Differential expression of B29 (CD79b) and mb-1 (CD79a) proteins in acute lymphoblastic leukaemia.
ABSTRACT CD79 is a heterodimeric molecule comprising two polypeptide chains, B29 (CD79b) and mb-1 (CD79a). It is physically linked in the surface of B cells to membrane immunoglobulin, forming the B cell antigen receptor complex. Expression of the mb-1 (CD79a) chain has been studied in leukaemias and shown to be present in most B lineage acute lymphoblastic leukaemias (ALL). In contrast, little is known about the expression of B29 (CD79b) in this condition. Two monoclonal antibodies (MoAb) were used in this study by immunocytochemistry and flow cytometry: HM57, against an intracellular epitope of the mb-1(CD79a) chain, and SN8, reacting with an extracellular epitope of B29 (CD79b). Our aim was to investigate the expression of B29 (CD79b) in the various immunological subtypes of B lineage ALL and compare its cytoplasmic and membrane expression. Seventy-nine cases were studied, including 13 chronic myeloid leukaemia in B lymphoid blast crisis (CML-BC) and 66 ALL, subclassified as early B (two), common (28), pre-B (23), mature (five) and biphenotypic with B lymphoid commitment (eight). Most cases expressed mb-1 (CD79a) in the cytoplasm. B29 (CD79b) was expressed in the cytoplasm in 65% (15/23) of pre-B-ALL and in 14% (4/28) common-ALL but it was detected in the cell membrane in only three cases of mature B-ALL, being negative in all other B lineage subtypes ALL. Three of the biphenotypic leukaemias coexpressed cytoplasmic B29 (CD79b) and mu-chain. This was also seen in two cases of CML-BC, while four cases expressed only cytoplasmic B29 (CD79b) without mu-chain. Our results suggest that during B cell differentiation, B29 (CD79b) is expressed later than mb-1 (CD79a) in the cytoplasm and parallels the cytoplasmic expression of mu-chain. B29 (CD79b) is present in the membrane at a later stage compared to its cytoplasmic expression and found in mature B blasts (B-ALL) that express membrane Ig as it is in normal and leukaemic B lymphocytes.
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ABSTRACT: High-throughput microarray experiments now permit researchers to screen thousands of genes simultaneously and determine the different expression levels of genes in normal or cancerous tissues. In this paper, we address the challenge of selecting a relevant and manageable subset of genes from a large microarray dataset. Currently, most gene selection methods focus on identifying a set of genes that can further improve classification accuracy. Few or none of these small sets of genes, however, are biologically relevant (i.e. supported by medical evidence). To deal with this critical issue, we propose two novel methods that can identify biologically relevant genes concerning cancers. In this paper, we propose two novel techniques, entitled random forest gene selection (RFGS) and support vector sampling technique (SVST). Compared with results from six other methods developed in this paper, we demonstrate experimentally that RFGS and SVST can identify more biologically relevant genes in patients with leukemia or prostate cancer. Among the top 25 genes selected using SVST method, 15 genes were biologically relevant genes in patients with leukemia and 13 genes were biologically relevant genes in patients with prostate cancer. Meanwhile, the RFGS method, while less effective than SVST, still identified an average of 9 biologically relevant genes in both leukemia and prostate cancers. In contrast to traditional statistical methods, which only identify less than 8 genes in patients with leukemia and less than 8 genes in patients with prostate cancer, our methods yield significantly better results. Our proposed SVST and RFGS methods are novel approaches that can identify a greater number of biologically relevant genes. These methods have been successfully applied to both leukemia and prostate cancers. Research in the fields of biology and medicine should benefit from the identification of biologically relevant genes by confirming recent discoveries in cancer research or suggesting new avenues for exploration.BMC Genomics 01/2010; 11:274. DOI:10.1186/1471-2164-11-274 · 4.04 Impact Factor
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ABSTRACT: By extracting significant samples (which we refer to as support vector samples as they are located only on support vectors), we can identify principal genes and then use these genes to classify cancers either by support vector machines (SVM) or back-propagation neural networking (BPNN). We call this approach the support vector sampling technique (SVST). No matter the number of genes selected, our SVST method shows a significant improvement of classification performance. Our SVST method has averages 2–3% better performance when applied to leukemia and 6–7% better performance when applied to prostate cancer.Expert Systems with Applications 04/2011; 38(4-38):3209-3219. DOI:10.1016/j.eswa.2010.09.009 · 1.97 Impact Factor
Cytometry 10/1997; 30(5):236 - 244. DOI:10.1002/(SICI)1097-0320(19971015)30:5<236::AID-CYTO4>3.0.CO;2-F