Multi-omics Analysis of Histone-related Genes in Osteosarcoma: A Multidimensional Integrated Study Revealing Drug Sensitivity and Immune Microenvironment Characteristics.

Abstract

IntroductionOsteosarcoma (OS) is a highly aggressive primary bone malignancy with poor prognosis. Histone modifications play crucial roles in tumor progression, but their systematic investigation in OS remains unexplored.MethodsThis study integrated single-cell RNA sequencing data and large-scale clinical information to systematically analyze the spatial heterogeneity of histone modifications in OS and their clinical significance. We employed Seurat for single-cell data analysis, CellChat for cell-cell communication network analysis, and LASSO Cox regression to construct a prognostic model. Additionally, we conducted functional enrichment analysis, immune characteristics analysis, and drug sensitivity prediction.ResultsWe identified five major cell types in the OS microenvironment and discovered significant differences in histone modification levels among different cell types, with osteosarcoma cells and endothelial cells exhibiting higher modification levels. Cell-cell communication network analysis revealed the importance of signaling pathways such as SPP1, CypA, MIF, IGFBP, and VEGF in OS. Based on nine histone modification-related genes, we constructed an efficient prognostic model (AUC values of 0.713, 0.845, and 0.888 for 1-, 3-, and 5-year predictions, respectively), which was validated in an external cohort (AUC = 0.808). Immune microenvironment analysis showed significantly higher proportions of CD8+ T cells and Treg cells in the low-risk group. Drug sensitivity analysis revealed that the low-risk group was more sensitive to Imatinib, Rapamycin, and Sunitinib, while the high-risk group was more sensitive to MAPK pathway inhibitors.ConclusionThis study systematically revealed the spatial heterogeneity of histone modifications in OS and their clinical significance for the first time, proposing an "epigenetic-immune" regulatory network hypothesis and developing a histone modification-based prognostic model. Our proposed "epigenetic-guided personalized medication strategy" provides new insights for precision treatment of OS, potentially significantly improving patient prognosis.