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 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. 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.
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 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. 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.
Authors: Holly Lewis; Raymond Lance; Dean Troyer; Hind Beydoun; Melissa Hadley; Joseph Orians; Tiffany Benzine; Kenya Madric; O John Semmes; Richard Drake; Aurora Esquela-Kerscher Journal: Cell Cycle Date: 2013-11-07 Impact factor: 4.534
Authors: Heba W Z Khella; Nicole M A White; Hala Faragalla; Manal Gabril; Mina Boazak; David Dorian; Bishoy Khalil; Hany Antonios; Tian Tian Bao; Maria D Pasic; R John Honey; Robert Stewart; Kenneth T Pace; Georg A Bjarnason; Michael A S Jewett; George M Yousef Journal: Tumour Biol Date: 2011-11-16
Authors: Yulian Mytsyk; Victor Dosenko; Yuriy Borys; Askold Kucher; Katarina Gazdikova; Dietrich Busselberg; Martin Caprnda; Peter Kruzliak; Ammad Ahmad Farooqi; Manyuk Lubov Journal: Int Urol Nephrol Date: 2018-03-16 Impact factor: 2.370
Authors: Samantha Jane Wala; Jason Raj Karamchandani; Rola Saleeb; Andrew Evans; Qiang Ding; Rania Ibrahim; Michael Jewett; Maria Pasic; Antonio Finelli; Kenneth Pace; Evi Lianidou; George Makram Yousef Journal: Mol Oncol Date: 2015-05-14 Impact factor: 6.603