Abstract：
We sought to determine neutrophil extracellular trap (NET)-related genes\' potential value in improving the efficacy of diagnosis and identifying novel therapeutic targets for osteosarcoma. Data were obtained from TARGET, GEO, and CCLE database. Differentially expressed genes were identified between the subtypes based on NET-related genes. PPI network was constructed using STRING, following by ClueGO enrichment analysis. Infiltration of immune cells was calculated by ssGSEA. Risk Score model was built by LASSO Cox regression analysis. Western blot and qRT-PCR were applied to validate the expression of genes used in the model. We identified 19 NET-related genes with prognostic potential in osteosarcoma using univariate Cox regression analysis. Patients from TARGET were clustered into two subtypes with distinct prognosis and immune features. 381 DEGs were identified between the two NET subtypes. Risk Score based on BST1, SELPLG, FPR1 and TNFRSF10C was reliable to predict the prognosis of osteosarcoma patients. The four genes expressed significantly lower in osteosarcoma than normal cells. Low Risk Score individuals only existed in C1 subtype with better prognosis. Osteosarcoma were clustered into two subtypes based on NET-related genes. Risk Score model constructed by four NET-related gene was able to independently predict the prognosis of osteosarcoma.
摘要：
我们试图确定中性粒细胞胞外诱捕网（NET）相关基因在提高骨肉瘤诊断效果和确定新的治疗靶点方面的潜在价值。数据来自TARGET，GEO,和CCLE数据库。基于NET相关基因鉴定了亚型之间的差异表达基因。PPI网络是使用STRING构建的，接下来是ClueGO富集分析。通过ssGSEA计算免疫细胞的浸润。通过LASSOCox回归分析建立风险评分模型。Westernblot和qRT-PCR用于验证模型中使用的基因的表达。我们使用单变量Cox回归分析鉴定了19个NET相关基因在骨肉瘤中具有预后潜力。来自TARGET的患者聚集为具有不同预后和免疫特征的两种亚型。在两个NET亚型之间识别出381个DEG。基于BST1、SELPLG、FPR1和TNFRSF10C是预测骨肉瘤患者预后的可靠指标。这四个基因在骨肉瘤中的表达明显低于正常细胞。低风险评分个体仅存在于预后较好的C1亚型中。根据NET相关基因将骨肉瘤分为两个亚型。由4个NET相关基因构建的风险评分模型能够独立预测骨肉瘤的预后。
