Abstract
Selection of the right reference gene(s) is crucial in the analysis and interpretation of gene expression data. The aim of the present study was to discover and validate a minimal set of internal control genes in head and neck tumor studies. We analyzed data from multiple sources (in house whole-genome gene expression microarrays, previously published quantitative real-time PCR (qPCR) data and RNA-seq data from TCGA) to come up with a list of 18 genes (discovery set) that had the lowest variance, a high level of expression across tumors, and their matched normal samples. The genes in the discovery set were ranked using four different algorithms (BestKeeper, geNorm, NormFinder, and comparative delta Ct) and a web-based comparative tool, RefFinder, for their stability and variance in expression across tissues. Finally, we validated their expression using qPCR in an additional set of tumor:matched normal samples that resulted in five genes (RPL30, RPL27, PSMC5, MTCH1, and OAZ1), out of which RPL30 and RPL27 were most stable and were abundantly expressed across the tissues. Our data suggest that RPL30 or RPL27 in combination with either PSMC5 or MTCH1 or OAZ1 can be used as a minimal set of control genes in head and neck tumor gene expression studies.