Accurate Molecular Classification of Kidney Cancer Subtypes Using MicroRNA Signature

被引:196
|
作者
Youssef, Youssef M. [1 ,2 ,3 ]
White, Nicole M. A. [1 ,2 ,3 ]
Grigull, Joerg [4 ]
Krizova, Adriana [1 ,2 ,3 ]
Samy, Christina [1 ,2 ,3 ]
Mejia-Guerrero, Salvador [1 ,2 ,3 ]
Evans, Andrew [3 ,5 ]
Yousef, George M. [1 ,2 ,3 ]
机构
[1] St Michaels Hosp, Dept Lab Med, Toronto, ON M5B 1W8, Canada
[2] St Michaels Hosp, Keenan Res Ctr, Li Ka Shing Knowledge Inst, Toronto, ON M5B 1W8, Canada
[3] Univ Toronto, Dept Lab Med & Pathobiol, Toronto, ON, Canada
[4] York Univ, Dept Math & Stat, Toronto, ON M3J 2R7, Canada
[5] Toronto Gen Hosp, Dept Pathol, Toronto, ON, Canada
关键词
Biomarker; Cancer; Clear cell renal cell carcinoma; Chromophobe; Diagnosis; Kidney cancer; MicroRNA; miRNA; Oncocytoma; Papillary; Pathology; Profiling; Statistical classifier; Prognosis; RCC; Renal cancer; Subtypes; Tumour markers; Unclassified; RENAL-CELL CARCINOMA; EXPRESSION PROFILES; TUMORS; PATHOGENESIS; TARGETS; MIRNAS;
D O I
10.1016/j.eururo.2011.01.004
中图分类号
R5 [内科学]; R69 [泌尿科学(泌尿生殖系疾病)];
学科分类号
1002 ; 100201 ;
摘要
Background: Renal cell carcinoma (RCC) encompasses different histologic subtypes. Distinguishing between the subtypes is usually made by morphologic assessment, which is not always accurate. Objective: 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. Measurements: Microarray data were analysed by standard approaches. Relative expression for qRT-PCR was determined using the DDCT 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. Results and limitations: 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. Conclusions: MiRNA expression patterns can distinguish between RCC subtypes. (C) 2011 European Association of Urology. Published by Elsevier B.V. All rights reserved.
引用
收藏
页码:721 / 730
页数:10
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