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The Heterogeneity of Infiltrating Macrophages in Metastatic Osteosarcoma and Its Correlation with Immunotherapy
Zhanchao Wang1, Huiqiao Wu1, Yu Chen1
1Department of Orthopaedics, Changzheng Hospital, Naval Medical University, Shanghai 200003, China.
Background:
Metastatic osteosarcoma is a common and fatal bone tumor. Several studies have found that tumor-infiltrating immune cells play pivotal roles in the progression of metastatic osteosarcoma. However, the heterogeneity of infiltrating immune cells across metastatic and primary osteosarcoma remains unclear.
Methods:
Immune infiltration analysis was carried out via the "ESTIMATE" and "xCell" algorithms in primary and metastatic osteosarcoma. Then, we evaluated the prognostic value of infiltrating immune cells in 85 osteosarcomas through the Kaplan-Meier (K-M) and receiver operating characteristic (ROC) curve. Infiltrations of macrophage M1 and M2 were evaluated in metastatic osteosarcoma, as well as their correlation with immune checkpoints. Macrophage-related prognostic genes were identified through Weighted Gene Coexpression Network Analysis (WGCNA), Lasso analysis, and Random Forest algorithm. Finally, a macrophage-related risk model had been constructed and validated.
Results:
Macrophages, especially the macrophage M1, sparingly infiltrated in metastatic compared with the primary osteosarcoma and predicted the worse overall survival (OS) and disease-free survival (DFS). Macrophage M1 was positively correlated with immune checkpoints PDCD1, CD274 (PD-L1), PDCD1LG2, CTLA4, and TIGIT. In addition, four macrophage-related prognostic genes (IL10, VAV1, CD14, and CCL2) had been identified, and the macrophage-related risk model had been validated to be reliable for evaluating prognosis in osteosarcoma. Simultaneously, the risk score showed a strong correlation with several immune checkpoints.
Conclusion:
Macrophages potentially contribute to the regulation of osteosarcoma metastasis. It can be used as a candidate marker for metastatic osteosarcoma' prognosis and immune checkpoints blockades (ICBs) therapy. We constructed a macrophage-related risk model through machine-learning, which might help us evaluate patients' prognosis and response to ICBs therapy.
