头颈部鳞状细胞癌血管生成预后模型的构建及验证

Construction and validation of an angiogenesis-related prognostic model in head and neck squamous cell carcinoma

目的通过生物信息学的方法构建头颈部鳞状细胞癌(HNSCC)血管生成相关基因(angiogenesis related genes,ARGs)预后模型,验证模型的预测价值。方法从UCSC Xena数据库下载头颈部鳞状细胞癌(TCGA-HNSCC)数据集,获取差异表达的ARGs,通过单因素Cox回归分析、蛋白互作网络(PPI)筛选预后相关核心ARGs,利用LASSO回归分析构建预后模型,分析高低风险组的预测效能,并使用GSE41613数据集进行验证。利用ESTIMATE算法对高低风险组进行免疫浸润分析,分析高低风险组免疫检查点基因表达的差异。对高低风险组进行药物敏感性分析。RT-qPCR验证PLAU、VEGF-C两个预后基因在舌鳞状细胞癌的表达情况。结果在TCGA-HNSCC中获得了414个差异表达的ARGs,基于14个ARGs构建了HNSCC预后模型。Kaplan-Meier生存曲线显示高风险组的生存时间低于低风险组(P<0.001),ROC曲线显示其有较高的预测价值(1年、3年、5年的AUC值分别为0.675、0.688、0.644),其预后价值在GSE41613数据集得到了验证。免疫浸润分析提示低风险组具有更高的免疫浸润评分,低风险组高表达免疫检查点基因。药物敏感性分析表明高低风险组对包括顺铂(cisplatin)、Tozasertib在内的多种药物的敏感性存在差异(P<0.05)。RT-qPCR结果显示PLAU、VEGF-C高表达于舌鳞状细胞癌(P<0.05)。结论由14个ARGs组成的HNSCC风险评分模型,可有效预测HNSCC患者的预后及对药物治疗的反应。

Objective To construct a prognostic model for angiogenesis-related genes(ARGs)in head and neck squamous cell carcinoma(HNSCC)using bioinformatics methods and validate its predictive value.Methods The HNSCC dataset(TCGA-HNSCC)was downloaded from the UCSC Xena database to obtain differentially expressed ARGs,univariate Cox regression analysis and protein-protein interaction(PPI)network were used to screen prognostic-related core ARGs.We then constructed a prognostic model using LASSO regression and analyzed its predictive efficacy in high and low-risk groups.We verified the model using the GSE41613 dataset.The ESTIMATE algorithm was used to analyze the immune infiltration of the high and low-risk groups,and differential expression of immune checkpoint genes between these groups was analyzed.Drug sensitivity was also analyzed for these groups.RT-qPCR was used to verify the expression of two prognostic genes,PLAU and VEGF-C,in tongue squamous cell carcinoma.Results We identified 414 differentially expressed ARGs in TCGA-HNSCC and constructed a prognostic model for HNSCC based on 14 ARGs.Kaplan-Meier survival curve showed that the high-risk group had shorter survival times compared to the low-risk group(P<0.001),and the ROC curve demonstrated its strong predictive value(AUC values at 1,3 and 5 years were 0.675,0.688 and 0.644,respectively),and the model’s prognostic value was validated in the GSE41613 dataset.Immune infiltration analysis suggested that the low-risk group had a higher immune infiltration score and high expression of immune checkpoint genes.Drug sensitivity analysis revealed varying sensitivity to multiple drugs,including Cisplatin and Tozasertib between high and low-risk groups(P<0.05).The results of RT-qPCR showed that PLAU and VEGF-C were highly expressed in tongue squamous cell carcinoma(P<0.05).Conclusion The HNSCC risk score model composed of 14 ARGs can effectively predict the prognosis and drug therapy response in HNSCC patients.