Selection of suitable reference genes for gene expression studies in normal human ovarian tissues, borderline ovarian tumours and ovarian cancer.

Abstract

The use of reference genes is the most common method of controlling the variation in mRNA expression during quantitative polymerase chain reaction, although the use of traditional reference genes, such as β‑actin, glyceraldehyde‑3‑phosphate dehydrogenase or 18S ribosomal RNA, without validation occasionally leads to unreliable results. Therefore, the present study aimed to evaluate a set of five commonly used reference genes to determine the most suitable for gene expression studies in normal ovarian tissues, borderline ovarian and ovarian cancer tissues. The expression stabilities of these genes were ranked using two gene stability algorithms, geNorm and NormFinder. Using geNorm, the two best reference genes in ovarian cancer were β‑glucuronidase and β‑actin. Hypoxanthine phosphoribosyltransferase‑1 and β‑glucuronidase were the most stable in ovarian borderline tumours, and hypoxanthine phosphoribosyltransferase‑1 and glyceraldehyde‑3‑phosphate dehydrogenase were the most stable in normal ovarian tissues. NormFinder ranked β‑actin the most stable in ovarian cancer, and the best combination of two genes was β‑glucuronidase and β‑actin. In borderline tumours, hypoxanthine phosphoribosyltransferase‑1 was identified as the most stable, and the best combination was hypoxanthine phosphoribosyltransferase‑1 and β‑glucuronidase. In normal ovarian tissues, β‑glucuronidase was recommended as the optimum reference gene, and the most optimum pair of reference genes was hypoxanthine phosphoribosyltransferase‑1 and β‑actin. To the best of our knowledge, this is the first study to investigate the selection of a set of reference genes for normalisation in quantitative polymerase chain reactions in different ovarian tissues, and therefore it is recommended that β‑glucuronidase, β‑actin and hypoxanthine phosphoribosyltransferase‑1 are the most suitable reference genes for such analyses.