Immune-related prognosis biomarkers associated with osteosarcoma microenvironment
Immune-related prognosis biomarkers associated with osteosarcoma microenvironment
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DOI：
10.1186/s12935-020-1165-7
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发表时间：
2020-03-16
影响因子：
5.8
通讯作者：
Ma, Liheng
中科院分区：
文献类型：
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作者：
Hong, Weifeng;Yuan, Hong;Ma, Liheng
背景 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。骨肉瘤的治疗和结果在过去 30 年中保持不变。骨肉瘤与免疫微环境之间的关系可能是其毁灭的关键。方法 我们使用 ESTIMATE 算法计算 Target 数据库中骨肉瘤病例的免疫和基质评分。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。根据免疫评分和基质评分鉴定差异表达基因（DEG）。利用相互作用基因数据库检索搜索工具（STRING）评估蛋白质-蛋白质相互作用（PPI）信息，并利用分子复合物检测（MCODE）插件筛选Cytoscape中PPI网络的枢纽模块。基因特征的预后价值在独立的 GSE39058 队列中得到了验证。进行基因集富集分析（GSEA）来研究信号通路中的枢纽基因。结果 从Target数据集中获得的83个骨肉瘤样本中，鉴定出137个DEG，其中包括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，这些基因主要参与免疫反应中的中性粒细胞脱颗粒和中性粒细胞活化，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。结论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，这可能是个体治疗的重要目标。
Background 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. Methods 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. Results 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. Conclusions Our study established an immune-related gene signature to predict outcomes of osteosarcoma, which may be important targets for individual treatment.