Dataset Information

MicroRNA expression profiling to identify and validate reference genes for relative quantification in colorectal cancer.

ABSTRACT: 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.

SUBMITTER: Chang KH

PROVIDER: S-EPMC2873395 | biostudies-literature | 2010

REPOSITORIES: biostudies-literature

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MicroRNA expression profiling to identify and validate reference genes for relative quantification in colorectal cancer.

Chang Kah Hoong KH Mestdagh Pieter P Vandesompele Jo J Kerin Michael J MJ Miller Nicola N

BMC cancer 20100429

<h4>Background</h4>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 hav ...[more]

PMID: 20429937

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Project description:IntroductionMicroRNAs (miRNAs) play important roles in regulating biological processes at the post-transcriptional level. Deregulation of miRNAs has been observed in cancer, and miRNAs are being investigated as potential biomarkers regarding diagnosis, prognosis and prediction in cancer management. Real-time quantitative polymerase chain reaction (RT-qPCR) is commonly used, when measuring miRNA expression. Appropriate normalisation of RT-qPCR data is important to ensure reliable results. The aim of the present study was to identify stably expressed miRNAs applicable as normaliser candidates in future studies of miRNA expression in rectal cancer.Materials and methodsWe performed high-throughput miRNA profiling (OpenArray®) on ten pairs of laser micro-dissected rectal cancer tissue and adjacent stroma. A global mean expression normalisation strategy was applied to identify the most stably expressed miRNAs for subsequent validation. In the first validation experiment, a panel of miRNAs were analysed on 25 pairs of micro dissected rectal cancer tissue and adjacent stroma. Subsequently, the same miRNAs were analysed in 28 pairs of rectal cancer tissue and normal rectal mucosa.ResultsFrom the miRNA profiling experiment, miR-645, miR-193a-5p, miR-27a and let-7g were identified as stably expressed, both in malignant and stromal tissue. In addition, NormFinder confirmed high expression stability for the four miRNAs. In the RT-qPCR based validation experiments, no significant difference between tumour and stroma/normal rectal mucosa was detected for the mean of the normaliser candidates miR-27a, miR-193a-5p and let-7g (first validation P = 0.801, second validation P = 0.321). MiR-645 was excluded from the data analysis, because it was undetected in 35 of 50 samples (first validation) and in 24 of 56 samples (second validation), respectively. Significant difference in expression level of RNU6B was observed between tumour and adjacent stromal (first validation), and between tumour and normal rectal mucosa (second validation).ConclusionWe recommend the mean expression of miR-27a, miR-193a-5p and let-7g as normalisation factor, when performing miRNA expression analyses by RT-qPCR on rectal cancer tissue.

Project description:BACKGROUND:An important consideration when analyzing both microarray and quantitative PCR expression data is the selection of appropriate genes as endogenous controls or reference genes. This step is especially critical when identifying genes differentially expressed between datasets. Moreover, reference genes suitable in one context (e.g. lung cancer) may not be suitable in another (e.g. breast cancer). Currently, the main approach to identify reference genes involves the mining of expression microarray data for highly expressed and relatively constant transcripts across a sample set. A caveat here is the requirement for transcript normalization prior to analysis, and measurements obtained are relative, not absolute. Alternatively, as sequencing-based technologies provide digital quantitative output, absolute quantification ensues, and reference gene identification becomes more accurate. METHODS:Serial analysis of gene expression (SAGE) profiles of non-malignant and malignant lung samples were compared using a permutation test to identify the most stably expressed genes across all samples. Subsequently, the specificity of the reference genes was evaluated across multiple tissue types, their constancy of expression was assessed using quantitative RT-PCR (qPCR), and their impact on differential expression analysis of microarray data was evaluated. RESULTS:We show that (i) conventional references genes such as ACTB and GAPDH are highly variable between cancerous and non-cancerous samples, (ii) reference genes identified for lung cancer do not perform well for other cancer types (breast and brain), (iii) reference genes identified through SAGE show low variability using qPCR in a different cohort of samples, and (iv) normalization of a lung cancer gene expression microarray dataset with or without our reference genes, yields different results for differential gene expression and subsequent analyses. Specifically, key established pathways in lung cancer exhibit higher statistical significance using a dataset normalized with our reference genes relative to normalization without using our reference genes. CONCLUSIONS:Our analyses found NDUFA1, RPL19, RAB5C, and RPS18 to occupy the top ranking positions among 15 suitable reference genes optimal for normalization of lung tissue expression data. Significantly, the approach used in this study can be applied to data generated using new generation sequencing platforms for the identification of reference genes optimal within diverse contexts.

Dataset Information

MicroRNA expression profiling to identify and validate reference genes for relative quantification in colorectal cancer.

Publications

MicroRNA expression profiling to identify and validate reference genes for relative quantification in colorectal cancer.

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