Selecting suitable reference genes for qPCR normalization : A comprehensive analysis in MCF-7 breast cancer cell line

Abstract

Background: MCF-7 breast cancer cell line is undoubtedly amongst the most extensively studied patient-derived research models, providing pivotal results that have over the decades translated to constantly improving patient care. Many research groups, have previously identified suitable reference genes for qPCR normalization in MCF-7 cell line. However, over the course of identification of suitable reference genes, a comparative analysis comprising these genes together in a single study has not been reported. Furthermore, the expression dynamics of these reference genes within sub-clones cultured over multiple passages (p) has attracted limited attention from research groups. Therefore, we investigated the expression dynamics of 12 previously suggested reference genes within two sub-clones (culture A1 and A2) cultured identically over multiple passages. Additionally, the effect of nutrient stress on reference gene expression was examined to postulate an evidence-based recommendation of the least variable reference genes that could be employed in future gene expression studies. Results: The analysis revealed the presence of differential reference gene expression within the sub-clones of MCF-7. In culture A1, GAPDH-CCSER2 were identified as the least variable reference genes while for culture A2, GAPDH-RNA28S were identified. However, upon validation using genes of interest, both these pairs were found to be unsuitable control pairs. Normalization of AURKA and KRT19 with triplet pair GAPDH-CCSER2-PCBP1 yielded successful results. The triplet also proved its capability to handle variations arising from nutrient stress. Conclusions: The variance in expression behavior amongst sub-clones highlights the potential need for exercising caution while selecting reference genes for MCF-7. GAPDH-CCSER2-PCBP1 triplet offers a reliable alternative to otherwise traditionally used internal controls for optimizing intra- and inter-assay gene expression differences. Furthermore, we suggest avoiding the use of ACTB, GAPDH and PGK1 as single internal controls.