Construction of gene-expression predictor model for patient outcome with renal cell carcinoma

肾细胞癌患者预后的基因表达预测模型的构建

基本信息

批准号：
18591764
负责人：
YAO Masahiro
金额：
$ 2.49万
依托单位：
Yokohama City University
依托单位国家：
日本
项目类别：
Grant-in-Aid for Scientific Research (C)
财政年份：
2006
资助国家：
日本
起止时间：
2006 至 2007
项目状态：
已结题

来源：
https://kaken.nii.ac.jp/grant/KAKENHI-PROJECT-18591764/
关键词：
renal cancer clear cell gene expression prognosis diagnosis 転移

项目摘要

Renal cell carcinomas (RCCs) are morphologically and genetically heterogeneous tumors and present diverse clinical courses. We used real-time quantitative PCR to examine the VCAMI and ADFP expression levels of more than 400 clear cell renal carcinomas. We evaluated their significances with respect to patient survival rates using the Cox regression model combined with the split-sample method. We demonstrated that high VCAM1 expression is strongly associated with cancerfine survival for patients with clear cell RCC after curative surgical resection. On the other hand, high ADFPexpression was associated with better cancerspecific survival. Next, We developed a scoring system using levels of gene expression to predict outcome for clear cell RCC patients. We selected differentially expressed genes firom the DNA microarray data of 27clear cell RCCs; 16 were metastasis phenotypes and 11 were not We compared the selected gene set with previously published data and identified 33 overlapping gen … More es closely associated with patient outcome. We selected the 12 top-ranked genes and confirmed the level of expression using quantitative reverse transcriptase PCR Multivariate Cox analysis revealed that three genes-要ascular cell adhesion molecule 1 (VCAM1), endothelin receptor type B (EDNRB), and regulator of G-protein signaling 5 (RGS5)-were the most tightly associated with cancerspecific survival and that higher expression of the three genes correlated with better outcome. A formula for an outcome predictor was generated from integration of the measurements of the expression levels of the three genes. Multivariate Cox models combined with a split-sample cress-validation method in a cohort of 386 clear cell RCC patients demonstrated that the derived score for outcome prediction was an independent predictor in cancerspecific survival tests. The accuracy of the prediction of cancer death after nephrectomy was improved by the inclusion of this score in receiver operating characteristic (ROC) analysis from multivariate logistic regression models, suggesting that a scoring system based on the expression levels of these three genes is useful in the prediction of survival far patients with clear cell RCC. Less

肾细胞癌 (RCC) 是形态和遗传异质性肿瘤，具有不同的临床过程，我们使用实时定量 PCR 检查了 400 多种肾透明细胞癌的 VCAMI 和 ADFP 表达水平，并评估了它们对患者的意义。另一方面，我们使用分割样本法的 Cox 回归模型证明，高 VCAM1 表达与治愈性手术切除后合并透明细胞肾细胞癌的患者的癌症良好生存率密切相关。另一方面，高 ADFP 表达与更好的癌症特异性生存相关，我们从 27 个透明细胞 RCC 的 DNA 微阵列数据中选择了差异表达的基因，该系统使用基因表达水平来预测转移。我们将选定的基因集与之前发表的数据进行比较，并确定了 33 个与患者结果密切相关的重叠基因。我们选择了 12 个排名最高的基因，并确认了这些基因。使用定量逆转录酶 PCR 多变量 Cox 分析的表达水平显示，三个基因 - 血管细胞粘附分子 1 (VCAM1)、B 型内皮素受体 (EDNRB) 和 G 蛋白信号传导调节因子 5 (RGS5) - 关联最紧密与癌症特异性生存相关，并且这三个基因的较高表达与更好的结果相关，这是通过将三个基因的表达水平的测量值与分割样本相结合而生成的。在 386 名透明细胞 RCC 患者队列中进行的 cress 验证方法表明，结果预测的衍生评分是癌症特异性生存测试中的独立预测因子。通过在接受者中纳入该评分，提高了肾切除术后癌症死亡预测的准确性。多变量逻辑回归模型的操作特征（ROC）分析表明，基于这三个基因表达水平的评分系统可用于预测透明细胞肾细胞癌患者的生存率。