Biomedical subjects

Antai Wang

Publications and source records attributed to Antai Wang.

5 recordsLinked to original sources

PubMed2006

Expression patterns among interferon regulatory factor-1, human X-box binding protein-1, nuclear factor kappa B, nucleophosmin, estrogen receptor-alpha and progesterone receptor proteins in breast cancer tissue microarrays.

Interferon regulatory factor-1 (IRF-1), human X-box binding protein-1 (hXBP-1), nuclear factor kappa B p65 (NFkappaB p65) and nucleophosmin (NPM) have been implicated in a signaling network of endocrine responsiveness. Expression of these proteins was measured by immunohistochemistry in tissue microarrays of 54 breast tumors. Correlations between each protein and established prognostic markers were assessed by Spearman's rank order correlation coefficient and partial correlation coefficient analyses. Moderate/strong staining is seen for hXBP-1 (79% of tumors) and NFkappaB p65 (57%). NPM exhibits nuclear staining (95%); IRF-1 exhibits both cytosolic (IRF-1c; 90%) and nuclear staining (IRF-1n; 51%). IRF-1c is associated with age (p=0.034); IRF-1n and PgR expression are correlated (p=0.014). NFkappaB p65 shows a borderline association with S phase (p=0.062). Coexpression of IRF-1c and hXBP1 (p=0.001), IRF-1c and NFkappaB (p=0.002), and hXBP-1 and NFkappaB (p=0.018) is observed. An inverse correlation exists between IRF-1n and NFkappaB (p=0.034). All four proteins are detected in breast tumors and their expression patterns support their role(s) in a key signaling network.

Adult↗

PubMed2006

Estrogen receptor alpha positive breast tumors and breast cancer cell lines share similarities in their transcriptome data structures.

Established human breast cancer cell lines are widely used as experimental models in breast cancer research. While these cell lines and their variants share many phenotypic characteristics with human breast tumors, the extent to which they reflect the underlying molecular biology of breast cancer remains controversial. We explored this issue using a probabilistic rather than heuristic approach. Data from gene expression microarrays were used to compare the global structures of the transcriptomes of three estrogen receptor alpha positive (ER+) human breast cancer cell lines (MCF-7, T47D, ZR-75-1) and 13 human breast tumors (11 ER+; 2 ER-). Linear representations of the respective data structures were obtained by deriving those top principal components (PCs) required to capture > or =80% of the cumulative variance for each data set (M PCs). We then identified those genes most highly correlated with the M PCs (Pearson's correlation coefficient r > or =0.800) and identified a group of 36 genes commonly correlated with both the cell line (M = 5 PCs) and tumor (M = 6 PCs) data structures. All 36 common genes were correlated with PC1 from the breast tumor data: 21/36 genes were correlated with PC1, 14/36 genes correlated with PC2, and 1/36 genes correlated with PC3 from the cell line data. Genes important in defining the data structures include NFkappaB p65, IGFBP-6, ornithine decarboxylase-1, and paxillin. When data from MDA-MB-435 xenografts (ER-) were included in the analysis, we were unable to find any common genes between these xenografts and the breast tumors. These data clearly imply that MCF-7, T47D, and ZR-75-1 cells and ER+ breast tumors share substantial global similarities in the structures of their respective transcriptomes, and that these cell lines are good models in which to identify molecular events that are likely to be important in some ER+ human breast cancers.

Animals↗

PubMed2005

Gene selection for microarray data analysis using principal component analysis.

Principal component analysis (PCA) has been widely used in multivariate data analysis to reduce the dimensionality of the data in order to simplify subsequent analysis and allow for summarization of the data in a parsimonious manner. It has become a useful tool in microarray data analysis. For a typical microarray data set, it is often difficult to compare the overall gene expression difference between observations from different groups or conduct the classification based on a very large number of genes. In this paper, we propose a gene selection method based on the strategy proposed by Krzanowski. We demonstrate the effectiveness of this procedure using a cancer gene expression data set and compare it with several other gene selection strategies. It turns out that the proposed method selects the best gene subset for preserving the original data structure.

Gene Expression↗

PubMed2005

Interferon regulatory factor-1 (IRF-1) exhibits tumor suppressor activities in breast cancer associated with caspase activation and induction of apoptosis.

We have directly assessed the ability of interferon regulatory factor-1 (IRF-1) to act as a tumor suppressor gene in human breast cancer cells and explored whether this suppressor function is mechanistically conferred by affecting cell cycle transition, apoptosis and/or caspase activation. We have used a dual approach, measuring whether overexpression of wild-type IRF-1 or a dominant negative IRF-1 (dnIRF-1) produce opposing effects on breast cancer cell proliferation in vitro or tumorigenicity in athymic nude mice. Mechanistic studies determined the effects of blocking endogenous IRF-1 expression on cell cycle transition by flow cytometry, on apoptosis by Annexin V staining, and on caspase activation by fluorescent substrate cleavage. IRF-1 mRNA (P < or = 0.001) and protein (P < or = 0.001) are highly expressed in non-tumorigenic, normal, mammary epithelial cells, with intermediate expression in tumorigenic, but non-metastatic, cells and very low expression in metastatic cell lines. In MCF-7 cells transfected with a wild-type IRF-1 (MCF-7/IRF-1), IRF-1 mRNA expression inversely correlates with the rate of cell proliferation (r = -0.91; P = 0.002). Conversely, expression of dnIRF-1 in both MCF-7 (MCF-7/dnIRF-1; p53 wild-type) and T47D cells (T47D/dnIRF-1; p53 mutant) increases cell proliferation (P < or = 0.001). In athymic nude mice, the incidence of MCF-7/IRF-1 xenografts is reduced (P = 0.045), whereas MCF-7/dnIRF-1 xenografts exhibit a significantly higher tumor incidence (P < or = 0.001). Effects of IRF-1/dnIRF-1 are mediated through changes in the rates of apoptosis and not through cell cycle regulation. MCF-7/dnIRF-1 cells exhibit a 50% decrease in basal apoptosis (P = 0.007) and a significant reduction in caspase 8 activity (P = 0.03); similar effects occur in T47D/dnIRF-1 cells, where the effects on apoptosis appear to be mediated through inhibition of caspases 3/7 (P < 0.001) and caspase 8 (P = 0.03). These data establish a functional role for IRF-1 in the growth suppression of breast cancer cells and strongly implicate IRF-1 as a tumor suppressor gene in breast cancer that acts, independent of p53, to control apoptosis.

Apoptosis↗

PubMed2005

Deletion, methylation, and expression of the NKX3.1 suppressor gene in primary human prostate cancer.

NKX3.1 is a prostate-specific homeoprotein and tumor suppressor that is affected by the loss of 8p21 in prostate cancer. In mice, Nkx3.1 haploinsufficiency results in prostatic dysplasia and complements cancer formation induced by loss of other suppressor genes. However, NKX3.1 expression can be immunohistochemically detected in most primary prostate cancers. We examined the relationship between suppressor gene haploinsufficiency, methylation, and quantitative NKX3.1 expression levels in primary prostate cancer. NKX3.1 gene copy number was assessed by microsatellite analysis, fluorescence in situ hybridization, and quantitative PCR. NKX3.1 gene methylation was determined in prostate cancer cell lines and we thereby identified potential CpG methylation sites for methylation-specific PCR analysis in tissues. We validated and then applied an internally controlled fluorescence immunomicroscopic assay for NKX3.1 protein expression in 48 primary prostate cancer specimens from radical prostatectomies. NKX3.1 loss of heterozygosity was found in 27 of 43 tissues tested. Classic CpG island methylation of the NKX3.1 gene was not found in either prostate cancer cell lines or tissues. However, in 33 of 40 samples tested, CpG sites at -921, -903, and -47 were methylated to a greater degree in malignant than in adjacent normal cells. In 43 of 48 samples, NKX3.1 protein expression was reduced from 0.34 to 0.90 compared with adjacent normal luminal epithelium (mean of all samples, 0.68; 95% confidence interval, 0.05). In 12 cases that also had high-grade prostatic intraepithelial neoplasia, NKX3.1 expression levels were similar in preinvasive and invasive cancer cells and significantly lower than adjacent normal cells. Even in the presence of allelic loss, NKX3.1 expression is reduced over a wide range in prostate cancer at the time of prostatectomy, suggesting that diverse factors influence expression. Samples with protein expression below the median level in cancer cells had both NKX3.1 deletion and selective CpG methylation.

Cell Line, Tumor↗