value of Monocytes in Immune Microenvironment and Prognosis of Glioma Patients Based on Machine Learning |
title_full | The Predictive Value of Monocytes in Immune Microenvironment and Prognosis of Glioma Patients Based on Machine Learning |
title_fullStr | The Predictive Value of Monocytes in Immune Microenvironment and Prognosis of Glioma Patients Based on Machine Learning |
title_full_unstemmed | The Predictive Value of Monocytes in Immune Microenvironment and Prognosis of Glioma Patients Based on Machine Learning |
title_short | The Predictive Value of Monocytes in Immune Microenvironment and Prognosis of Glioma Patients Based on Machine Learning |
title_sort | predictive value of monocytes in immune microenvironment and prognosis of glioma patients based on machine learning |
topic | Immunology |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8095378/ https://www.ncbi.nlm.nih.gov/pubmed/33959130 http://dx.doi.org/10.3389/fimmu.2021.656541 |
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