目的: 通过生物信息学分析方法构建肾透明细胞癌上皮间质转化基因预后模型。方法: 从TCGA数据库中下载肾透明细胞癌的基因芯片及临床数据,获得72例正常肾组织样本和539例肾透明细胞癌组织样本,利用wilcoxon秩和检验进行差异分析,筛选出肾透明细胞癌差异表达的EMT基因,对其进行GO和KEGG功能富集分析,使用单因素COX回归及LASSO回归筛选与肾透明细胞癌预后相关差异表达的EMT基因,并构建及验证风险预测模型。结果: 肾透明细胞癌共筛选93个EMT差异基因,有41个差异基因与患者总体生存率相关,从中筛选6个核心EMT差异基因用于构建肾透明细胞癌预后模型,训练集ROC曲线下的面积(AUC=0.717),验证集ROC曲线下的面积(AUC=0.771 )。通过风险模型及临床变量(包括年龄,性别、临床分期)进行多因素COX回归分析,结果表明,该预后模型可作为肾透明细胞癌独立预后因素(P＜0.001)。结论: 通过构建及验证的EMT预后模型可能预测肾透明细胞癌患者的预后。
Objective: To construct a prognostic model for epithelial to mesenchymal transition (EMT) in clear cell renal cell carcinoma by bioinformatics, and use the model to predict the survival and prognosis of patients with renal clear cell carcinoma. Methods: The gene chip and clinical data of renal clear cell carcinoma from the TCGA database were download, and 72 normal renal tissue samples and 539 renal clear cell carcinoma tissue samples were obtained. Wilcoxon rank-sum test was used for differential analysis to screen renal clear cell carcinoma. Differentially expressed EMT genes were analyzed by GO and KEGG functional enrichment analysis. Single-factor COX regression and LASSO regression were used to screen the differentially expressed EMT genes related to the prognosis of clear cell renal cell carcinoma, and the risk-predicting model was constructed and verified. Results: A total of 93 differential EMT genes were screened in clear cell renal cell carcinoma, 41 differential genes were related to the overall survival rate of patients, and 6 key EMT differential genes were screened for construction a prognostic model of clear cell renal cell carcinoma. The area under the ROC curve of the training set was 0.717(AUC=0.717), the area under the ROC curve of the validation set was 0.771(AUC=0.771). Multivariate COX regression analysis was conducted with risk model and clinical variables (including age, gender, clinical stage). The results showed that the prognostic model could be used as an independent prognostic factor for clear cell renal cell carcinoma (P＜0.001). Conclusion: The constructed and validated EMT prognostic model could be used to predict the prognosis of patients with renal clear cell carcinoma.
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