Molecular classification of renal cell carcinoma subtypes using microRNA signatures

1. Molecular classification of renal cell carcinoma subtypes using microRNA signatures Zaravinos A 1, Lambrou GI 2, Mourmouras N 3, Delakas D 3, Deltas C 1 1Molecular Medicine Research Center and Laboratory of Molecular and Medical Genetics, Department of Biological Sciences, University of Cyprus, Kallipoleos 75, 1678, Nicosia, Cyprus. 2First Department of Pediatrics, Choremeio Research Laboratory, University of Athens, Athens 11527, Greece. 3Department of Urology, Asklipieio General Hospital, Athens, Greece. Background Renal cell carcinoma (RCC) is composed of various morphologically and cytogenetically distinct subtypes, the most prevalent of which are clear cell RCC (ccRCC, 75-80%), papillary RCC (papRCC, 10-15%) and chromophobe RCC (chRCC, 5%). Upper urinary tract urothelial cell carcinomas (UUT-UCCs) are uncommon (5-10% of urothelial carcinomas). Distinguishing between the subtypes is usually made by morphologic assessment, which is not always accurate. MicroRNAs (miRNAs) are small non-coding RNAs of approximately 22 nt size and modulate differentiation, growth, apoptosis and proliferation of cells. MiRNA expression profiles not only allow distinguishing malignant and non-malignant tissue, but also distinguishing different tumor entities. Our objective was to identify miRNA signatures that can distinguish the different RCC subtypes accurately. Methodology Total RNA extraction from FFPE kidney cancers: Sections were deparaffinized with xylene and ethanol washes, treated with protease and then total RNA containing small RNAs was isolated using the High Pure FFPE RNA Micro Kit (Roche Applied Science). The quality of the total RNA was verified by an Agilent 2100 Bioanalyzer and the concentration was measured on a Nanodrop spectrophotometer. Patient samples: All patients underwent radical tumour nephrectomy. Paraffin sections were reviewed by a pathologist and histological classified according to the WHO classification and staged according to the UICC-TNM classification (2002). Nuclear grade was scored according to the Fuhrman classification system. Immunohistochemistry: About 5-10 serial tissue sections of 10 μm were cut from each FFPE block and stained with H&E before microscopic examination. When the proportion of tumour cells was >70%, the FFPE block was subjected to RNA extraction. Immunohistochemistry of tissue sections was performed using an anti-vimentin antibody (Fig. 1). MicroRNA profiling: We used 19 ccRCC, 3 chRCC, 5 papRCC, 4 UUT-UCC and 20 normal tissue samples for miRNA profiling. Total RNA (0.5 µg) from each sample and reference was labeled with Hy3™ fluorescent label, using the miRCURY LNA™ microRNA Hi-Power Labeling Kit (Exiqon), following a two-step protocol. First, a Calf Intestinal Alkaline Phosphatase (CIP) was used to remove the 5’-phosphates from the terminal of the microRNAs and a fluorescent label was attached enzymatically to the 3’-end of the microRNAs in the total RNA sample, followed by an enzyme inactivation step. The Hy3™-labeled samples were hybridized to the miRCURY LNA™ microRNA Array, 6th gen (Exiqon). After hybridization, the microarray slides were scanned at 10 μm using the High-Resolution Microarray Scanner (Agilent Technologies). Image analysis was carried out using ImaGene 8.0. Data were filtered, background corrected and quantile normalized (Fig.2). MicroRNAs were considered to be significantly differentially expressed if they obtained a p-value<0.05 and a FDR≤0.05. Slide quality check was performed using spike-in controls. Fig. 1 Fig. 2 Reverse transcription (RT) and real-time PCR (qPCR): The microRNA in the total RNA sample (80 ng/ul) was converted to cDNA using the miRCURY LNA™ Universal RT cDNA synthesis kit (Exiqon). qPCR was performed in order to validate the microarray results of 28 of the top differentially expressed (DE) miRNAs. Fig. 3 Fig. 4 Results We identified 434 miRNAs that were significantly deregulated (DE) in all kidney tumours compared to the normal tissues (Fig. 3). A miRNA signature using unsupervised hierarchical clustering with Euclidian distance distinguished well normal tissues from kidney tumours, as well as the various subtypes among them (Fig. 4). A total of 126 miRNAs (29%) had increased expression while 303 (69.8%) had decreased expression in RCC. We detected 240, 5 and 33 deregulated miRNAs, which were specific for ccRCC, chRCC and papRCC, respectively (Fig. 5). miR-514b-3p was co-deregulated among chRCC, papRCC and UUT-UCC. Ten miRNAs (miR-10b-5p, miR-1257, miR-1303, miR-23c, miR-3171, miR-4270, miR-514b-3p, miR-515-5p, miR-620 and miR-98) were co-deregulated in all three subtypes, ccRCC, chRCC and papRCC. miR-199a-3p/miR-199b-3p, miR-3920, miR-4279, miR-514b-3p and miRPlus-J376c* were co-deregulated among ccRCC, papRCC and UUT-UCC. The qRT-PCR results were quite comparable with the microarray results (CC=0.778, p<0.001, ccRCC/normal; CC=0.590, p=0.002, papRCC/normal; CC=0.630, p<0.001, chRCC/normal; CC=0.521, p=0.009, UUT-UCC/normal; Pearson’s correlation) (Fig. 6). Fig. 6 Fig. 5 Discussion We present a molecular signature of the deregulated miRNAs in RCC that accurately classifies its most prevalent subtypes. This project is funded by the Cyprus Research Promotion Foundation, New Infrastructure/ΣΤΡΑΤΗ/0308/24 (co-funding by the EU Structural Funds)