3.9 Article

Optimization of laser capture microdissection and RNA amplification for gene expression profiling of prostate cancer

Journal

BMC MOLECULAR BIOLOGY
Volume 8, Issue -, Pages -

Publisher

BMC
DOI: 10.1186/1471-2199-8-25

Keywords

-

Funding

  1. NCI NIH HHS [P50CA91956-5, P50 CA091956] Funding Source: Medline

Ask authors/readers for more resources

Background: To discover prostate cancer biomarkers, we profiled gene expression in benign and malignant cells laser capture microdissected (LCM) from prostate tissues and metastatic prostatic adenocarcinomas. Here we present methods developed, optimized, and validated to obtain high quality gene expression data. Results: RNase inhibitor was included in solutions used to stain frozen tissue sections for LCM, which improved RNA quality significantly. Quantitative PCR assays, requiring minimal amounts of LCM RNA, were developed to determine RNA quality and concentration. SuperScript II (TM) reverse transcriptase was replaced with SuperScript III (TM) and SpeedVac concentration was eliminated to optimize linear amplification. The GeneChip((R)) IVT labeling kit was used rather than the Enzo BioArray (TM) HighYield (TM) RNA transcript labeling kit since side-by-side comparisons indicated high-end signal saturation with the latter. We obtained 72 mu g of labeled complementary RNA on average after linear amplification of about 2 ng of total RNA. Conclusion: Unsupervised clustering placed 5/5 normal and 2/2 benign prostatic hyperplasia cases in one group, 5/7 Gleason pattern 3 cases in another group, and the remaining 2/7 pattern 3 cases in a third group with 8/8 Gleason pattern 5 cases and 3/3 metastatic prostatic adenocarcinomas. Differential expression of alpha-methylacyl coenzyme A racemase (AMACR) and hepsin was confirmed using quantitative PCR.

Authors

I am an author on this paper
Click your name to claim this paper and add it to your profile.

Reviews

Primary Rating

3.9
Not enough ratings

Secondary Ratings

Novelty
-
Significance
-
Scientific rigor
-
Rate this paper

Recommended

No Data Available
No Data Available