Identification and validation of an eight-gene expression signature for predicting high Fuhrman grade renal cell carcinoma
Identification and validation of an eight-gene expression signature for predicting high Fuhrman grade renal cell carcinoma
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鉴定和验证用于预测高福尔曼级别肾细胞癌的 8 基因表达特征。
DOI：
10.1002/ijc.30535
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发表时间：
2017-03-01
影响因子：
6.4
通讯作者：
Ye, Dingwei
中科院分区：
文献类型：
--
作者：
Wan, Fangning;Zhu, Yao;Ye, Dingwei
透明细胞肾细胞癌（ccRCC）是一种具有异质性结局的恶性肿瘤。目前，肾肿块活检通常用于提取疾病特征和辅助预后。虽然在目前的报告中，恶性疾病的病理诊断是准确的，但使用活检标本进行Fuhrman分级的分类仍远未被看好。为了生成区分高级别ccRCC的基因签名，我们使用癌症基因组图谱（TCGA）数据库来开发用于区分高级别（G3/4）和低级别（G1/2）疾病的基因表达签名。分别在283个冷冻肾癌样本和127个离体肾肿块活检样本中进一步验证了表达谱的性能和临床用途。曲线下面积（AUC）用于量化辨别能力，并使用De-long检验进行比较。使用发现数据集，我们确定了高级别疾病的24个基因签名，AUC为0.884。在应用于开发数据集后，定义了八个基因的谱，并实现了0.823的AUC。八基因组的准确性在肾肿块活检（RMB）样品中保持（AUC=0.821）。总之，使用三阶段设计，我们验证了用于预测ccRCC的高Fuhrman分级的八个基因表达特征。该工具可能有助于揭示ccRCC活检标本的特征。
Clear cell renal cell carcinoma (ccRCC) is a malignancy with heterogeneous outcomes. Currently, renal mass biopsies are commonly employed to extract disease characteristics and aid prognosis. Although the pathological diagnosis of malignant disease is accurate in contemporary reports, the classification of Fuhrman grade using biopsy specimens remains far from promising. To generate a gene signature to distinguish high-grade ccRCC, we used the cancer genome atlas (TCGA) database to develop a gene expression signature for distinguishing high-grade (G3/4) from low-grade (G1/2) disease. The expression profile was further validated for performance and clinical use in 283 frozen renal cancer samples and 127 ex vivo renal mass biopsy samples, respectively. The area under curve (AUC) was used to quantify discriminative ability and was compared using the De-long test. Using the discovery dataset, we identified a 24-gene signature for high-grade disease with an AUC of 0.884. After applied to the development dataset, an eight-gene profile was defined and achieved an AUC of 0.823. Accuracy of eight-gene panel was maintained in the renal mass biopsies (RMB) samples (AUC=0.821). In summary, using three-stage design, we validated an eight-gene expression signature for predicting high Fuhrman grade of ccRCC. This tool may help to reveal the characteristics of ccRCC biopsy specimens.