Abstract
BackgroundGene 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.ResultsThe 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.ConclusionThis 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.
Highlights
Gene expression analysis has many applications in cancer diagnosis, prognosis and therapeutic care
To accurately quantify an mRNA target by real-time quantitative (RQ-)PCR, samples are assayed during the exponential phase of the PCR reaction during which the amount of target is assumed to double with each cycle of PCR without bias due to limiting reagents
Range of Expression of Candidate expressed control (EC) Genes A range of cycle threshold (Ct) values was observed across the candidate EC genes in tumour and tumour associated normal (TAN) tissue from Colorectal cancer (CRC) patients as indicated in table 1
Summary
Gene expression analysis has many applications in cancer diagnosis, prognosis and therapeutic care. Analysis of cycle threshold (Ct), the cycle number at which signals are detected above background, can be used to estimate gene expression levels by relating Ct values either to a standard curve (absolute quantification) or to a control gene (relative quantification) The latter method requires the generation of standard curves of known copy number for each target and so is limited due to logistical issues associated with the generation of standards in studies of multiple gene targets. Vandestompele et al 2002 described a normalisation method whereby geometrical averaging of multiple EC genes improved accuracy [8] This approach has been adopted to reliably measure levels of gene expression in many studies in different tissue types including breast [9,10,11], lung [12], kidney [13], brain [14] and liver [15]
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
