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

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.

N7-methylguanosin regulators-mediated methylation modification patterns and characterization of the immune microenvironment in lower-grade glioma

“…In most cases, LGGs develop into higher-grade tumors, and approximately 50–75% of individuals with LGG frequently develop deterioration, pathological progression, or die. It is, therefore, crucial to find out the mechanisms of regulation of LGG initiation and progression thoroughly for biomarker identification and therapeutic targeting [ 20 ].…”

Identification of Immunological Characteristics and Immune Subtypes Based on Single-Sample Gene Set Enrichment Analysis Algorithm in Lower-Grade Glioma

“…Individualized therapeutic schedules were because of the natural intrinsic heterogeneity of LGG (Deng et al, 2020). Li et al (2021) provided a metabolic signature-based subgrouping method for LGG and Zhou et al (2021) divided LGG into three clusters based on a tertiary lymphoid structure to provide potential treating options. Since the existence of an afferent system between the brain and peripheral immune system had been demonstrated (Qi et al, 2020), immunotherapy would be a promising strategy for its ability to penetrate the blood-brain barrier (Xu et al, 2020).…”

A Metabolic Plasticity-Based Signature for Molecular Classification and Prognosis of Lower-Grade Glioma

“…Although some studies have constructed some gene signatures [ 20 , 21 , 22 , 23 , 24 ], biomarkers for predicting the prognosis of LGG are still needed. The present study constructed a metabolic signature for Glioma classification.…”