ArticleFrontiers in pharmacology2024
Integration of histone modification-based risk signature with drug sensitivity analysis reveals novel therapeutic strategies for lower-grade glioma.
Article in Frontiers in pharmacology, 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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2 citing papers in PubMed.
- Methionine concentration regulates LSD1 acetylation in glioma cells.Amino acids · 2026Article
- Metabolic reprogramming and immunosenescence: a new sight for glioma therapy.Frontiers in cell and developmental biology · 2026Review
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2 authors.
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Abstract
Background: Lower-grade glioma (LGG) exhibits significant heterogeneity in clinical outcomes, and current prognostic markers have limited predictive value. Despite the growing recognition of histone modifications in tumor progression, their role in LGG remains poorly understood. This study aimed to develop a histone modification-based risk signature and investigate its relationship with drug sensitivity to guide personalized treatment strategies. Methods: We performed single-cell RNA sequencing analysis on LGG samples (n = 4) to characterize histone modification patterns. Through integrative analysis of TCGA-LGG (n = 513) and CGGA datasets (n = 693 and n = 325), we constructed a histone modification-related risk signature (HMRS) using machine learning approaches. The model's performance was validated in multiple independent cohorts. We further conducted comprehensive analyses of molecular mechanisms, immune microenvironment, and drug sensitivity associated with the risk stratification. Results: We identified distinct histone modification patterns across five major cell populations in LGG and developed a robust 20-gene HMRS from 129 candidate genes that effectively stratified patients into high- and low-risk groups with significantly different survival outcomes (training set: AUC = 0.77, 0.73, and 0.71 for 1-, 3-, and 5-year survival; Conclusion: Our study establishes a novel histone modification-based prognostic model that not only accurately predicts LGG patient outcomes but also reveals potential therapeutic targets. The identified associations between risk stratification and drug sensitivity provide valuable insights for personalized treatment strategies. This integrated approach offers a promising framework for improving LGG patient care through molecular-based risk assessment and treatment selection.
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