Development of a prognostic model for osteosarcoma based on macrophage polarization-related genes using machine learning: implications for personalized therapy.

While neoadjuvant chemotherapy combined with surgical resection has improved the prognosis for patients with osteosarcoma, its impact on metastatic and recurrent cases remains limited. Immunotherapy is emerging as a promising alternative. However, the relationship between the phenotype of tumor-associated macrophages and the prognosis of osteosarcoma remains unclear. Differentially expressed gene during macrophage polarization were identified using the Monocle package. Weighted gene co-expression network analysis was conducted to select genes regulating macrophage polarization. The least absolute shrinkage and selection operator algorithm and multivariate Cox regression were used to construct long-term survival predictive strategies. Multiple machine learning algorithms identified target genes for pan-cancer analysis. Lentiviral transfection created stable strains with target gene knockdown, and CCK-8 and transwell migration assays verified the target gene's effects. Western blot and flow cytometry assessed the impact of target genes on macrophage polarization. A total of 141 genes regulating macrophage polarization were identified, from which eight genes were selected to construct prognostic models. Significant differences between high-risk and low-risk groups were observed in immune cell activation, immune-related signaling pathways, and immune function. The prognostic model and target gene were validated to provide more precise immunotherapy options for osteosarcoma and other tumors. BNIP3 knockdown decreased osteosarcoma cell proliferation and migration and promoted macrophage polarization to the M2 phenotype. The constructed prognostic model offers precise immunotherapy regimens and valuable insights into mechanisms underlying current studies. Furthermore, BNIP3 may serve as a potential immunotherapeutic target for osteosarcoma and other tumors.