Jingyuan Wang ¹ Shuai Yan ² Sheng Han ^1*

• ¹ The First Affiliated Hospital of China Medical University, Shenyang, China
• ² Affiliated Hospital of Hebei University, Baoding, Hebei Province, China

Lower-grade glioma (LGG) exhibits significant heterogeneity in clinical outcomes, and current prognostic markers have limited predictive value. In this comprehensive study, we investigated the role of histone modifications in LGG progression and developed a novel prognostic model through integrated multi-omics analysis. Using single-cell RNA sequencing on LGG samples (n=4), combined with extensive analysis of TCGA-LGG (n=513) and two independent CGGA validation cohorts (n=693 and n=325), we identified distinct histone modification patterns across five major cell populations and constructed a 20-gene histone modification-related risk signature (HMRS) from 129 candidate genes. This signature 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; P<0.001). The model's prognostic accuracy was further enhanced by integrating clinical features (C-index>0.70). High-risk tumors demonstrated activation of multiple oncogenic pathways, particularly TGF-β and IL6-JAK-STAT3 signaling, along with distinct mutation profiles including TP53 (63% vs 28%), IDH1 (68% vs 85%), and ATRX (46% vs 20%).Notably, the high-risk group exhibited significantly elevated immune and stromal scores (P<0.001) with characteristic patterns of immune cell infiltration, especially in memory CD4+ T cells (P<0.001) and CD8+ T cells (P=0.001). Drug sensitivity analysis revealed differential responses to six therapeutic agents, including Temozolomide and various targeted drugs (P<0.05), suggesting potential strategies for personalized treatment. Furthermore, pan-cancer analysis demonstrated the model's broader applicability across multiple cancer types, particularly in kidney cancers and lung adenocarcinoma. Our findings not only establish a robust prognostic model but also provide deep insights into the molecular mechanisms underlying LGG progression, offering a promising framework for improving patient care through molecular-based risk assessment and personalized treatment selection. This integrated approach represents a significant advancement in understanding LGG biology and has important implications for clinical practice and therapeutic development.