Abstract

BackgroundReal-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.ResultsThe 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.ConclusionWe 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.

Highlights

  • 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

  • The aim of this study was to identify the most stable endogenous control genes from a panel of eleven candidates commonly used as endogenous controls in the context of, but not limited to, breast cancer: glyceraldehyde-3-phosphate dehydrogenase (GAPDH), TFRC, ABL, PPIA, HPRT1, RPLP0, B2M, GUSB, MRPL19, PUM1, PSMC4, for the quantification of gene expression by relative comparative RQ-PCR in primary breast cancer tissues

  • To identify suitable Endogenous control (EC) genes for breast cancer gene expression studies in fresh-frozen primary tissue, a panel of 11 genes commonly used as ECs was selected from the literature for analysis of stability: GAPDH, TFRC, ABL, PPIA, HPRT1, RPLP0, B2M, GUSB, MRPL19, PUM1 and PSMC4

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Summary

Introduction

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. 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. With the development of high throughput and reliable instrumentation, improved detection chemistries, more efficient protocols and appropriate analysis software, RQPCR has become the basis of many breast cancer biomarker studies as well as several novel diagnostic and prognostic assays [6,7,8,9,10,11,12]. RQ-PCR is used to validate microarray expression profiles and quantify genes of interest identified from those analyses

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