Women's Reproductive Health Laboratory of Zhejiang Province, Women's Hospital, School of Medicine, Zhejiang University, Hangzhou 310006, China.
Anal Biochem. 2010 Oct 15;405(2):224-9. doi: 10.1016/j.ab.2010.06.029. Epub 2010 Jun 19.
For quantitative real-time reverse transcription-polymerase chain reaction (RT-qPCR), the most commonly used normalization strategy is to select a stable reference gene. However, no suitable reference genes have been identified in cervical tissues to date. The aim of this study was to identify the most stable gene or a set of genes as reference genes for RT-qPCR analysis in cervical tissues from a panel of 12 candidates (ALAS1, PPIA, GAPDH, HBB, TBP, ACTIN, B2M, MBNL2, PGKL, RPLP0, RPL-4, and EEF1A1). In total, 20 normal and 20 cervical cancer specimens were examined. Gene expression data were analyzed using two different statistical models (geNorm and NormFinder). EEF1A1 was identified as the most stable and reliable reference gene, followed by GAPDH and RPLP0, whereas EEF1A1 and GAPDH were the best two-gene combination by NormFinder. The expression validity of EEF1A1 was further determined in 21 normal, 22 cervical intraepithelial neoplasia (CIN(2-3)), and 18 cancer tissues; no expression differences were found among normal, CIN(2-3), and cancer tissues (P>0.05). Our results suggested that EEF1A1 can be used as a reference gene for normalization in gene profiling studies in clinic cervical samples, and the combination of EEF1A1 and GAPDH could be recommended as a much more reliable normalization strategy.
对于定量实时逆转录聚合酶链反应 (RT-qPCR)，最常用的归一化策略是选择稳定的参考基因。然而，迄今为止，尚未在宫颈组织中鉴定出合适的参考基因。本研究旨在从 12 个候选基因（ALAS1、PPIA、GAPDH、HBB、TBP、ACTIN、B2M、MBNL2、PGKL、RPLP0、RPL-4 和 EEF1A1）中鉴定用于 RT-qPCR 分析的最稳定基因或一组基因作为参考基因。总共检查了 20 个正常和 20 个宫颈癌标本。使用两种不同的统计模型（geNorm 和 NormFinder）分析基因表达数据。结果发现 EEF1A1 是最稳定和可靠的参考基因，其次是 GAPDH 和 RPLP0，而 NormFinder 认为 EEF1A1 和 GAPDH 是最佳的双基因组合。进一步在 21 个正常、22 个宫颈上皮内瘤变（CIN(2-3)）和 18 个癌症组织中确定 EEF1A1 的表达有效性；在正常、CIN(2-3)和癌症组织之间未发现表达差异（P>0.05）。我们的结果表明，EEF1A1 可作为临床宫颈样本基因谱研究中归一化的参考基因，EEF1A1 和 GAPDH 的组合可作为更可靠的归一化策略。
