A Glycerophospholipid Metabolism-Based Prognostic Model Guides Osteosarcoma Therapy

Area of Science:

• Oncology
• Molecular Biology
• Metabolomics

Background:

• Glycerophospholipid metabolism is crucial for cell function but its role in osteosarcoma (OS) is not well understood.
• Investigating this pathway may reveal new insights into OS progression, immunity, and treatment.

Purpose of the Study:

• To explore the role of glycerophospholipid metabolism in osteosarcoma.
• To develop a prognostic model based on glycerophospholipid metabolism activity.
• To identify potential therapeutic strategies for osteosarcoma.

Main Methods:

• Integrated bulk and single-cell RNA-seq data from multiple OS cohorts.
• Quantified glycerophospholipid metabolism activity using five algorithms to create a Glycerophospholipid Activity Score (GAS).
• Employed Cox regression and Lasso-Cox modeling for prognostic gene identification and model construction, followed by analyses of cell communication, immune infiltration, mutations, and drug sensitivity.

Main Results:

• Identified twelve cell types, with malignant OS cells exhibiting the lowest glycerophospholipid metabolism.
• Developed a GAS-based prognostic model that outperformed existing signatures.
• Found that high GAS is associated with increased immune gene expression, copy number alterations, and serves as an independent prognostic factor for OS.
• Predicted potential efficacy of lovastatin, simvastatin, and tamatinib for high-risk patients, and noted a correlation between higher GAS and poorer immunotherapy response.

Conclusions:

• The GAS-based stratification effectively predicts osteosarcoma prognosis.
• Revealed significant interactions between glycerophospholipid metabolism and tumor immunity.
• Provides a foundation for personalized therapeutic strategies and identifies novel drug candidates for high-risk osteosarcoma patients.