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1.  Identification of endogenous control genes for normalisation of real-time quantitative PCR data in colorectal cancer 
BMC Molecular Biology  2010;11:12.
Background
Gene expression analysis has many applications in cancer diagnosis, prognosis and therapeutic care. Relative quantification is the most widely adopted approach whereby quantification of gene expression is normalised relative to an endogenously expressed control (EC) gene. Central to the reliable determination of gene expression is the choice of control gene. The purpose of this study was to evaluate a panel of candidate EC genes from which to identify the most stably expressed gene(s) to normalise RQ-PCR data derived from primary colorectal cancer tissue.
Results
The expression of thirteen candidate EC genes: B2M, HPRT, GAPDH, ACTB, PPIA, HCRT, SLC25A23, DTX3, APOC4, RTDR1, KRTAP12-3, CHRNB4 and MRPL19 were analysed in a cohort of 64 colorectal tumours and tumour associated normal specimens. CXCL12, FABP1, MUC2 and PDCD4 genes were chosen as target genes against which a comparison of the effect of each EC gene on gene expression could be determined. Data analysis using descriptive statistics, geNorm, NormFinder and qBasePlus indicated significant difference in variances between candidate EC genes. We determined that two genes were required for optimal normalisation and identified B2M and PPIA as the most stably expressed and reliable EC genes.
Conclusion
This study identified that the combination of two EC genes (B2M and PPIA) more accurately normalised RQ-PCR data in colorectal tissue. Although these control genes might not be optimal for use in other cancer studies, the approach described herein could serve as a template for the identification of valid ECs in other cancer types.
doi:10.1186/1471-2199-11-12
PMCID: PMC2825202  PMID: 20122155
2.  Identification of suitable endogenous control genes for microRNA gene expression analysis in human breast cancer 
The discovery of microRNAs (miRNAs) added an extra level of intricacy to the already complex system regulating gene expression. These single-stranded RNA molecules, 18–25 nucleotides in length, negatively regulate gene expression through translational inhibition or mRNA cleavage. The discovery that aberrant expression of specific miRNAs contributes to human disease has fueled much interest in profiling the expression of these molecules. Real-time quantitative PCR (RQ-PCR) is a sensitive and reproducible gene expression quantitation technique which is now being used to profile miRNA expression in cells and tissues. To correct for systematic variables such as amount of starting template, RNA quality and enzymatic efficiencies, RQ-PCR data is commonly normalised to an endogenous control (EC) gene, which ideally, is stably-expressed across the test sample set. A universal endogenous control suitable for every tissue type, treatment and disease stage has not been identified and is unlikely to exist, so, to avoid introducing further error in the quantification of expression data it is necessary that candidate ECs be validated in the samples of interest. While ECs have been validated for quantification of mRNA expression in various experimental settings, to date there is no report of the validation of miRNA ECs for expression profiling in breast tissue. In this study, the expression of five miRNA genes (let-7a, miR-10b, miR-16, miR-21 and miR-26b) and three small nucleolar RNA genes (RNU19, RNU48 and Z30) was examined across malignant, benign and normal breast tissues to determine the most appropriate normalisation strategy. This is the first study to identify reliable ECs for analysis of miRNA by RQ-PCR in human breast tissue.
doi:10.1186/1471-2199-9-76
PMCID: PMC2533012  PMID: 18718003
3.  Evaluation and validation of candidate endogenous control genes for real-time quantitative PCR studies of breast cancer 
BMC Molecular Biology  2007;8:107.
Background
Real-time quantitative PCR (RQ-PCR) forms the basis of many breast cancer biomarker studies and novel prognostic assays, paving the way towards personalised cancer treatments. Normalisation of relative RQ-PCR data is required to control for non-biological variation introduced during sample preparation. Endogenous control (EC) genes, used in this context, should ideally be expressed constitutively and uniformly across treatments in all test samples. Despite widespread recognition that the accuracy of the normalised data is largely dependent on the reliability of the EC, there are no reports of the systematic validation of genes commonly used for this purpose in the analysis of gene expression by RQ-PCR in primary breast cancer tissues. The aim of this study was to identify the most suitable endogenous control genes for RQ-PCR analysis of primary breast tissue from a panel of eleven candidates in current use. Oestrogen receptor alpha (ESR1) was used a target gene to compare the effect of choice of EC on the estimate of gene quantity.
Results
The expression and validity of candidate ECs (GAPDH, TFRC, ABL, PPIA, HPRT1, RPLP0, B2M, GUSB, MRPL19, PUM1 and PSMC4) was determined in 6 benign and 21 malignant primary breast cancer tissues. Gene expression data was analysed using two different statistical models. MRPL19 and PPIA were identified as the most stable and reliable EC genes, while GUSB, RPLP0 and ABL were least stable. There was a highly significant difference in variance between ECs. ESR1 expression was appreciably higher in malignant compared to benign tissues and there was a significant effect of EC on the magnitude of the error associated with the relative quantity of ESR1.
Conclusion
We have validated two endogenous control genes, MRPL19 and PPIA, for RQ-PCR analysis of gene expression in primary breast tissue. Of the genes in current use in this field, the above combination offers increased accuracy and resolution in the quantitation of gene expression data, facilitating the detection of smaller changes in gene expression than otherwise possible. The combination identified here is a good candidate for use as a two-gene endogenous control in a broad spectrum of future research and diagnostic applications in breast cancer.
doi:10.1186/1471-2199-8-107
PMCID: PMC2211316  PMID: 18042273

Results 1-3 (3)