Deubiquitinase Genes as Prognostic Biomarkers in Osteosarcoma: Insights From Single-Cell Sequencing and Machine Learning-Based Prognostic Modeling.

Osteosarcoma (OS) is a highly aggressive bone tumor characterized by poor prognosis and frequent metastasis. Despite advances in treatment, the survival rate remains low. This study investigates the role of deubiquitinase (DUB) genes in OS to identify novel prognostic biomarkers and therapeutic targets. We analyzed the expression of DUB genes using single-cell RNA sequencing (scRNA-seq) data from OS samples. Bulk RNA sequencing datasets were also assessed for survival correlation. A prognostic model was constructed by integrating DUB gene expression with clinical features using machine learning algorithms, including random forest and support vector machines. Immune cell infiltration in the tumor microenvironment (TME) was evaluated based on correlations between DUB gene expression and immune cell abundance. Single-cell analysis revealed significant heterogeneity in DUB gene expression across OS cell types, influencing tumor progression and cell-cell communication. The constructed prognostic model stratified OS patients into the high- and low-risk groups with significantly different survival outcomes. High-risk patients exhibited reduced immune cell infiltration and more aggressive clinical characteristics. The model was validated using three independent cohorts (TARGET-OS, GSE16091, and GSE21257), demonstrating high robustness and clinical utility. DUB genes are critical regulators of OS progression and immune modulation. The developed prognostic model may serve as a promising tool for risk stratification and personalized treatment strategies in OS.