Mitchell E Garber | Search PubMed

PubMed2003

Loss of PDCD4 expression in human lung cancer correlates with tumour progression and prognosis.

The programmed cell death 4 gene (PDCD4), a newly identified transformation suppressor, was analysed in lung tumour cell lines and primary lung carcinomas. Reduced PDCD4 mRNA expression was observed in two immortalized lung cell lines and 18 cancer cell lines by northern blot analysis. In the survey of primary lung tumours, PDCD4 cDNA was poorly represented in 47 lung tumours compared with normal lung tissue by cDNA microarray analysis and this poor representation was significantly associated with high-grade (G3) adenocarcinomas (p = 0.012). Immunohistochemical analysis of 124 primary carcinomas comprising all subtypes demonstrated that PDCD4 protein expression was widely lost in tumour samples (83%) and was negatively related to poor prognosis (p = 0.013). The loss of PDCD4 expression correlated with higher grade and disease stage (p = 0.045 and 0.034, respectively), but not tumour size and nodal status. Similarly to the cDNA data, lack of PDCD4 expression was significantly linked to tumour grade in adenocarcinoma (n = 59, p = 0.048), while in squamous cell carcinoma (n = 58), no relationship between PDCD4 expression and clinicopathological parameters was established. These data suggest that the loss of PDCD4 expression is a prognostic factor in lung cancer and may correlate with tumour progression.

Adenocarcinoma↗

PubMed2002

Nonparametric methods for identifying differentially expressed genes in microarray data.

MOTIVATION: Gene expression experiments provide a fast and systematic way to identify disease markers relevant to clinical care. In this study, we address the problem of robust identification of differentially expressed genes from microarray data. Differentially expressed genes, or discriminator genes, are genes with significantly different expression in two user-defined groups of microarray experiments. We compare three model-free approaches: (1). nonparametric t-test, (2). Wilcoxon (or Mann-Whitney) rank sum test, and (3). a heuristic method based on high Pearson correlation to a perfectly differentiating gene ('ideal discriminator method'). We systematically assess the performance of each method based on simulated and biological data under varying noise levels and p-value cutoffs. RESULTS: All methods exhibit very low false positive rates and identify a large fraction of the differentially expressed genes in simulated data sets with noise level similar to that of actual data. Overall, the rank sum test appears most conservative, which may be advantageous when the computationally identified genes need to be tested biologically. However, if a more inclusive list of markers is desired, a higher p-value cutoff or the nonparametric t-test may be appropriate. When applied to data from lung tumor and lymphoma data sets, the methods identify biologically relevant differentially expressed genes that allow clear separation of groups in question. Thus the methods described and evaluated here provide a convenient and robust way to identify differentially expressed genes for further biological and clinical analysis.

Carcinoma, Squamous Cell↗