PubMed Health⌕ Search

Biomedical subjects

Mitchell E Garber

Publications and source records attributed to Mitchell E Garber.

4 recordsLinked to original sources
PubMed2004

[Evaluation of p63 expression in lung cancer by use of complementary DNA and tissue microarray].

OBJECTIVE: To study p63 expression at mRNA transcript and protein levels in human lung cancers, including squamous cell lung carcinoma (SCC), adenocarcinoma, large cell lung carcinoma (LCLC) and small cell lung carcinoma (SCLC), or the corresponding metastatic foci. The relationship of p63 expression and alterations in p63 locus at chromosomal 3q27-29 was also determined. METHODS: p63 gene expression in 72 cases of SCC, adenocarcinoma, LCLC and SCLC was analyzed by cDNA microarray technology. Tissue microarray of specimens from 150 cases of primary lung cancer was prepared for immunohistochemical study for p63 protein. Possible chromosomal alterations at the p63 locus in 70 cases of primary lung cancer were studied by comparative genomic hybridization (CGH) technology. RESULTS: p63 mRNA transcript expression was significantly increased by more than 10-fold in SCC, as compared with that in other histologic subtypes including adenocarcinoma, LCLC and SCLC. p63 mRNA expression in metastatic foci was also remarkably higher than that in their primary tumors (P < 0.001). Immunostaining showed that p63 protein expression was observed in 94.64% of SCC, whereas only one lung adenocarcinoma (1.79%) was positive. Immunopositivity was also demonstrated in 2 of the 4 LCLC cases studied. None of the SCLC cases was positive. There was a statistically significant difference in p63 expression between pT1 and pT2 tumors (P < 0.05). The CGH results showed that overrepresentation of p63 locus at chromosomal 3q27-29 was a typical finding in SCC. p63 immunopositivity also correlated significantly with pronounced gains of p63 locus at chromosomal 3q27-q29 (P < 0.000 1), suggesting that strong expression of p63 in lung SCC was associated with increased gene amplification. CONCLUSION: p63 may play a role in oncogenesis of human lung squamous cell carcinoma and development of metastasis.

Adenocarcinoma↗
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↗