Profiling and Verification of Gene Expression Patterns in Normal and Malignant Human Prostate Tissues by cDNA Microarray Analysis

• Main Authors: Hassan Chaib, Erin K. Cockrell, Mark A. Rubin, Jill A. Macoska
• Format: Article
• Language: English
• Published: Elsevier 2001-01-01
• Series: Neoplasia: An International Journal for Oncology Research
• Subjects: microarmy; prostate; expression; RNA; cancer
• Online Access: http://www.sciencedirect.com/science/article/pii/S1476558601800154
• ISSN: 1476-5586; 1522-8002

cDNA microarray technology allows the ‘profiling’ of gene expression patterns for virtually any cellular material. In this study, we applied cDNA microarray technology to profile changes in gene expression associated with human prostate tumorigenesis. RNA prepared from normal and malignant prostate tissue was examined for the expression levels of 588 human genes. Four different methods for data normalization were utilized. Of these, normalization to ACTB expression proved to be the most rigorous technique with the least probability of producing spurious results. After normalization to ACTB expression, 15 of 588 (2.6%) genes examined by array analysis were differentially expressed by a factor of 2x or more in malignant compared to normal prostate tissues. The expression patterns for 8 of 15 genes have been reported previously in prostate tissues (TGFβ3, TGFBR3, IGFII, IGFBP2, VEGF, FGF7, ERBB3, MYC), but those of seven genes are reported here for the first time (MLH1, CYP1B1, RFC4, EPHB3, MGST1, BTEB2, MLP). These genes describe at least four metabolic and signaling pathways likely disrupted in human prostate tumorigenesis. Reverse transcriptase polymerase chain reaction (RTPCR) and Northern blot analyses quantitated with reference to ACTB expression levels verified the trends in gene expression levels observed by array analysis for 14/15 and 8/8 genes, respectively. However, RT-PCR and Northern blot analyses accurately verified the ‘fold’ differences in expression levels for only 6/15 (40%) and 7/8 (88%) of genes examined, respectively, demonstrating the need to better validate quantitative differences in gene expression revealed by array-based techniques.