Identification of genes for normalization of quantitative real-time PCR data in ovarian tissues

Increased attention has been paid to the determination of the potential biomarker and therapeutic target for ovarian cancer in recent years. However, the normalization of quantitative real-time PCR is important to obtain accurate gene expression data. We investigated the stability of 20 reference genes in ovarian tissues under different conditions to determine the most adequate for this application. The study characterized the expression of 20 possible reference genes among 52 ovarian tissue samples involving the normal, non-malignant, and primary ovarian carcinomas. One-way analysis of variance (ANOVA) method was used to compare the candidate gene changes brought about by the disease progression. The stability and suitability of the genes with no statistic difference were further validated employing geNorm and NormFinder softwares. Results showed that the expression levels of the 20 reference genes varied, while the RPL4, RPLP0, HSPCB, TPT1, RPL13A, 18S rRNA, PPIA, TBP, and GUSB kept statistic stability despite different ovarian tissue conditions. RPL4, RPLP0, and HSPCB were demonstrated as the most stable reference genes and the combination of the RPLP0 and RPL4 should be recommended as a much more reliable normalization strategy.