Abstract
BACKGROUND: Osteosarcoma is an aggressive bone tumor that mainly affects children and adolescents. Studies have found that pathways involved in programmed cell death (PCD) are important in the progression of OS and response to chemotherapy. However, there is a lack of comprehensive and detailed studies on PCD-related biomarkers that can reliably predict the prognosis of patients with OS and evaluate their response to therapy. METHODS: First, we performed multiple analyses on the TARGET-OS cohort to screen differentially expressed genes (DEGs) associated with PCD. Subsequently, we developed a PCD-based prognostic model using mutation analysis, survival analysis, and machine learning algorithms. By using the cell death index (CDI) as a core biomarker, we evaluated its predictive accuracy in terms of drug sensitivity and immunotherapy response and validated the model. In addition, a comprehensive evaluation of the tumor microenvironment (TME) was conducted, exploring associations with immune checkpoints and incorporating single-cell RNA sequencing data to deliver an overall perspective of the TME. Finally, we performed wet experiments to demonstrate the role of TRIM8 in osteosarcoma. RESULTS: We screened out 285 DEGs, which were mainly enriched in pathways related to apoptosis, autophagy, and necrosis. Based on these DEGs, we successfully established a model containing 17 genes. The two risk groups divided according to the model showed significant differences in clinicopathological characteristics and survival in the training set and validation set. Through multiple analyses such as CIBERSORT, we described the OS immune landscape in detail and obtained the correlation between CDI values and immune checkpoint markers. Single-cell transcriptomics studies provided us with many immunological insights, emphasizing the close relationship between high CDI values and tumor-infiltrating lymphocytes (Til). A relationship was observed between the genes of the CDI model and various drugs, with notable variations in drug sensitivity across different risk groups. Moreover, the TIDE scores suggested that people classified within the low CDI group could benefit from immunotherapy. Finally, we demonstrated through wet experiments that the model gene TRIM8 promoted the proliferation and migration of osteosarcoma. CONCLUSION: The model not only has a high accuracy in survival prediction, but also has great potential in many aspects such as immunotherapy effect prediction and sensitive drug screening. Our work not only helps researchers deepen their understanding of OS, but also provides a more powerful tool for improving the current clinical treatment status.