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（RCCS）在形态和遗传上是异质性肿瘤，并呈现潜水员的临床课程。我们使用实时定量PCR检查了400多个透明细胞肾癌的VCAMI和ADFP表达水平。我们使用COX回归模型与拆分样本方法相对于患者的存活率进行了评估。我们证明，在治愈手术切除后，透明细胞RCC患者的cancerfine生存率高的VCAM1表达与癌细胞的存活密切相关。另一方面，高ADFPPERSEDS与更好的癌症特异性生存有关。接下来，我们使用基因表达水平开发了一个评分系统，以预测透明细胞RCC患者的结果。我们选择了不同表达的基因FIROM 27clear RCC的DNA微阵列数据； 16是转移表型，而11个不是我们比较了选定的基因与先前发表的数据，并确定了33个重叠的基因……与患者结局密切相关。我们选择了12个顶级基因，并使用定量逆转录酶PCR多变量COX分析证实了表达水平，表明三个基因所需的末端细胞粘附分子1（VCAM1）（VCAM1）（vCAM1），内皮蛋白受体B型B（EDNRB）（EDNRB）和较高的GENERS contive and Conlise concy and Conlise cancer 5（RGS 5） - WERE CANCER CANCER CANCER CANCER CANCER CANCER CANCER CANCAR 5（REGES）5（rgs）5（RGS5） - 旋转癌症5（rgs）5（rgs）。与更好的结果相关。结果预测因子的公式是由三个基因表达水平的测量值的整合产生的。在386名透明细胞RCC患者队列中，多变量COX模型与分式样本商标验证方法相结合，证明结果预测的得分是cancers特异性生存测试中的独立预测指标。通过将该分数纳入来自多元逻辑回归模型的接收器操作特征（ROC）分析中，提高了肾切除术后癌症死亡预测的准确性，这表明基于这三个基因的表达水平的评分系统可用于预测具有透明细胞RCC的存活率。较少的