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Metabolic Signature-Based Subtypes May Pave Novel Ways for Low-Grade Glioma Prognosis and Therapy

Metabolic signatures are frequently observed in cancer and are starting to be recognized as important regulators for tumor progression and therapy. Because metabolism genes are involved in tumor initiation and progression, little is known about the metabolic genomic profiles in low-grade glioma (LGG...

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Autores principales: Li, Ganglei, Wu, Zhanxiong, Gu, Jun, Zhu, Yu, Zhang, Tiesong, Wang, Feng, Huang, Kaiyuan, Gu, Chenjie, Xu, Kangli, Zhan, Renya, Shen, Jian
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Frontiers Media S.A. 2021
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8650219/
https://www.ncbi.nlm.nih.gov/pubmed/34888308
http://dx.doi.org/10.3389/fcell.2021.755776
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author Li, Ganglei
Wu, Zhanxiong
Gu, Jun
Zhu, Yu
Zhang, Tiesong
Wang, Feng
Huang, Kaiyuan
Gu, Chenjie
Xu, Kangli
Zhan, Renya
Shen, Jian
author_facet Li, Ganglei
Wu, Zhanxiong
Gu, Jun
Zhu, Yu
Zhang, Tiesong
Wang, Feng
Huang, Kaiyuan
Gu, Chenjie
Xu, Kangli
Zhan, Renya
Shen, Jian
author_sort Li, Ganglei
collection PubMed
description Metabolic signatures are frequently observed in cancer and are starting to be recognized as important regulators for tumor progression and therapy. Because metabolism genes are involved in tumor initiation and progression, little is known about the metabolic genomic profiles in low-grade glioma (LGG). Here, we applied bioinformatics analysis to determine the metabolic characteristics of patients with LGG from the Cancer Genome Atlas (TCGA) and the Chinese Glioma Genome Atlas (CGGA). We also performed the ConsensusClusterPlus, the CIBERSORT algorithm, the Estimate software, the R package “GSVA,” and TIDE to comprehensively describe and compare the characteristic difference between three metabolic subtypes. The R package WGCNA helped us to identify co-expression modules with associated metabolic subtypes. We found that LGG patients were classified into three subtypes based on 113 metabolic characteristics. MC1 patients had poor prognoses and MC3 patients obtained longer survival times. The different metabolic subtypes had different metabolic and immune characteristics, and may have different response patterns to immunotherapy. Based on the metabolic subtype, different patterns were exhibited that reflected the characteristics of each subtype. We also identified eight potential genetic markers associated with the characteristic index of metabolic subtypes. In conclusion, a comprehensive understanding of metabolism associated characteristics and classifications may improve clinical outcomes for LGG.
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spelling pubmed-86502192021-12-08 Metabolic Signature-Based Subtypes May Pave Novel Ways for Low-Grade Glioma Prognosis and Therapy Li, Ganglei Wu, Zhanxiong Gu, Jun Zhu, Yu Zhang, Tiesong Wang, Feng Huang, Kaiyuan Gu, Chenjie Xu, Kangli Zhan, Renya Shen, Jian Front Cell Dev Biol Cell and Developmental Biology Metabolic signatures are frequently observed in cancer and are starting to be recognized as important regulators for tumor progression and therapy. Because metabolism genes are involved in tumor initiation and progression, little is known about the metabolic genomic profiles in low-grade glioma (LGG). Here, we applied bioinformatics analysis to determine the metabolic characteristics of patients with LGG from the Cancer Genome Atlas (TCGA) and the Chinese Glioma Genome Atlas (CGGA). We also performed the ConsensusClusterPlus, the CIBERSORT algorithm, the Estimate software, the R package “GSVA,” and TIDE to comprehensively describe and compare the characteristic difference between three metabolic subtypes. The R package WGCNA helped us to identify co-expression modules with associated metabolic subtypes. We found that LGG patients were classified into three subtypes based on 113 metabolic characteristics. MC1 patients had poor prognoses and MC3 patients obtained longer survival times. The different metabolic subtypes had different metabolic and immune characteristics, and may have different response patterns to immunotherapy. Based on the metabolic subtype, different patterns were exhibited that reflected the characteristics of each subtype. We also identified eight potential genetic markers associated with the characteristic index of metabolic subtypes. In conclusion, a comprehensive understanding of metabolism associated characteristics and classifications may improve clinical outcomes for LGG. Frontiers Media S.A. 2021-11-23 /pmc/articles/PMC8650219/ /pubmed/34888308 http://dx.doi.org/10.3389/fcell.2021.755776 Text en Copyright © 2021 Li, Wu, Gu, Zhu, Zhang, Wang, Huang, Gu, Xu, Zhan and Shen. 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 Cell and Developmental Biology
Li, Ganglei
Wu, Zhanxiong
Gu, Jun
Zhu, Yu
Zhang, Tiesong
Wang, Feng
Huang, Kaiyuan
Gu, Chenjie
Xu, Kangli
Zhan, Renya
Shen, Jian
Metabolic Signature-Based Subtypes May Pave Novel Ways for Low-Grade Glioma Prognosis and Therapy
title Metabolic Signature-Based Subtypes May Pave Novel Ways for Low-Grade Glioma Prognosis and Therapy
title_full Metabolic Signature-Based Subtypes May Pave Novel Ways for Low-Grade Glioma Prognosis and Therapy
title_fullStr Metabolic Signature-Based Subtypes May Pave Novel Ways for Low-Grade Glioma Prognosis and Therapy
title_full_unstemmed Metabolic Signature-Based Subtypes May Pave Novel Ways for Low-Grade Glioma Prognosis and Therapy
title_short Metabolic Signature-Based Subtypes May Pave Novel Ways for Low-Grade Glioma Prognosis and Therapy
title_sort metabolic signature-based subtypes may pave novel ways for low-grade glioma prognosis and therapy
topic Cell and Developmental Biology
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8650219/
https://www.ncbi.nlm.nih.gov/pubmed/34888308
http://dx.doi.org/10.3389/fcell.2021.755776
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