ArticleAmerican journal of cancer research2024
Leveraging FAM features to predict the prognosis of LGG patients and immunotherapy outcome.
Article in American journal of cancer research, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.
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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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Who cites it
2 citing papers in PubMed.
- Molecular mechanisms and clinical significance of the glioma metabolic-immune axis: a comprehensive review.Frontiers in immunology · 2026Review
- A Promising Prognostic Signature Consisting of Fatty Acid Metabolism Genes based on Machine Learning Predicts Biochemical Recurrence and Aids ARSI Therapy in Prostate Cancer.Journal of Cancer · 2025Article
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Authors and funding
9 authors.
Funding
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Abstract
Heterogeneity at biological and transcriptomic levels poses a challenge in defining and typing low-grade glioma (LGG), leading to a critical need for specific molecular signatures to enhance diagnosis, therapy, and prognostic evaluation of LGG. This study focused on fatty acid metabolism (FAM) related genes and prognostic features to investigate the mechanisms and treatment strategies for LGG cell metastasis and invasion. By screening 158 FAM-related genes and clustering 512 LGG samples into two subtypes (C1 and C2), differential gene expression analysis and functional enrichment were performed. The immune cell scores and prognosis were compared between the two subtypes, with C1 showing poorer outcomes and higher immune scores. A four-gene signature (PHEX, SHANK2, HOPX, and LGALS1) was identified and validated across different datasets, demonstrating a stable predictive effect. Cellular experiments confirmed the roles of LGALS1 and HOPX in promoting tumor cell proliferation, migration, and invasion, while SHANK2 exhibited a suppressive effect. This four-gene signature based on FAM-related genes offers valuable insights for understanding the pathogenesis and clinical management of LGG.
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