title: Selection of suitable reference genes for accurate normalization of gene expression profile studies in non-small cell lung cancer.
abstract: BACKGROUND
  In real-time RT quantitative PCR (qPCR) the accuracy of normalized data is highly dependent on the reliability of the reference genes (RGs). Failure to use an appropriate control gene for normalization of qPCR data may result in biased gene expression profiles, as well as low precision, so that only gross changes in expression level are declared statistically significant or patterns of expression are erroneously characterized. Therefore, it is essential to determine whether potential RGs are appropriate for specific experimental purposes. Aim of this study was to identify and validate RGs for use in the differentiation of normal and tumor lung expression profiles.
  
  
  METHODS
  A meta-analysis of lung cancer transcription profiles generated with the GeneChip technology was used to identify five putative RGs. Their consistency and that of seven commonly used RGs was tested by using Taqman probes on 18 paired normal-tumor lung snap-frozen specimens obtained from non-small-cell lung cancer (NSCLC) patients during primary curative resection.
  
  
  RESULTS
  The 12 RGs displayed showed a wide range of Ct values: except for rRNA18S (mean 9.8), the mean values of all the commercial RGs and ESD ranged from 19 to 26, whereas those of the microarray-selected RGs (BTF-3, YAP1, HIST1H2BC, RPL30) exceeded 26. RG expression stability within sample populations and under the experimental conditions (tumour versus normal lung specimens) was evaluated by: (1) descriptive statistic; (2) equivalence test; (3) GeNorm applet. All these approaches indicated that the most stable RGs were POLR2A, rRNA18S, YAP1 and ESD.
  
  
  CONCLUSIONS
  These data suggest that POLR2A, rRNA18S, YAP1 and ESD are the most suitable RGs for gene expression profile studies in NSCLC. Furthermore, they highlight the limitations of commercial RGs and indicate that meta-data analysis of genome-wide transcription profiling studies may identify new RGs.
authors.affiliation: Department of Clinical and Biological Sciences, University of Torino, Regione Gonzole 10, 10043 Orbassano, Torino, Italy. silvia.saviozzi@unito.it
authors.forename: Silvia
authors.initials: S
authors.lastname: Saviozzi
authors.forename: Francesca
authors.initials: F
authors.lastname: Cordero
authors.forename: Marco
authors.initials: M
authors.lastname: Lo Iacono
authors.forename: Silvia
authors.initials: S
authors.lastname: Novello
authors.forename: Giorgio V
authors.initials: GV
authors.lastname: Scagliotti
authors.forename: Raffaele A
authors.initials: RA
authors.lastname: Calogero
mesh_terms: D002289:Carcinoma, Non-Small-Cell Lung / Q000235:genetics*; D018076:DNA, Complementary; D005260:Female; D020869:Gene Expression Profiling; D006801:Humans; D008168:Lung / Q000166:cytology / Q000502:physiology; D008175:Lung Neoplasms / Q000235:genetics*; D008297:Male; D020411:Oligonucleotide Array Sequence Analysis; D016133:Polymerase Chain Reaction; D012016:Reference Values; D014158:Transcription, Genetic
chemical_list: D018076:DNA, Complementary