Wu Guangzhen, Xu Yingkun, Han Chenglin, Wang Zilong, Li Jiayi, Wang Qifei, Che Xiangyu
Department of Urology, The First Affiliated Hospital of Dalian Medical University, Dalian, China.
Department of Urology, Shandong Provincial Hospital, Cheeloo College of Medicine, Shandong University, Jinan, China.
J Oncol. 2020 Dec 30;2020:6657013. doi: 10.1155/2020/6657013. eCollection 2020.
To construct a survival model for predicting the prognosis of patients with kidney renal clear cell carcinoma (KIRC) based on gene expression related to immune response regulation.
KIRC mRNA sequencing data and patient clinical data were downloaded from the TCGA database. The pathways and genes involved in the regulation of the immune response were identified from the GSEA database. A single factor Cox analysis was used to determine the association of mRNA in relation to patient prognosis ( < 0.05). The prognostic risk model was further established using the LASSO regression curve. The survival prognosis model was constructed, and the sensitivity and specificity of the model were evaluated using the ROC curve.
Compared with normal kidney tissues, there were 28 dysregulated mRNA expressions in KIRC tissues ( < 0.05). Univariate Cox regression analysis revealed that 12 mRNAs were related to the prognosis of patients with renal cell carcinoma. The LASSO regression curve drew a risk signature consisting of six genes: TRAF6, FYN, IKBKG, LAT2, C2, IL4, EREG, TRAF2, and IL12A. The five-year ROC area analysis (AUC) showed that the model has good sensitivity and specificity (AUC >0.712).
We constructed a risk prediction model based on the regulated immune response-related genes, which can effectively predict the survival of patients with KIRC.
基于与免疫反应调节相关的基因表达构建一个预测肾透明细胞癌（KIRC）患者预后的生存模型。
从TCGA数据库下载KIRC mRNA测序数据和患者临床数据。从GSEA数据库中识别参与免疫反应调节的通路和基因。采用单因素Cox分析确定mRNA与患者预后的相关性（<0.05）。使用LASSO回归曲线进一步建立预后风险模型。构建生存预后模型，并使用ROC曲线评估模型的敏感性和特异性。
与正常肾组织相比，KIRC组织中有28种mRNA表达失调（<0.05）。单因素Cox回归分析显示，12种mRNA与肾细胞癌患者的预后相关。LASSO回归曲线得出了一个由六个基因组成的风险特征：TRAF6、FYN、IKBKG、LAT2、C2、IL4、EREG、TRAF2和IL12A。五年ROC面积分析（AUC）表明该模型具有良好的敏感性和特异性（AUC>0.712）。
我们基于免疫反应调节相关基因构建了一个风险预测模型，该模型可以有效预测KIRC患者的生存情况。
