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. 2010 Feb 1:11:12.
doi: 10.1186/1471-2199-11-12.

Identification of endogenous control genes for normalisation of real-time quantitative PCR data in colorectal cancer

Elrasheid A H Kheirelseid  1 , Kah Hoong Chang, John Newell, Michael J Kerin, Nicola Miller
Affiliations

Identification of endogenous control genes for normalisation of real-time quantitative PCR data in colorectal cancer

Elrasheid A H Kheirelseid et al. BMC Mol Biol. .

Abstract

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.

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Figures

Figure 1
Figure 1
Scaled expression levels and variation of each candidate EC gene. (A) Log 10 of cycle threshold of candidate EC genes ACTB, B2M, GAPDH, HPRT, MRPL19, SLC25A23, DTX3, CHRNB4 and PPIA in tumour and normal colorectal tissues. Boxplot shows interquartile range box, median, range whiskers and outliers (*). Within gene, differences were found in expression between tissue groups in both SLC25A23 (p = 0.040) and CHRNB4 (p = 0.002) but not the other genes (p > 0.05) (ANOVA). (B) Variation associated with EC gene expression. There was a significant difference in variation associated with gene expression (p < 0.001) with ACTB, GAPDH and HPRT showing greater variation than B2M, MRPL19 or PPIA. DTX3, CHRNB4 and SLC25A23 showed the least variations (Levene's test).
Figure 2
Figure 2
Analysis of candidate EC genes using geNorm. (A): Average expression stability values of eligible EC genes. Expression stability of the control genes as calculated by geNorm. Stability value M is based on the average pair-wise variation between all genes. The least stable gene with highest M value was excluded and M value recalculated till end up with the most stable pair. (B): Determination of optimal number of control genes for normalisation. The GeNorm programme calculates a normalisation factor (NF) which is used to determine the optimal number of EC genes required for accurate normalisation. This factor is calculated using the variable V as the pairwise variation (Vn/Vn + 1) between two sequential NFs (NFn and NFn + 1). To meet the recommended cut off V-value which is the point at which it is unnecessary to include additional genes in a normalisation strategy. The recommended limit for V value is 0.15 but it is not always achievable. In this instance, the GeNorm output file indicated that the optimal number of genes required for normalisation was three.
Figure 3
Figure 3
Relative quantity of CXCL12, FABP1, MUC2 and PDCD4 in colorectal tissue. Error bars indicate 95% confidence intervals. No significant differences in the relative quantities of target genes were found using a combination of PPIA and B2M (PB) genes in comparison to the use of combination of PPIA, B2M and MRPL19 (PBM) EC genes (ANOVA).
Figure 4
Figure 4
Equivalence test for candidate control genes in colorectal tissue. Differences in logarithmic expression levels between tumour and normal tissues (●) are indicated. The upper and lower bars of each line indicate the upper and lower limits of the symmetrical confidence intervals, respectively. The deviation area (-1, 1) for a fold change of 2 or less is plotted as a continuous line while the deviation area of (-1.58, 1.58) for a fold change of 3 is plotted as a dotted line.
Figure 5
Figure 5
Relative quantity of target gene expression in colorectal tissues relative to each EC gene and to the geometric mean of the combined use of PPIA and B2M (PB). (A) Target gene expression in tumour versus normal using either individual candidate EC genes or the PB combination. (B) Significant differences in relative gene expression values as determined using ANOVA to compare mean expression levels across all tissues using either individual EC genes or PB in combination. (C) One way ANOVA indicating a reduction in the magnitude of error when the PB combination was used to normalise expression of CXCL12 (p < 0.001) and PDCD4 (p < 0.001) in comparison to the use of individual EC genes. See Table 1 Additional files for Post Hoc tests. Error bars indicate 95% confidence intervals.
Figure 6
Figure 6
Non-normalised cycle threshold (Ct) of CXCL12, FABP1, MUC2 and PDCD4 in colorectal tissue. Using this approach, the expression of each gene appears to be down-regulated in tumours compared to normal tissues in the large cohort of patients (30 tumour and 34 normal tissue specimens), similar to previous published reports of reduced expression in colorectal tumours. No significant differences were noted in expression levels of target genes when using the small cohort of patients (10 tumour and 10 normal tissue specimens) (2-sample t-test). This confirms the effect of sample size on findings when using non-normalised Ct values and therefore the importance of normalisation especially in such type of studies
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