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Six Immune Associated Genes Construct Prognostic Model Evaluate Low-Grade Glioma

BACKGROUND: The immunotherapy of Glioma has always been a research hotspot. Although tumor associated microglia/macrophages (TAMs) proves to be important in glioma progression and drug resistance, our knowledge about how TAMs influence glioma remains unclear. The relationship between glioma and TAMs...

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Autores principales: Tan, Yin Qiu, Li, Yun Tao, Yan, Teng Feng, Xu, Yang, Liu, Bao Hui, Yang, Ji An, Yang, Xue, Chen, Qian Xue, Zhang, Hong Bo
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Frontiers Media S.A. 2020
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7779629/
https://www.ncbi.nlm.nih.gov/pubmed/33408717
http://dx.doi.org/10.3389/fimmu.2020.606164
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author Tan, Yin Qiu
Li, Yun Tao
Yan, Teng Feng
Xu, Yang
Liu, Bao Hui
Yang, Ji An
Yang, Xue
Chen, Qian Xue
Zhang, Hong Bo
author_facet Tan, Yin Qiu
Li, Yun Tao
Yan, Teng Feng
Xu, Yang
Liu, Bao Hui
Yang, Ji An
Yang, Xue
Chen, Qian Xue
Zhang, Hong Bo
author_sort Tan, Yin Qiu
collection PubMed
description BACKGROUND: The immunotherapy of Glioma has always been a research hotspot. Although tumor associated microglia/macrophages (TAMs) proves to be important in glioma progression and drug resistance, our knowledge about how TAMs influence glioma remains unclear. The relationship between glioma and TAMs still needs further study. METHODS: We collected the data of TAMs in glioma from NCBI Gene Expression Omnibus (GEO) that included 20 glioma samples and 15 control samples from four datasets. Six genes were screened from the Differential Expression Gene through Gene ontology (GO) analysis, Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analysis, protein–protein interaction (PPI) network and single-cell sequencing analysis. A risk score was then constructed based on the six genes and patients’ overall survival rates of 669 patients from The Cancer Genome Atlas (TCGA). The efficacy of the risk score in prognosis and prediction was verified in Chinese Glioma Genome Atlas (CGGA). RESULTS: Six genes, including CD163, FPR3, LPAR5, P2ry12, PLAUR, SIGLEC1, that participate in signal transduction and plasma membrane were selected. Half of them, like CD163, FPR3, SIGLEC1, were mainly expression in M2 macrophages. FPR3 and SIGLEC1 were high expression genes in glioma associated with grades and IDH status. The overall survival rates of the high risk score group was significantly lower than that of the low risk score group, especially in LGG. CONCLUSION: Joint usage of the 6 candidate genes may be an effective method to diagnose and evaluate the prognosis of glioma, especially in Low-grade glioma (LGG).
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spelling pubmed-77796292021-01-05 Six Immune Associated Genes Construct Prognostic Model Evaluate Low-Grade Glioma Tan, Yin Qiu Li, Yun Tao Yan, Teng Feng Xu, Yang Liu, Bao Hui Yang, Ji An Yang, Xue Chen, Qian Xue Zhang, Hong Bo Front Immunol Immunology BACKGROUND: The immunotherapy of Glioma has always been a research hotspot. Although tumor associated microglia/macrophages (TAMs) proves to be important in glioma progression and drug resistance, our knowledge about how TAMs influence glioma remains unclear. The relationship between glioma and TAMs still needs further study. METHODS: We collected the data of TAMs in glioma from NCBI Gene Expression Omnibus (GEO) that included 20 glioma samples and 15 control samples from four datasets. Six genes were screened from the Differential Expression Gene through Gene ontology (GO) analysis, Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analysis, protein–protein interaction (PPI) network and single-cell sequencing analysis. A risk score was then constructed based on the six genes and patients’ overall survival rates of 669 patients from The Cancer Genome Atlas (TCGA). The efficacy of the risk score in prognosis and prediction was verified in Chinese Glioma Genome Atlas (CGGA). RESULTS: Six genes, including CD163, FPR3, LPAR5, P2ry12, PLAUR, SIGLEC1, that participate in signal transduction and plasma membrane were selected. Half of them, like CD163, FPR3, SIGLEC1, were mainly expression in M2 macrophages. FPR3 and SIGLEC1 were high expression genes in glioma associated with grades and IDH status. The overall survival rates of the high risk score group was significantly lower than that of the low risk score group, especially in LGG. CONCLUSION: Joint usage of the 6 candidate genes may be an effective method to diagnose and evaluate the prognosis of glioma, especially in Low-grade glioma (LGG). Frontiers Media S.A. 2020-12-21 /pmc/articles/PMC7779629/ /pubmed/33408717 http://dx.doi.org/10.3389/fimmu.2020.606164 Text en Copyright © 2020 Tan, Li, Yan, Xu, Liu, Yang, Yang, Chen and Zhang http://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
Tan, Yin Qiu
Li, Yun Tao
Yan, Teng Feng
Xu, Yang
Liu, Bao Hui
Yang, Ji An
Yang, Xue
Chen, Qian Xue
Zhang, Hong Bo
Six Immune Associated Genes Construct Prognostic Model Evaluate Low-Grade Glioma
title Six Immune Associated Genes Construct Prognostic Model Evaluate Low-Grade Glioma
title_full Six Immune Associated Genes Construct Prognostic Model Evaluate Low-Grade Glioma
title_fullStr Six Immune Associated Genes Construct Prognostic Model Evaluate Low-Grade Glioma
title_full_unstemmed Six Immune Associated Genes Construct Prognostic Model Evaluate Low-Grade Glioma
title_short Six Immune Associated Genes Construct Prognostic Model Evaluate Low-Grade Glioma
title_sort six immune associated genes construct prognostic model evaluate low-grade glioma
topic Immunology
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7779629/
https://www.ncbi.nlm.nih.gov/pubmed/33408717
http://dx.doi.org/10.3389/fimmu.2020.606164
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