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Zwijnaarde, Belgium

Chang K.H.,National University of Ireland | Mestdagh P.,Ghent University | Vandesompele J.,Ghent University | Vandesompele J.,Biogazelle | And 2 more authors.
BMC Cancer | Year: 2010

Background: Advances in high-throughput technologies and bioinformatics have transformed gene expression profiling methodologies. The results of microarray experiments are often validated using reverse transcription quantitative PCR (RT-qPCR), which is the most sensitive and reproducible method to quantify gene expression. Appropriate normalisation of RT-qPCR data using stably expressed reference genes is critical to ensure accurate and reliable results. Mi(cro)RNA expression profiles have been shown to be more accurate in disease classification than mRNA expression profiles. However, few reports detailed a robust identification and validation strategy for suitable reference genes for normalisation in miRNA RT-qPCR studies.Methods: We adopt and report a systematic approach to identify the most stable reference genes for miRNA expression studies by RT-qPCR in colorectal cancer (CRC). High-throughput miRNA profiling was performed on ten pairs of CRC and normal tissues. By using the mean expression value of all expressed miRNAs, we identified the most stable candidate reference genes for subsequent validation. As such the stability of a panel of miRNAs was examined on 35 tumour and 39 normal tissues. The effects of normalisers on the relative quantity of established oncogenic (miR-21 and miR-31) and tumour suppressor (miR-143 and miR-145) target miRNAs were assessed.Results: In the array experiment, miR-26a, miR-345, miR-425 and miR-454 were identified as having expression profiles closest to the global mean. From a panel of six miRNAs (let-7a, miR-16, miR-26a, miR-345, miR-425 and miR-454) and two small nucleolar RNA genes (RNU48 and Z30), miR-16 and miR-345 were identified as the most stably expressed reference genes. The combined use of miR-16 and miR-345 to normalise expression data enabled detection of a significant dysregulation of all four target miRNAs between tumour and normal colorectal tissue.Conclusions: Our study demonstrates that the top six most stably expressed miRNAs (let-7a, miR-16, miR-26a, miR-345, miR-425 and miR-454) described herein should be validated as suitable reference genes in both high-throughput and lower throughput RT-qPCR colorectal miRNA studies. © 2010 Chang et al; licensee BioMed Central Ltd. Source


Formosa A.,University of Rome Tor Vergata | Lena A.M.,University of Rome Tor Vergata | Markert E.K.,Institute for Advanced Study | Cortelli S.,University of Rome Tor Vergata | And 12 more authors.
Oncogene | Year: 2013

Silencing of microRNAs (miRNAs) by promoter CpG island methylation may be an important mechanism in prostate carcinogenesis. To screen for epigenetically silenced miRNAs in prostate cancer (PCa), we treated prostate normal epithelial and carcinoma cells with 5-aza-2′-deoxycytidine (AZA) and subsequently examined expression changes of 650 miRNAs by megaplex stemloop reverse transcription-quantitative PCR. After applying a selection strategy, we analyzed the methylation status of CpG islands upstream to a subset of miRNAs by methylation-specific PCR. The CpG islands of miR-18b, miR-132, miR-34b/c, miR-148a, miR-450a and miR-542-3p showed methylation patterns congruent with their expression modulations in response to AZA. Methylation analysis of these CpG islands in a panel of 50 human prostate carcinoma specimens and 24 normal controls revealed miR-132 to be methylated in 42% of human cancer cases in a manner positively correlated to total Gleason score and tumor stage. Expression analysis of miR-132 in our tissue panel confirmed its downregulation in methylated tumors. Re-expression of miR-132 in PC3 cells induced cell detachment followed by cell death (anoikis). Two pro-survival proteins - heparin-binding epidermal growth factor and TALIN2 - were confirmed as direct targets of miR-132. The results of this study point to miR-132 as a methylation-silenced miRNA with an antimetastatic role in PCa controlling cellular adhesion. © 2013 Macmillan Publishers Limited. Source


Grant
Agency: Cordis | Branch: FP7 | Program: CP-IP | Phase: HEALTH-2009-2.4.5-2 | Award Amount: 15.74M | Year: 2010

Chronic kidney disease (CKD) affects up to 10% of the population. Besides eventual progression towards end stage renal disease CKD impacts the patients quality of life by causing serious comorbidities including cardiovascular complications and bone metabolism disorders. On the everyday clinical level early stage diagnosis and tailored treatment of CKD are still inadequate. In addition, CKD seems not to have reached its appropriate emplacement in an epidemiological and healthcare perspective yet, and the pathophysiology of the disease on a molecular and cellular level is not well enough understood. Our sysKID consortium was installed for precisely addressing these issues: To unravel the molecular and cellular mechanisms of chronic kidney disease development, combine this information with clinical risk factors, and on this basis delineate chronic kidney disease biomarkers. These markers will allow us to perform preclinical studies of novel therapy approaches for halting disease progression, and will provide us with the materials for development and clinical evaluation of tools for early stage diagnosis as well as prognosis and treatment monitoring. sysKID assures a successful implementation of these goals by a truly international consortium of 27 leading research groups. We combine clinical know how, provide access to a huge chronic kidney disease sample and clinical data pool, and build a Systems Biology framework for chronic kidney disease by integrating molecular and cellular biology, computational biology, statistics and epidemiology. Our expert group is further complemented by a high level advisory board covering science, product development, and the patients perspective. sysKID implementation is structured for completing pre-clinical Proof of Concept studies of novel chronic kidney disease therapy regimes, and further for completing clinical evaluation of an epidemiological screening tool as well as of early stage chronic kidney disease diagnostic kits.


Pfaffl M.W.,TU Munich | Zhao S.,University of California at Berkeley | Spiess A.N.,University of Hamburg | Boggy G.,Dna Software, Inc. | And 10 more authors.
Methods | Year: 2013

RNA transcripts such as mRNA or microRNA are frequently used as biomarkers to determine disease state or response to therapy. Reverse transcription (RT) in combination with quantitative PCR (qPCR) has become the method of choice to quantify small amounts of such RNA molecules. In parallel with the democratization of RT-qPCR and its increasing use in biomedical research or biomarker discovery, we witnessed a growth in the number of gene expression data analysis methods. Most of these methods are based on the principle that the position of the amplification curve with respect to the cycle-axis is a measure for the initial target quantity: the later the curve, the lower the target quantity. However, most methods differ in the mathematical algorithms used to determine this position, as well as in the way the efficiency of the PCR reaction (the fold increase of product per cycle) is determined and applied in the calculations. Moreover, there is dispute about whether the PCR efficiency is constant or continuously decreasing. Together this has lead to the development of different methods to analyze amplification curves. In published comparisons of these methods, available algorithms were typically applied in a restricted or outdated way, which does not do them justice. Therefore, we aimed at development of a framework for robust and unbiased assessment of curve analysis performance whereby various publicly available curve analysis methods were thoroughly compared using a previously published large clinical data set (Vermeulen et al., 2009) [11]. The original developers of these methods applied their algorithms and are co-author on this study. We assessed the curve analysis methods' impact on transcriptional biomarker identification in terms of expression level, statistical significance, and patient-classification accuracy. The concentration series per gene, together with data sets from unpublished technical performance experiments, were analyzed in order to assess the algorithms' precision, bias, and resolution. While large differences exist between methods when considering the technical performance experiments, most methods perform relatively well on the biomarker data. The data and the analysis results per method are made available to serve as benchmark for further development and evaluation of qPCR curve analysis methods (http://qPCRDataMethods.hfrc.nl). © 2012 Elsevier Inc. Source


D'haene B.,Biogazelle | Mestdagh P.,Ghent University | Hellemans J.,Biogazelle | Vandesompele J.,Ghent University
Methods in Molecular Biology | Year: 2012

MicroRNAs (miRNAs) are an important class of gene regulators, acting on several aspects of cellular function such as differentiation, cell cycle control, and stemness. These master regulators constitute an invaluable source of biomarkers, and several miRNA signatures correlating with patient diagnosis, prognosis, and response to treatment have been identified. Within this exciting field of research, whole-genome RT-qPCR-based miRNA profiling in combination with a global mean normalization strategy has proven to be the most sensitive and accurate approach for high-throughput miRNA profiling (Mestdagh et al., Genome Biol 10:R64, 2009). In this chapter, we summarize the power of the previously described global mean normalization method in comparison to the multiple reference gene normalization method using the most stably expressed small RNA controls. In addition, we compare the original global mean method to a modified global mean normalization strategy based on the attribution of equal weight to each individual miRNA during normalization. This modified algorithm is implemented in Biogazelle's qbasePLUS software and is presented here for the first time. © 2012 Springer Science+Business Media, LLC. Source

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