Work

A Glycerophospholipid Metabolism-Based Prognostic Model Guides Osteosarcoma Therapy

Year: 2025

Type: article

Abstract: BACKGROUND: Glycerophospholipid metabolism regulates membrane synthesis, signaling, and cell homeostasis, but its role in osteosarcoma (OS) progression, immunity, and therapy remains unclear. METHODS: We integrated bulk transcriptome data from TARGET (n = 85) and GEO cohorts (n = 124) with single-cell RNA-seq data (GSE162454, six OS samples). Glycerophospholipid metabolism activity was quantified using five algorithms to generate a GAS score. Prognostic genes were identified via univariate Cox regression, followed by Lasso-Cox modeling. Downstream analyses included cell communication, immune infiltration, mutation profiling, drug sensitivity, and immunotherapy response. RESULTS: Twelve cell types were identified; malignant OS cells showed the lowest glycerophospholipid metabolism. Nine genes correlated with GAS (r=±0.2, p < 0.01), six were used to construct the model, which outperformed 42 published signatures (C-index>0.7). High GAS patients exhibited elevated immune gene expression and copy number alterations (p < 0.05). GAS remained an independent prognostic factor (HR=3.58, 95 %CI:2.23-5.74, p < 0.001). Drug prediction highlighted lovastatin, simvastatin, and tamatinib for high-risk patients, and higher GAS associated with poor immunotherapy response (p < 0.01). CONCLUSION: GAS-based stratification not only robustly predicts OS prognosis but also reveals interactions between glycerophospholipid metabolism and tumor immunity, guiding personalized therapeutic strategies and highlighting novel drug candidates for high-risk patients.

Source: Translational Oncology

DOI: 10.1016/j.tranon.2025.102572