Osteosarcoma typically arises during adolescence, posing a significant challenge. Despite comprehensive treatment strategies encompassing surgery, radiation therapy, and chemotherapy, which can notably enhance long-term survival rates among osteosarcoma patients, the 5-year survival rate for metastatic cases remains discouragingly low. Consequently, early diagnosis and prompt intervention are paramount in improving the prognosis of patients afflicted with this condition. Metabolic reprogramming holds paramount significance in the initiation and progression of tumors. In this meticulous investigation, we devised a risk prediction model that encompasses seven pivotal nucleotide metabolism-related genes: MYC, MUC1, IMPDH1, SAMHD1, NUDT13, UCK2, and NUDT16. This model was formulated leveraging six advanced machine learning algorithms. The results demonstrated that the risk prediction model exhibited robust prognostic predictive capability. Notably, patients identified with a high-risk phenotype exhibited a significantly lower long-term survival rate, coupled with elevated expression of immunosuppressive genes, highlighting the importance of metabolic reprogramming in influencing both survival outcomes and immune status. The multivariate Cox regression analysis confirmed that our model serves as an independent prognostic indicator, significantly impacting the long-term prognosis of osteosarcoma patients. Subsequently, we developed and validated a nomogram, which accurately predicts 1-, 3-, and 5-year survival rates for these patients. Furthermore, we compared chemosensitivity between high- and low-risk groups, gaining valuable insights into potential therapeutic differences. In conclusion, this model demonstrates superior prognostic predictive capability and holds promise in guiding chemotherapy treatment strategies for osteosarcoma patients, thereby enhancing treatment outcomes.
Ju et al. (Sat,) studied this question.