ArticleTranslational cancer research2025
Development of a fatty acid metabolism (FAM)-related gene signature for prognosis prediction and personalized therapy in lower grade gliomas.
Article in Translational cancer research, 2025. 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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Who cites it
1 citing paper in PubMed.
- Ensemble Machine Learning Approaches Predict Survival in Lower-Grade Glioma Based on Glycosphingolipid Gene Expression and Metabolic Modeling.Computational and structural biotechnology journal · 2026Article
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4 authors.
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Abstract
Background: Lower grade gliomas (LGGs) exhibit significant molecular and clinical heterogeneity, challenging accurate prognosis and treatment strategies based on current parameters. Alterations in fatty acid metabolism (FAM) have been implicated in tumor initiation, proliferation, and metastasis. This study investigates the role of FAM in LGGs to enhance patient management. Methods: Gene expression data from The Cancer Genome Atlas (TCGA) database were analyzed to identify FAM-related genes with differential expression in LGGs. A prognostic model was constructed using Cox regression and least absolute shrinkage and selection operator regression. The model's predictive efficacy was validated using both TCGA test and Chinese Glioma Genome Atlas databases. Functional analyses included Gene Ontology, Gene Set Variation Analysis, Kyoto Encyclopedia of Genes and Genomes, and immune infiltration analysis. Drug sensitivity was assessed based on patient risk scores. Finally, we utilized the Human Protein Atlas (HPA) database to conduct a comparative analysis of protein expression patterns for the identified prognostic genes between LGG samples and normal cerebral cortex tissue. Results: The prognostic model comprised four genes: carnitine palmitoyltransferase 2 ( Conclusions: This study underscores the prognostic role of the FAM-related risk model for LGGs. Assessing patient risk scores through this model could help tailor personalized treatments, providing valuable guidance for clinical decision-making.
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