Song Delei, Yin Xuqing, Che Chunqing
Department of West Hospital Orthopaedic Trauma, Zibo Central Hospital, No. 54 Gongqingtuan Road, Zibo, 255036, China.
Department of East Hospital Orthopaedic Trauma, Zibo Central Hospital, Zibo, 255036, China.
Appl Biochem Biotechnol. 2025 Jan;197(1):55-72. doi: 10.1007/s12010-024-05021-2. Epub 2024 Aug 3.
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.
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能及识别新治疗靶点方面的潜在价值。数据来源于TARGET、GEO和CCLE数据库。基于NET相关基因在各亚型间鉴定差异表达基因。使用STRING构建蛋白质-蛋白质相互作用（PPI）网络，随后进行ClueGO富集分析。通过单样本基因集富集分析（ssGSEA）计算免疫细胞浸润情况。采用套索Cox回归分析建立风险评分模型。应用蛋白质免疫印迹法（Western blot）和实时定量聚合酶链反应（qRT-PCR）验证模型中所用基因的表达。通过单变量Cox回归分析，我们在骨肉瘤中鉴定出19个具有预后潜力的NET相关基因。来自TARGET的患者被聚类为两个具有不同预后和免疫特征的亚型。在这两个NET亚型间鉴定出381个差异表达基因（DEG）。基于BST1、SELPLG、FPR1和TNFRSF10C的风险评分可可靠地预测骨肉瘤患者的预后。这四个基因在骨肉瘤中的表达显著低于正常细胞。低风险评分个体仅存在于预后较好的C1亚型中。骨肉瘤基于NET相关基因被聚类为两个亚型。由四个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。
