Validation of suitable reference genes for quantitative polymerase chain reaction analysis in rabbit bone marrow mesenchymal stem cell differentiation.

Bone marrow mesenchymal stem cells (BMSCs) are considered as multipotent cells, representing a multi-lineage potential to differentiate into mesodermal lineages of mesenchymal tissues, including cartilage, bone, fat, muscle and tendon. Tissue engineering in BMSCs has made great advances in the regeneration of cartilage and bone defects. To uncover the mechanisms of the multipotent differentiation process, the molecular changes in gene expression profiles during chondrogenic and osteogenic differentiation need to be evaluated with reliable, accurate, fast and sensitive methods. Reverse transcription-quantitative polymerase chain reaction is a commonly used technology for analyzing gene expression, depending on an appropriate reference gene to normalize the errors. The commonly used reference genes vary, and no ideal and universal reference genes suitable for all conditions exist; therefore validation of the stability of gene expression is required. In the present study, three common statistical algorithms, geNorm, Normfinder and BestKeeper, were used to identify the expression stability of 12 genes, and the target differentiation markers during the differentiation of BMSCs were evaluated accurately. Our results demonstrated that YWHAZ, PPIA and GAPDH were suitable as reference genes for chondrogenic differentiation, while RPL13a allowed an efficient normalization expression value of interest genes for osteogenic differentiation of BMSCs. By contrast, the most unstable reference genes were 18s rRNA, B2M and HPRT1 in all studies, and these should be avoided when investigating the differentiation of BMSCs. Our results demonstrate validation of the appropriate reference genes for accurate gene expression in chondrogenic and osteogenic differentiation of BMSCs.

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

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.