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:	Ganglei Li, Zhanxiong Wu, Jun Gu, Yu Zhu, Tiesong Zhang, Feng Wang, Kaiyuan Huang, Chenjie Gu, Kangli Xu, Renya Zhan, Jian Shen
Formato:	article
Lenguaje:	EN
Publicado:	Frontiers Media S.A. 2021
Materias:	low-grade glioma metabolic signature subtypes prognosis immune characteristics Biology (General) QH301-705.5
Acceso en línea:	https://doaj.org/article/4081c3bc4a9e4bcca4056144667cfe62
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spelling	oai:doaj.org-article:4081c3bc4a9e4bcca4056144667cfe622021-11-30T13:13:59ZMetabolic Signature-Based Subtypes May Pave Novel Ways for Low-Grade Glioma Prognosis and Therapy2296-634X10.3389/fcell.2021.755776https://doaj.org/article/4081c3bc4a9e4bcca4056144667cfe622021-11-01T00:00:00Zhttps://www.frontiersin.org/articles/10.3389/fcell.2021.755776/fullhttps://doaj.org/toc/2296-634XMetabolic 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.Ganglei LiZhanxiong WuJun GuYu ZhuTiesong ZhangFeng WangKaiyuan HuangChenjie GuKangli XuRenya ZhanJian ShenFrontiers Media S.A.articlelow-grade gliomametabolic signaturesubtypesprognosisimmune characteristicsBiology (General)QH301-705.5ENFrontiers in Cell and Developmental Biology, Vol 9 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	low-grade glioma metabolic signature subtypes prognosis immune characteristics Biology (General) QH301-705.5
spellingShingle	low-grade glioma metabolic signature subtypes prognosis immune characteristics Biology (General) QH301-705.5 Ganglei Li Zhanxiong Wu Jun Gu Yu Zhu Tiesong Zhang Feng Wang Kaiyuan Huang Chenjie Gu Kangli Xu Renya Zhan Jian Shen Metabolic Signature-Based Subtypes May Pave Novel Ways for Low-Grade Glioma Prognosis and Therapy
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.
format	article
author	Ganglei Li Zhanxiong Wu Jun Gu Yu Zhu Tiesong Zhang Feng Wang Kaiyuan Huang Chenjie Gu Kangli Xu Renya Zhan Jian Shen
author_facet	Ganglei Li Zhanxiong Wu Jun Gu Yu Zhu Tiesong Zhang Feng Wang Kaiyuan Huang Chenjie Gu Kangli Xu Renya Zhan Jian Shen
author_sort	Ganglei Li
title	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_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_sort	metabolic signature-based subtypes may pave novel ways for low-grade glioma prognosis and therapy
publisher	Frontiers Media S.A.
publishDate	2021
url	https://doaj.org/article/4081c3bc4a9e4bcca4056144667cfe62
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Metabolic Signature-Based Subtypes May Pave Novel Ways for Low-Grade Glioma Prognosis and Therapy

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