Osteosarcoma is a highly aggressive bone tumor that most commonly affects children and adolescents. Treatment and outcomes for osteosarcoma have remained unchanged over the past 30 years. The relationship between osteosarcoma and the immune microenvironment may represent a key to its undoing. We calculated the immune and stromal scores of osteosarcoma cases from the Target database using the ESTIMATE algorithm. Then we used the CIBERSORT algorithm to explore the tumor microenvironment and analyze immune infiltration of osteosarcoma. Differentially expressed genes (DEGs) were identified based on immune scores and stromal scores. Search Tool for the Retrieval of Interacting Genes Database (STRING) was utilized to assess protein–protein interaction (PPI) information, and Molecular Complex Detection (MCODE) plugin was used to screen hub modules of PPI network in Cytoscape. The prognostic value of the gene signature was validated in an independent GSE39058 cohort. Gene set enrichment analysis (GSEA) was performed to study the hub genes in signaling pathways. From 83 samples of osteosarcoma obtained from the Target dataset, 137 DEGs were identified, including 134 upregulated genes and three downregulated genes. Functional enrichment analysis and PPI networks demonstrated that these genes were mainly involved in neutrophil degranulation and neutrophil activation involved in immune response, and participated in neuroactive ligand–receptor interaction and staphylococcus aureus infection. Our study established an immune-related gene signature to predict outcomes of osteosarcoma, which may be important targets for individual treatment.

中文翻译：

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与骨肉瘤微环境相关的免疫相关预后生物标志物

骨肉瘤是一种高度侵袭性的骨肿瘤，最常影响儿童和青少年。在过去的 30 年中，骨肉瘤的治疗和结果保持不变。骨肉瘤与免疫微环境之间的关系可能是其毁灭的关键。我们使用 ESTIMATE 算法从 Target 数据库中计算了骨肉瘤病例的免疫和间质评分。然后我们使用CIBERSORT算法探索肿瘤微环境并分析骨肉瘤的免疫浸润。基于免疫评分和基质评分鉴定差异表达基因（DEG）。检索相互作用基因数据库 (STRING) 的搜索工具用于评估蛋白质-蛋白质相互作用 (PPI) 信息，和分子复合物检测 (MCODE) 插件用于在 Cytoscape 中筛选 PPI 网络的集线器模块。基因特征的预后价值在独立的 GSE39058 队列中得到验证。进行基因集富集分析（GSEA）以研究信号通路中的枢纽基因。从 Target 数据集中获得的 83 个骨肉瘤样本中，鉴定出 137 个 DEG，包括 134 个上调基因和 3 个下调基因。功能富集分析和 PPI 网络表明，这些基因主要参与中性粒细胞脱粒和中性粒细胞活化参与免疫反应，并参与神经活性配体-受体相互作用和金黄色葡萄球菌感染。我们的研究建立了一个免疫相关基因特征来预测骨肉瘤的结果，