ArticleFrontiers in oncology2023
Heterogeneity in clinical prognosis, immune infiltration and molecular characteristics of three glycolytic subtypes in lower-grade gliomas.
Article in Frontiers in oncology, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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1 citing paper in PubMed, 1 citations in OpenAlex.
- Prognostic value of four immune-related genes in lower-grade gliomas: a biomarker discovery study.Frontiers in genetics · 2024Article
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7 authors at 2 institutions in 1 country.
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Abstract
Background and purpose: Lower-grade gliomas (LGG) exhibit a wide range of metabolic pathway changes, and metabolic reprogramming can be largely seen as a result of oncogenic driving events. Glycolysis, an important pathway of tumor energy source, has been poorly studied in gliomas. The aim of this article is to analyze the relationship between glycolysis and lower-grade glioma development and prognosis in order to explore the heterogeneous relevance of glycolysis in lower-grade gliomas. Methods and results: Our study searched the TCGA database and identified three glycolytic subtypes with significant prognostic differences by unsupervised clustering analysis of core glycolytic genes, named C1, C2, and C3. By analysis of clinical prognosis, somatic cell variation, and immune infiltration, we found that C3 had the best prognosis with molecular features of IDHmut-codel, followed by C1 with major molecular features of IDHmut-non-codel, G -CIMP high subtype, while C2 had the worst prognosis, mainly exhibiting IDHwt, G-CIMP low and mesenchymal-like subtypes with seven important CNV features, including CDKN2A/B deletion, chr7 gain and chr10 deletion, chr19/20 co-gain, EGFR amplification and PDGFRA/B deletion phenotypes were significantly increased, with the highest level of stemness and significant T-cell depletion features. Finally, to quantify the level of abnormal glycolysis and its impact on prognosis, we developed GlySig to reflect the glycolytic activity of LGG and integrated molecular features to construct nomogram that can be independently assessed to predict prognosis. Conclusions: Our study analyzed the tumor characteristics of different glycolytic states, and our findings explain and describe the heterogeneity of glycolytic metabolism within diffuse LGGs.
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