Identification of suitable reference genes for gene expression studies using quantitative polymerase chain reaction in lung cancer in vitro

Mol Med Rep. 2015 May;11(5):3767-73. doi: 10.3892/mmr.2015.3159. Epub 2015 Jan 8.

Abstract

The present study aimed to examine 10 housekeeping genes (HKGs), including 18s ribosomal RNA (18S), glyceraldehyde‑3‑phosphate dehydrogenase (GAPDH), ribosomal protein large P0 (RPLP0), β‑actin (ACTB), peptidylprolyl isomerase A (PPIA), phosphoglycerate kinase‑1 (PGK1), β‑2‑microglobulin (B2M), ribosomal protein LI3a (RPL13A), hypoxanthine phosphoribosyl transferase‑1 (HPRT1) and TATA box binding protein (TBP) in order to identify the most stable and suitable reference genes for use in expression studies in non‑small cell lung cancer. The mRNA expression encoding the panel of the 10 HKGs was determined using reverse transcription‑quantitative PCR (RT‑qPCR) in human lung cancer cell lines. Three software programs, BestKeeper, NormFinder and geNorm, were used to ascertain the most suitable reference genes to normalize the RNA input. The present study examined three lung cancer cell lines (A549, NCI‑H446 and NCI‑H460). The analysis of the experimental data using BestKeeper software revealed that all 10 HKGs were stable, with GADPH, followed by 18S being the most stable genes and PPIA and HPRT1 being the least stable genes. The NormFinder software results demonstrated that PPIA followed by ACTB were the most stable and B2M and RPLP0 were the least stable. The geNorm software results revealed that ACTB and PGK1, followed by PPIA were the most stable genes and B2M and RPLP0 were identified as the least stable genes. Due to discrepancies in the ranking orders of the reference genes obtained by different analyzing software programs, it was not possible to determine a single universal reference gene. The suitability of selected reference genes requires unconditional validation prior to each study. Based on the three analyzing programs, ACTB, PPIA and PGK1 were the most stable reference genes in lung cancer cell lines.

Publication types

  • Research Support, Non-U.S. Gov't

MeSH terms

  • Gene Expression Profiling*
  • Gene Expression Regulation, Neoplastic
  • Genes, Essential
  • Genetic Association Studies*
  • Humans
  • Lung Neoplasms / genetics*
  • Models, Statistical
  • Real-Time Polymerase Chain Reaction