Biomedical subjects

Junbai Wang

Publications and source records attributed to Junbai Wang.

3 recordsLinked to original sources

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↗