Department of Laboratory Medicine and Pathobiology, University of Toronto, Toronto, Canada.
Eur Urol. 2011 May;59(5):721-30. doi: 10.1016/j.eururo.2011.01.004. Epub 2011 Jan 13.
Renal cell carcinoma (RCC) encompasses different histologic subtypes. Distinguishing between the subtypes is usually made by morphologic assessment, which is not always accurate.
Our aim was to identify microRNA (miRNA) signatures that can distinguish the different RCC subtypes accurately.
DESIGN, SETTING, AND PARTICIPANTS: A total of 94 different subtype cases were analysed. miRNA microarray analysis was performed on fresh frozen tissues of three common RCC subtypes (clear cell, chromophobe, and papillary) and on oncocytoma. Results were validated on the original as well as on an independent set of tumours, using quantitative reverse transcription-polymerase chain reaction (qRT-PCR) analysis with miRNA-specific primers.
Microarray data were analysed by standard approaches. Relative expression for qRT-PCR was determined using the ΔΔC(T) method, and expression values were normalised to small nucleolar RNA, C/D box 44 (SNORD44, formerly RNU44). Experiments were done in triplicate, and an average was calculated. Fold change was expressed as a log(2) value. The top-scoring pairs classifier identified operational decision rules for distinguishing between different RCC subtypes and was robust under cross-validation.
We developed a classification system that can distinguish the different RCC subtypes using unique miRNA signatures in a maximum of four steps. The system has a sensitivity of 97% in distinguishing normal from RCC, 100% for clear cell RCC (ccRCC) subtype, 97% for papillary RCC (pRCC) subtype, and 100% accuracy in distinguishing oncocytoma from chromophobe RCC (chRCC) subtype. This system was cross-validated and showed an accuracy of about 90%. The oncogenesis of ccRCC is more closely related to pRCC, whereas chRCC is comparable with oncocytoma. We also developed a binary classification system that can distinguish between two individual subtypes.
MiRNA expression patterns can distinguish between RCC subtypes.
肾细胞癌(RCC)包括不同的组织学亚型。通常通过形态评估来区分这些亚型,但这种方法并不总是准确的。
我们的目的是确定可以准确区分不同 RCC 亚型的 microRNA(miRNA)特征。
设计、环境和参与者:共分析了 94 种不同的亚型病例。对三种常见 RCC 亚型(透明细胞型、嫌色细胞型和乳头状型)和嗜酸细胞瘤的新鲜冷冻组织进行 miRNA 微阵列分析。使用 miRNA 特异性引物的定量逆转录聚合酶链反应(qRT-PCR)分析在原始和独立的肿瘤组上验证结果。
通过标准方法分析微阵列数据。使用 ΔΔC(T)方法确定 qRT-PCR 的相对表达,并用小核仁 RNA、C/D 框 44(SNORD44,以前称为 RNU44)对表达值进行标准化。实验一式三份进行,并计算平均值。倍数变化表示为 log(2) 值。得分最高的对分类器确定了用于区分不同 RCC 亚型的操作决策规则,并且在交叉验证下具有稳健性。
我们开发了一种分类系统,该系统可以使用不同 RCC 亚型的独特 miRNA 特征在最多四个步骤中进行区分。该系统在区分正常与 RCC 时的灵敏度为 97%,在区分透明细胞 RCC(ccRCC)亚型时的灵敏度为 100%,在区分乳头状 RCC(pRCC)亚型时的灵敏度为 97%,在区分嗜酸细胞瘤与嫌色细胞 RCC(chRCC)亚型时的灵敏度为 100%。该系统经过交叉验证,准确率约为 90%。ccRCC 的发生与 pRCC 更为密切相关,而 chRCC 与嗜酸细胞瘤相似。我们还开发了一种可以区分两种特定亚型的二分类系统。
miRNA 表达模式可以区分 RCC 亚型。