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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...

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Autores principales: Zhang, Nan, Dai, Ziyu, Wu, Wantao, Wang, Zeyu, Cao, Hui, Zhang, Yakun, Wang, Zhanchao, Zhang, Hao, Cheng, Quan
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Frontiers Media S.A. 2021
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.
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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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