The growing interest on microRNA (miRNA) expression in cancer motivates the need for improvement of technical aspects. TaqMan Human MicroRNA Arrays with comprehensive coverage of 667 unique miRNAs from Sanger miRBase are now available (Applied Biosystems, Foster city, CA) to quantitatively assay miRNA expression. A recent study reported that these qRT-PCR arrays perform better than miRNA microarrays which utilizes U6 RNA as an endogenous control for normalization. However, the choice of control for normalization varies depending on the tissue under examination. The TaqMan Low Density Arrays (TLDA) arrays A and B include six different controls with at least quadruplet assays for each. A recent study indicated that the average expression normalization including all miRNAs on array performed better than other normalization methods indicating a further need to examine normalization of TaqMan miRNA array data. The choice of a suitable control other than an average expression over the entire array is a more general approach for qRT-PCR data since constant average expression may not be valid for these arrays. Hence, we combined both concepts to determine a normalization constant. We examined miRNA expressions of 5 endometrial cancer cell lines assayed by these TaqMan Low Density Arrays for miRNA. Each cell line was assayed in triplicate. The cycle threshold (Ct) values of positive controls U6 (MammU6), RNU48, RNU44, RNU24, RNU6B, RNU43 in these were found to be in the range of 15 to 39 where RNU6B and RNU43 had low expressions. In some cases, we observed failures in endogenous control U6 and differences between arrays A and B. Therefore multiple controls were included for normalization but with appropriate weighting for highly expressed controls. This was done by calculating the average of 2−Ct values for controls that had Ct-values below a threshold level (Ct < 25) and transforming this average back to logarithmic scale to the base 2. We then compared the normalized Ct-values by this method with the normalization using geometric average method. This weighted average normalization method resulted in smaller replicate variances of controls than the geometric average method.

Citation Format: {Authors}. {Abstract title} [abstract]. In: Proceedings of the 101st Annual Meeting of the American Association for Cancer Research; 2010 Apr 17-21; Washington, DC. Philadelphia (PA): AACR; Cancer Res 2010;70(8 Suppl):Abstract nr 4050.