Biomedical subjects

Junbai Wang

Publications and source records attributed to Junbai Wang.

10 recordsLinked to original sources

PubMed2006

Array comparative genomic hybridization reveals distinct DNA copy number differences between gastrointestinal stromal tumors and leiomyosarcomas.

Leiomyosarcomas are spindle cell tumors showing smooth muscle differentiation. Until recently, most gastrointestinal stromal tumors (GIST) were also classified as smooth muscle tumors, but now GISTs are recognized as a separate entity, defined as spindle cell and/or epithelioid tumors localized in the gastrointestinal tract. Using microarray-based comparative genomic hybridization (array CGH), we have created a detailed map of DNA copy number changes for 7 GISTs and 12 leiomyosarcomas. Considerable gains and losses of chromosomal segments were observed in both tumor types. The most frequent aberration observed in GISTs was loss of chromosomes 14 and 22, with minimal recurrent regions in 14q11.2-q32.33 (71% of the tumors) and 22q12.2-q13.31 (100%). In leiomyosarcomas, frequent loss of chromosome 10 and 13q was observed, with minimal recurrent regions in 10q21.3 (75%) and 13q14.2-q14.3 (75%). Recurrent high-level amplification of 17p13.1-p11.2 was detected in leiomyosarcomas. Expression profiling using cDNA microarrays revealed four candidate genes in this region with high expression (AURKB, SREBF1, MFAP4, and FLJ10847). Altered expression of AURKB and SREBF1 has been observed previously in other malignancies. Hierarchical clustering of all samples separated GISTs and leiomyosarcomas into two distinct clusters. Statistical analysis identified six chromosomal regions, 1p36.11-p13.1, 9q21.11-9q34.3, 14q11.2-q23.2, 14q31.3-q32.33, 15q24.3-q26.3, and 22q11.21-q13.31, which were significantly different in copy number between GISTs and leiomyosarcomas. Our results show the potential of using array comparative genomic hybridization to classify histologically similar tumors such as GISTs and leiomyosarcomas.

Adult↗

PubMed2006

Hotspots of transcription factor colocalization in the genome of Drosophila melanogaster.

Regulation of gene expression is a highly complex process that requires the concerted action of many proteins, including sequence-specific transcription factors, cofactors, and chromatin proteins. In higher eukaryotes, the interplay between these proteins and their interactions with the genome still is poorly understood. We systematically mapped the in vivo binding sites of seven transcription factors with diverse physiological functions, five cofactors, and two heterochromatin proteins at approximately 1-kb resolution in a 2.9 Mb region of the Drosophila melanogaster genome. Surprisingly, all tested transcription factors and cofactors show strongly overlapping localization patterns, and the genome contains many "hotspots" that are targeted by all of these proteins. Several control experiments show that the strong overlap is not an artifact of the techniques used. Colocalization hotspots are 1-5 kb in size, spaced on average by approximately 50 kb, and preferentially located in regions of active transcription. We provide evidence that protein-protein interactions play a role in the hotspot association of some transcription factors. Colocalization hotspots constitute a previously uncharacterized type of feature in the genome of Drosophila, and our results provide insights into the general targeting mechanisms of transcription regulators in a higher eukaryote.

Animals↗

PubMed2005

New probabilistic graphical models for genetic regulatory networks studies.

This paper introduces two new probabilistic graphical models for reconstruction of genetic regulatory networks using DNA microarray data. One is an independence graph (IG) model with either a forward or a backward search algorithm and the other one is a Gaussian network (GN) model with a novel greedy search method. The performances of both models were evaluated on four MAPK pathways in yeast and three simulated data sets. Generally, an IG model provides a sparse graph but a GN model produces a dense graph where more information about gene-gene interactions may be preserved. The results of our proposed models were compared with several other commonly used models, and our models have shown to give superior performance. Additionally, we found the same common limitations in the prediction of genetic regulatory networks when using only DNA microarray data.

Algorithms↗

PubMed2004

M-CGH: analysing microarray-based CGH experiments.

BACKGROUND: Microarray-based comparative genomic hybridisation (array CGH) is a technique by which variation in relative copy numbers between two genomes can be analysed by competitive hybridisation to DNA microarrays. This technology has most commonly been used to detect chromosomal amplifications and deletions in cancer. Dedicated tools are needed to analyse the results of such experiments, which include appropriate visualisation, and to take into consideration the physical relation in the genome between the probes on the array. RESULTS: M-CGH is a MATLAB toolbox with a graphical user interface designed specifically for the analysis of array CGH experiments, with multiple approaches to ratio normalization. Specifically, the distributions of three classes of DNA copy numbers (gains, normal and losses) can be estimated using a maximum likelihood method. Amplicon boundaries are computed by either the fuzzy K-nearest neighbour method or a wavelet approach. The program also allows linking each genomic clone with the corresponding genomic information in the Ensembl database http://www.ensembl.org. CONCLUSIONS: M-CGH, which encompasses the basic tools needed for analysing array CGH experiments, is freely available for academics http://www.uio.no/~junbaiw/mcgh, and does not require any other MATLAB toolbox.

Chromosomes, Artificial, Bacterial↗

PubMed2004

Plasminogen activator inhibitor-1 increases the expression of VEGF in human glioma cells.

The level of plasminogen activator inhibitor-1 (PAI-1) in tumor tissue has been shown to be an independent negative prognostic factor in different cancers. There are several proposed reasons for this, among these, the influence of PAI-1 on tumor neovascularization and cell migration. We report that PAI-1 stimulates expression and release of vascular endothelial growth factor (VEGF) in the human glioma cell line D54Mg, and thereby stimulates the proliferation of human umbilical vein endothelial cells (HUVEC) in vitro. To search for possible molecular effects of PAI-1 on malignant cells, cDNA array hybridization analysis of D54Mg glioma cells transfected with an adenoviral PAI-1 expression vector was performed. This revealed that the VEGF response was accompanied with the simultaneous upregulation of GADD153, Rho GTPase activating protein 4 (p115), Collagen type VI alpha 1 and cell division cycle 42 (CDC42) transcripts. Exogenous treatment of D54Mg cells with a constitutively active recombinant PAI-1 protein confirmed an upregulation of VEGF expression in a time- and dose-dependent manner, and supernatants from such cultures stimulated the proliferation of human umbilical vein endothelial cells in vitro. In 44 human glioma biopsies, patients, the protein levels of PAI-1 correlated strongly with the levels of VEGF in the tumor tissues. Whereas VEGF expression correlated inversely with survival, there was no statistically significant prediction of survival by PAI-1 in this group of patients. These clinical data support and strengthen the hypothesis that PAI-1 is one of the factors regulating and inducing the VEGF expression in human gliomas. The induction of VEGF expression and thus endothelial cell proliferation may represent an as yet undiscovered mechanism whereby PAI-1 contributes to tumor neoangiogenesis.

Adenoviridae↗

PubMed2003

Tumor classification and marker gene prediction by feature selection and fuzzy c-means clustering using microarray data.

BACKGROUND: Using DNA microarrays, we have developed two novel models for tumor classification and target gene prediction. First, gene expression profiles are summarized by optimally selected Self-Organizing Maps (SOMs), followed by tumor sample classification by Fuzzy C-means clustering. Then, the prediction of marker genes is accomplished by either manual feature selection (visualizing the weighted/mean SOM component plane) or automatic feature selection (by pair-wise Fisher's linear discriminant). RESULTS: The proposed models were tested on four published datasets: (1) Leukemia (2) Colon cancer (3) Brain tumors and (4) NCI cancer cell lines. The models gave class prediction with markedly reduced error rates compared to other class prediction approaches, and the importance of feature selection on microarray data analysis was also emphasized. CONCLUSIONS: Our models identify marker genes with predictive potential, often better than other available methods in the literature. The models are potentially useful for medical diagnostics and may reveal some insights into cancer classification. Additionally, we illustrated two limitations in tumor classification from microarray data related to the biology underlying the data, in terms of (1) the class size of data, and (2) the internal structure of classes. These limitations are not specific for the classification models used.

Biomarkers, Tumor↗

PubMed2003

MGraph: graphical models for microarray data analysis.

UNLABELLED: This paper introduces a MATLAB toolbox, MGraph, which applies graphical models as a natural environment to formulate and solve problems in microarray data analysis. MGraph with its graphical interface allows the user to predict genetic regulatory networks by a graphical gaussian model (GGM), and to quantify the effects of different experimental treatment conditions on gene expression profiles by a graphical log-linear model (GLM). The power of graphical models was explored and illustrated through two example applications. First, four MAPK pathways in yeast were meaningfully reconstructed through GGM. Second, GLM was used to quantify the contributions of sex, genotype and age to transcriptional variance in Drosophila melanogaster. This application may provide a valuable aid in the prediction of genetic regulatory networks, as well as in investigations of various experimental conditions that affect global gene expression profiles. AVAILABILITY: The MATLAB program MGraph is freely available at http://www.uio.no/~junbaiw/mgraph/mgraph.html for academics.

Algorithms↗

PubMed2003

Splenic marginal zone lymphoma with villous lymphocytes shows on-going immunoglobulin gene mutations.

Splenic marginal zone lymphoma (also splenic lymphoma with villous lymphocytes) is a B-cell non-Hodgkin's lymphoma with a characteristic morphology and phenotype. We studied the pattern of somatic hypermutation of the rearranged immunoglobulin heavy chain genes on 23 cases and have correlated these data with survival as well as immunophenotypic and genetic characteristics of the cases. Two-thirds of the cases show immunoglobulin gene mutations, half of which show evidence of antigen selection, whereas one-third of the cases show no significant mutations. On-going mutation, a feature characteristic of follicular lymphoma, was demonstrated in all six cases randomly selected for this analysis, including one case with a low number of mutations (<2%). No statistical significant correlation was found between immunoglobulin mutation status and clinical, immunophenotypic, or genetic characteristics. Our results demonstrate that on-going somatic hypermutation is a prominent feature of splenic marginal zone lymphoma with circulating villous lymphocytes. On-going somatic hypermutation has previously been demonstrated in extra-nodal and nodal marginal zone lymphoma. Our results indicate that marginal zone lymphomas at different anatomical localizations may derive from a similar B-cell subset.

Aged↗

PubMed2002

Clustering of the SOM easily reveals distinct gene expression patterns: results of a reanalysis of lymphoma study.

BACKGROUND: A method to evaluate and analyze the massive data generated by series of microarray experiments is of utmost importance to reveal the hidden patterns of gene expression. Because of the complexity and the high dimensionality of microarray gene expression profiles, the dimensional reduction of raw expression data and the feature selections necessary for, for example, classification of disease samples remains a challenge. To solve the problem we propose a two-level analysis. First self-organizing map (SOM) is used. SOM is a vector quantization method that simplifies and reduces the dimensionality of original measurements and visualizes individual tumor sample in a SOM component plane. Next, hierarchical clustering and K-means clustering is used to identify patterns of gene expression useful for classification of samples. RESULTS: We tested the two-level analysis on public data from diffuse large B-cell lymphomas. The analysis easily distinguished major gene expression patterns without the need for supervision: a germinal center-related, a proliferation, an inflammatory and a plasma cell differentiation-related gene expression pattern. The first three patterns matched the patterns described in the original publication using supervised clustering analysis, whereas the fourth one was novel. CONCLUSIONS: Our study shows that by using SOM as an intermediate step to analyze genome-wide gene expression data, the gene expression patterns can more easily be revealed. The "expression display" by the SOM component plane summarises the complicated data in a way that allows the clinician to evaluate the classification options rather than giving a fixed diagnosis.

Cluster Analysis↗

PubMed2002

MArray: analysing single, replicated or reversed microarray experiments.

UNLABELLED: MArray is a Matlab toolbox with a graphical user interface that allows the user to analyse single or paired microarray datasets by direct input of the raw data output file from image analysis packages, such as QuantArray or GenePiX. The application provides simple procedures to manually evaluate the quality of each measurement, multiple approaches to both ratio normalization (simple normalization, intensity dependent normalization) and evaluation of the reproducibility of paired experiments (using the techniques 'simple statistical method' and 'quality control ellipse' and 'significance analysis of microarrays'). Specifically, interactive spot evaluation functions are available in MArray and an online gene information database (NCBI UniGene) is linked. The application may provide a valuable aid in selecting and optimizing experimental procedures, as well as serving as an analytical tool for two-state biological comparisons, such as a study of single-dose activation. It is entirely platform independent, and only requires Matlab installed. AVAILABILITY: http://matrise.uio.no/marray/marray.html

Computer Graphics↗