Biomedical subjects

Zailong Wang

Publications and source records attributed to Zailong Wang.

3 recordsLinked to original sources

PubMed2006

Prognostic DNA methylation biomarkers in ovarian cancer.

PURPOSE: Aberrant DNA methylation, now recognized as a contributing factor to neoplasia, often shows definitive gene/sequence preferences unique to specific cancer types. Correspondingly, distinct combinations of methylated loci can function as biomarkers for numerous clinical correlates of ovarian and other cancers. EXPERIMENTAL DESIGN: We used a microarray approach to identify methylated loci prognostic for reduced progression-free survival (PFS) in advanced ovarian cancer patients. Two data set classification algorithms, Significance Analysis of Microarray and Prediction Analysis of Microarray, successfully identified 220 candidate PFS-discriminatory methylated loci. Of those, 112 were found capable of predicting PFS with 95% accuracy, by Prediction Analysis of Microarray, using an independent set of 40 advanced ovarian tumors (from 20 short-PFS and 20 long-PFS patients, respectively). Additionally, we showed the use of these predictive loci using two bioinformatics machine-learning algorithms, Support Vector Machine and Multilayer Perceptron. CONCLUSION: In this report, we show that highly prognostic DNA methylation biomarkers can be successfully identified and characterized, using previously unused, rigorous classifying algorithms. Such ovarian cancer biomarkers represent a promising approach for the assessment and management of this devastating disease.

Adenocarcinoma↗

PubMed2006

Microarray analysis of gene expression: considerations in data mining and statistical treatment.

DNA microarray represents a powerful tool in biomedical discoveries. Harnessing the potential of this technology depends on the development and appropriate use of data mining and statistical tools. Significant current advances have made microarray data mining more versatile. Researchers are no longer limited to default choices that generate suboptimal results. Conflicting results in repeated experiments can be resolved through attention to the statistical details. In the current dynamic environment, there are many choices and potential pitfalls for researchers who intend to incorporate microarrays as a research tool. This review is intended to provide a simple framework to understand the choices and identify the pitfalls. Specifically, this review article discusses the choice of microarray platform, preprocessing raw data, differential expression and validation, clustering, annotation and functional characterization of genes, and pathway construction in light of emergent concepts and tools.

Cluster Analysis↗

PubMed2005

Hypermethylation of 18S and 28S ribosomal DNAs predicts progression-free survival in patients with ovarian cancer.

PURPOSE: Repetitive ribosomal DNA (rDNA) genes are GC-rich clusters in the human genome. The aim of the study was to determine the methylation status of two rDNA subunits, the 18S and 28S genes, in ovarian tumors and to correlate methylation levels with clinicopathologic features in a cohort of ovarian cancer patients. EXPERIMENTAL DESIGN: 18S and 28S rDNA methylation was examined by quantitative methylation-specific PCR in 74 late-stage ovarian cancers, 9 histologically uninvolved, and 11 normal ovarian surface epithelial samples. In addition, methylation and gene expression levels of 18S and 28S rDNAs in two ovarian cancer cell lines were examined by reverse transcription-PCR before and after treatment with the demethylating drug 5'-aza-2'-deoxycytidine. RESULTS: The methylation level (amount of methylated rDNA/beta-actin) of 18S and 28S rDNAs was significantly higher (P < 0.05) in tumors than in normal ovarian surface epithelial samples. Methylation of 18S and 28S rDNA was highly correlated (R2= 0.842). Multivariate analysis by Cox regression found that rDNA hypermethylation [hazard ratio (HR), 0.25; P < 0.01], but not age (HR, 1.29; P = 0.291) and stage (HR, 1.09; P = 0.709), was independently associated with longer progression-free survival. In ovarian cancer cell lines, methylation levels of rDNA correlated with gene down-regulation and 5'-aza-2'-deoxycytidine treatment resulted in a moderate increase in 18S and 28S rDNA gene expressions. CONCLUSION: This is the first report of rDNA hypermethylation in ovarian tumors. Furthermore, rDNA methylation levels were higher in patients with long progression-free survival versus patients with short survival. Thus, rDNA methylation as a prognostic marker in ovarian cancer warrants further investigation.

Adult↗