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Updated: Jan 12, 2026
Modeling Osteosarcoma Using Li-Fraumeni Syndrome Patient-derived Induced Pluripotent Stem Cells
Published on: June 13, 2018
A Glycerophospholipid Metabolism-Based Prognostic Model Guides Osteosarcoma Therapy
Yong Wen1, Donglian Wang2, Hongshen Wang1
1Department of Orthopedics, the second Affiliated Hospital of Guangzhou University of Chinese Medicine, Guangzhou 510120, Guangdong Province, China.
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.
