ArticleFrontiers in cell and developmental biology2021
Metabolic Signature-Based Subtypes May Pave Novel Ways for Low-Grade Glioma Prognosis and Therapy.
Article in Frontiers in cell and developmental biology, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 papers.
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10 citing papers in PubMed, 22 citations in OpenAlex.
- Exploring the prognostic potential of m6A methylation regulators in low-grade glioma: implications for tumor microenvironment modulation.European journal of medical research · 2024Article
- Three Liquid-Liquid Phase Separation-Related Genes Associated with Prognosis in Glioma.Pharmacogenomics and personalized medicine · 2024Article
- N7-methylguanosin regulators-mediated methylation modification patterns and characterization of the immune microenvironment in lower-grade glioma.European journal of medical research · 2023Article
- Diagnostic significance of dysregulated miRNAs in T-cell malignancies and their metabolic roles.Frontiers in oncology · 2023Review
- AnPeerJ · 2023Article
- A Metabolic Plasticity-Based Signature for Molecular Classification and Prognosis of Lower-Grade Glioma.Brain sciences · 2022Article
- Development of a prognostic metabolic signature in stomach adenocarcinoma.Clinical & translational oncology : official publication of the Federation of Spanish Oncology Societies and of the National Cancer Institute of Mexico · 2022Article
- Prognosis and immune features of pyroptosis-related RNA patterns in low-grade glioma.Frontiers in oncology · 2022Article
- N6-methyladenosine RNA methylation regulator-related alternative splicing gene signature as prognostic predictor and in immune microenvironment characterization of patients with low-grade glioma.Frontiers in genetics · 2022Article
- Identification of Immunological Characteristics and Immune Subtypes Based on Single-Sample Gene Set Enrichment Analysis Algorithm in Lower-Grade Glioma.Frontiers in genetics · 2022Article
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Authors and funding
11 authors at 2 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
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.
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