Genome-Wide MicroRNA Expression Analysis of Clear Cell Renal Cell Carcinoma by Next Generation Deep Sequencing

Department of Clinical Oncology, Leiden University Medical Center, Leiden, The Netherlands.
PLoS ONE (Impact Factor: 3.23). 06/2012; 7(6):e38298. DOI: 10.1371/journal.pone.0038298
Source: PubMed

ABSTRACT MicroRNAs (miRNAs), non-coding RNAs regulating gene expression, are frequently aberrantly expressed in human cancers. Next-generation deep sequencing technology enables genome-wide expression profiling of known miRNAs and discovery of novel miRNAs at unprecedented quantitative and qualitative accuracy. Deep sequencing was performed on 11 fresh frozen clear cell renal cell carcinoma (ccRCC) and adjacent non-tumoral renal cortex (NRC) pairs, 11 additional frozen ccRCC tissues, and 2 ccRCC cell lines (n = 35). The 22 ccRCCs patients belonged to 3 prognostic sub-groups, i.e. those without disease recurrence, with recurrence and with metastatic disease at diagnosis. Thirty-two consecutive samples (16 ccRCC/NRC pairs) were used for stem-loop PCR validation. Novel miRNAs were predicted using 2 distinct bioinformatic pipelines. In total, 463 known miRNAs (expression frequency 1-150,000/million) were identified. We found that 100 miRNA were significantly differentially expressed between ccRCC and NRC. Differential expression of 5 miRNAs was confirmed by stem-loop PCR in the 32 ccRCC/NRC samples. With respect to RCC subgroups, 5 miRNAs discriminated between non-recurrent versus recurrent and metastatic disease, whereas 12 uniquely distinguished non-recurrent versus metastatic disease. Blocking overexpressed miR-210 or miR-27a in cell line SKCR-7 by transfecting specific antagomirs did not result in significant changes in proliferation or apoptosis. Twenty-three previously unknown miRNAs were predicted in silico. Quantitative genome-wide miRNA profiling accurately separated ccRCC from (benign) NRC. Individual differentially expressed miRNAs may potentially serve as diagnostic or prognostic markers or future therapeutic targets in ccRCC. The biological relevance of candidate novel miRNAs is unknown at present.

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Available from: Jelle J Goeman, Aug 27, 2015
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    • "Several microarray based studies have demonstrated 21 to 34 differentially expressed miRNAs between ccRCC and normal kidney tissue [11]. SRNA-Seq studies reported more than 100 differentially regulated miRNAs, some of which might serve as diagnostic and prognostic markers [12] [13]. Nevertheless, these studies lack detailed information about miRNA targets and bioinformatical analysis is often only focused on miRNAs currently known to miRbase. "
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    ABSTRACT: Altered microRNA (miRNA) expression is a hallmark of many cancer types. The combined analysis of miRNA and messenger RNA (mRNA) expression profiles is crucial to identifying links between deregulated miRNAs and oncogenic pathways. Therefore, we investigated the small non-coding (snc) transcriptomes of nine clear cell renal cell carcinomas (ccRCCs) and adjacent normal tissues for alterations in miRNA expression using a publicly available small RNA-Sequencing (sRNA-Seq) raw-dataset. We constructed a network of deregulated miRNAs and a set of differentially expressed genes publicly available from an independent study to in silico determine miRNAs that contribute to clear cell renal cell carcinogenesis. From a total of 1,672 sncRNAs, 61 were differentially expressed across all ccRCC tissue samples. Several with known implications in ccRCC development, like the upregulated miR-21-5p, miR-142-5p, as well as the downregulated miR-106a-5p, miR-135a-5p, or miR-206. Additionally, novel promising candidates like miR-3065, which i.a. targets NRP2 and FLT1, were detected in this study. Interaction network analysis revealed pivotal roles for miR-106a-5p, whose loss might contribute to the upregulation of 49 target mRNAs, miR-135a-5p (32 targets), miR-206 (28 targets), miR-363-3p (22 targets), and miR-216b (13 targets). Among these targets are the angiogenesis, metastasis, and motility promoting oncogenes c-MET, VEGFA, NRP2, and FLT1, the latter two coding for VEGFA receptors.
    BioMed Research International 05/2014; 2014. DOI:10.1155/2014/948408 · 2.71 Impact Factor
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    • "or oncogenes (Lu et al., 2005). Recently numerous studies have shown aberrant expression of microRNA-122-5p (miR-122) in human cancer tissues, including RCC (Zhou et al., 2010; Heinzelmann et al., 2011; Osanto et al., 2012), suggesting that it is a candidate tumor activator in RCC. miR-122 is one of the most frequent miRNA isolated in the liver and plays important roles in many aspects of liver physiology, such as stress response (Bhattacharyya et al., 2006) and lipid metabolism (Esau et al., 2006). "
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    ABSTRACT: Objective: MicroRNAs (miRNAs) are a small class of non-coding, single-stranded RNAs with a critical role in genesis and maintenance of renal cancer mainly through binding to 3'-untranslated regions (3'UTR) of target mRNAs, which causes a block of translation and/or mRNA degradation. The aim of the present study was to investigate the potential effects of miR-122 in human renal cell carcinomas. Methods: The expression level of miR-122 was quantified by qRT-PCR. MTT, colony formation, invasion and migration assays were used to explore the potential functions of miR-122 in human renal cell carcinoma cells. Results: Cellular growth, invasion and migration in two A498 and 786-O cells were significantly increased after miR-122 transfection. Further experiments demonstrated that overexpression of miR-122 resulted in the increase of phospho-Akt (Ser473) and phospho-mTOR (Ser2448), then activation of mTOR targets, p70S6K and 4E-BP1. Conclusions: The up-regulation of miR-122 may play an important role in the progress of renal cancer through activating PI3K/Akt signal pathway and could be a potential molecular target for anti-cancer therapeutics.
    Asian Pacific journal of cancer prevention: APJCP 09/2013; 14(9):5017-21. DOI:10.7314/APJCP.2013.14.9.5017 · 2.51 Impact Factor
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    • "Instead of ligating a barcode directly to the miRNA and introducing bias through ligation efficiency, the same adaptor sequence is ligated to all of the samples and the individual barcode is introduced by PCR, resulting in virtually no bias (Post Amplification Ligation-Mediated multiplexing; Van Nieuwerburgh et al., 2011). The technique accommodates many different sample types (Osanto et al., 2012; Semenov et al., 2012; Wang et al., 2012), making NGS of miRNAs increasingly more accessible, cost efficient, and quantitative. As the protocols for miRNA library preparation for deep sequencing have improved, the real challenge has become how to appropriately adapt, and integrate better analysis tools. "
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    ABSTRACT: Recent advances in sample preparation and analysis for next generation sequencing have made it possible to profile and discover new miRNAs in a high throughput manner. In the case of neurological disease and injury, these types of experiments have been more limited. Possibly because tissues such as the brain and spinal cord are inaccessible for direct sampling in living patients, and indirect sampling of blood and cerebrospinal fluid are affected by low amounts of RNA. We used a mouse model to examine changes in miRNA expression in response to acute nerve crush. We assayed miRNA from both muscle tissue and blood plasma. We examined how the depth of coverage (the number of mapped reads) changed the number of detectable miRNAs in each sample type. We also found that samples with very low starting amounts of RNA (mouse plasma) made high depth of mature miRNA coverage more difficult to obtain. Each tissue must be assessed independently for the depth of coverage required to adequately power detection of differential expression, weighed against the cost of sequencing that sample to the adequate depth. We explored the changes in total mapped reads and differential expression results generated by three different software packages: miRDeep2, miRNAKey, and miRExpress and two different analysis packages, DESeq and EdgeR. We also examine the accuracy of using miRDeep2 to predict novel miRNAs and subsequently detect them in the samples using qRT-PCR.
    Frontiers in Genetics 03/2013; 4:20. DOI:10.3389/fgene.2013.00020
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