Abstract

Gene expression analysis has many applications in the management of cancer, including diagnosis, prognosis, and therapeutic care. In this context, the reverse transcription quantitative polymerase chain reaction (RT-qPCR) has become the "gold standard" for mRNA quantification. However, this technique involves several critical steps such as RNA extraction, cDNA synthesis, quantitative PCR, and analysis, which all can be source of variation. To obtain biologically meaningful results, data normalisation is required to correct sample-to-sample variations that may be introduced during this multistage process. Normalisation can be carried out against a housekeeping gene, total RNA mass, or cell number. Careful choice of the normalization method is crucial, as any variation in the reference will introduce errors in the quantification of mRNA transcripts. By reviewing the different methods available and their related problems, the aim of this article is to provide recommendations for the set up of an appropriate normalisation strategy for RT-qPCR data in oncology.