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

Ganglei Li,Chenjie Gu,Jian Shen,Zhanxiong Wu,Yu Zhu,Jun Gu,Renya Zhan,Kaiyuan Huang,Feng Wang,Tiesong Zhang,Kangli Xu

doi:10.3389/fcell.2021.755776

Ganglei Li, Chenjie Gu + Show 9 more

Open Access

https://doi.org/10.3389/fcell.2021.755776

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Abstract

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.

Highlights

Low-grade glioma (LGG) is the most common slow-growing brain cancer in central nervous system neoplasms (De Andrade Costa et al, 2021)
The results indicated that there were 69 prognosis-related metabolic signatures in the the Cancer Genome Atlas (TCGA) database, and 73 prognosis-associated metabolic genes (Figure 1A)
We adopted ConsensusClusterPlus to explore reasonable classifications according to the characteristics of the 509 low-grade glioma (LGG) sample, and classified these similar characteristics genes into one category

Summary

INTRODUCTION

Low-grade glioma (LGG) is the most common slow-growing brain cancer in central nervous system neoplasms (De Andrade Costa et al, 2021). LGGs are characterized as indolent tumors, with survival rates that range from 1 to 15 years (Gargini et al, 2020). An intensive exploration of the regulation mechanism in LGG initiation and progression is vital for biomarker identification and determination of therapeutic targets. Aberrant cellular metabolism alters the metabolic and immune microenvironments, and has emerged as a therapeutic target in cancer diagnosis and therapy. Metabolism-associated genes may be a fruitful focus for identifying the genomic profiles and inner regulation mechanism of LGG. TIDE and the R package WGCNA were applied to evaluate potential clinical effects in immunotherapy and identify co-expression modules with associated metabolic subtypes. We identified eight potential biomarkers reflecting metabolic subtype characteristics which have potential become novel therapeutic targets for LGG therapies

MATERIALS AND METHODS

RESULTS

DATA AVAILABILITY STATEMENT