Significance Analysis Of Microarrays Method Research Articles

Data analysis methods for detection of differential protein expression in two-dimensional gel electrophoresis

The recent development of microarray technology has led statisticians and bioinformaticians to develop new statistical methodologies for comparing different biological samples. The objective is to identify a small number of differentially expressed genes from among thousands. In quantitative proteomics, analysis of protein expression using two-dimensional gel electrophoresis shows some similarities with transcriptomic studies. Thus, the goal of this study was to evaluate different data analysis methodologies widely used in array analysis using different proteomic data sets of hundreds of proteins. Even with few replications, the significance analysis of microarrays method appeared to be more powerful than the Student’s t test in truly declaring differentially expressed proteins. This procedure will avoid wasting time due to false positives and losing information with false negatives.

Identification of differentially expressed genes in clinically distinct groups of serous ovarian carcinomas using cDNA microarray

To identify changes in gene expression in serous epithelial ovarian cancers (SEOC), we utilized cDNA microarrays consisting of 2382 genes with cancer related properties to analyze tumors from 20 patients with defined clinical out-comes. The significance analysis of microarrays method was used to determine differentially expressed genes, leading to the identification of 134 up-regulated and 231 down-regulated genes overall. By increasing the stringency of the statistical selection criteria, 41 over-expressed and 51 under-expressed genes were identified. The median duration of follow-up of the 20 patients was 16.8 months with a median progression free survival of 7.0 months. We found 11 genes that were differentially over-expressed in patients with recurrent disease, and 3 genes (homo sapiens mRNA for Ins P3 5-phophatase, lipoma HMGIC fusion partner-like 2 and CD63 melanoma 1 antigen) in patients who were dead of disease. Subsequently, we examined the distribution of the differentially expressed genes in the cDNA library database from adult human tumor and normal tissues using the DigiNorthern method to identify a subset of genes with relatively restricted tissue distribution. Finally, protein expression of 5 selected genes were further examined using immunohistochemistry applied on a tissue microarray prepared from an independent panel of 93 SEOC tissues. The results provided validation for 2 under-expressed genes (E2F transcription factor 5 and CK14) and 3 over-expressed genes (Bcl2-like 1, COX-2, CD63). Our study demonstrates differential gene expression in clinically distinct groups of SEOC using cDNA microarray. These genes may potentially be useful as biomarkers and/or targets for therapeutic intervention.