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A prognostic model based on nucleotide metabolism genes in osteosarcoma
Songli Ju1, Lu Tao2, Xuyan Li3
1Department of Orthopedics, Affiliated Hospital of Zunyi Medical University, Zunyi, 563000, P.R. China. 9695170@qq.com.
Discover Oncology
|May 2, 2026
Summary
A new risk model using nucleotide metabolism genes aids osteosarcoma prognosis. This tool identifies high-risk patients with poor survival and aids in guiding chemotherapy decisions for better outcomes.
Area of Science:
- Oncology
- Metabolic Reprogramming
- Computational Biology
Background:
- Osteosarcoma presents a significant challenge, especially in adolescents.
- Current treatments improve survival but metastatic cases have poor prognoses.
- Metabolic reprogramming is crucial in tumor initiation and progression.
Purpose of the Study:
- To develop a robust risk prediction model for osteosarcoma.
- To identify key nucleotide metabolism-related genes impacting prognosis.
- To explore the relationship between metabolic reprogramming, immune status, and survival.
Main Methods:
- Utilized six machine learning algorithms to build a risk model.
- Included seven nucleotide metabolism-related genes: MYC, MUC1, IMPDH1, SAMHD1, NUDT13, UCK2, and NUDT16.
- Performed multivariate Cox regression and developed a nomogram for survival prediction.
Main Results:
- The risk prediction model demonstrated strong prognostic capability.
- High-risk patients showed significantly lower survival rates and increased immunosuppressive gene expression.
- The model serves as an independent prognostic indicator, and the nomogram accurately predicts survival rates.
Conclusions:
- The developed model offers superior prognostic prediction for osteosarcoma.
- Metabolic reprogramming significantly influences patient survival and immune status.
- This model shows promise in guiding chemotherapy strategies and improving patient outcomes.
