Biomedical subjects

Ioan Tabus

Publications and source records attributed to Ioan Tabus.

4 recordsLinked to original sources

PubMed2006

Pathway alterations during glioma progression revealed by reverse phase protein lysate arrays.

The progression of gliomas has been extensively studied at the genomic level using cDNA microarrays. However, systematic examinations at the protein translational and post-translational levels are far more limited. We constructed a glioma protein lysate array from 82 different primary glioma tissues, and surveyed the expression and phosphorylation of 46 different proteins involved in signaling pathways of cell proliferation, cell survival, apoptosis, angiogenesis, and cell invasion. An analysis algorithm was employed to robustly estimate the protein expressions in these samples. When ranked by their discriminating power to separate 37 glioblastomas (high-grade gliomas) from 45 lower-grade gliomas, the following 12 proteins were identified as the most powerful discriminators: IBalpha, EGFRpTyr845, AKTpThr308, phosphatidylinositol 3-kinase (PI3K), BadpSer136, insulin-like growth factor binding protein (IGFBP) 2, IGFBP5, matrix metalloproteinase 9 (MMP9), vascular endothelial growth factor (VEGF), phosphorylated retinoblastoma protein (pRB), Bcl-2, and c-Abl. Clustering analysis showed a close link between PI3K and AKTpThr308, IGFBP5 and IGFBP2, and IBalpha and EGFRpTyr845. Another cluster includes MMP9, Bcl-2, VEGF, and pRB. These clustering patterns may suggest functional relationships, which warrant further investigation. The marked association of phosphorylation of AKT at Thr308, but not Ser473, with glioblastoma suggests a specific event of PI3K pathway activation in glioma progression.

Adult↗

PubMed2005

Robust estimation of protein expression ratios with lysate microarray technology.

MOTIVATION: The protein lysate microarray is a developing proteomic technology for measuring protein expression levels in a large number of biological samples simultaneously. A challenge for accurate quantification is the relatively narrow dynamic range associated with the commonly used chromogenic signal detection system. To facilitate accurate measurement of the relative expression levels, each sample is serially diluted and each diluted version is spotted on a nitrocellulose-coated slide in triplicate. Thus, each sample yields multiple measurements in different dynamic ranges of the detection system. This study aims to develop suitable algorithms that yield accurate representations of the relative expression levels in different samples from multiple data points. RESULTS: We evaluated two algorithms for estimating relative protein expression in different samples on the lysate microarray by means of a cross-validation procedure. For this purpose as well as for quality control we designed a 1440-spot lysate microarray containing 80 identical samples of purified bovine serum albumin, printed in triplicate with six 2-fold dilutions. Our analysis showed that the algorithm based on a robust least squares estimator provided the most accurate quantification of the protein lysate microarray data. We also demonstrated our methods by estimating relative expression levels of p53 and p21 in either p53(+/+) or p53(-/-) HCT116 colon cancer cells after two drug treatments and their combinations on another lysate microarray. AVAILABILITY: http://www.cs.tut.fi/~mirceanc/lysate_array_bioinformatics.htm

Algorithms↗

PubMed2005

Molecular voting for glioma classification reflecting heterogeneity in the continuum of cancer progression.

Gliomas, the most common brain tumors, are generally categorized into two lineages (astrocytic and oligodendrocytic) and further classified as low-grade (astrocytoma and oligodendroglioma), mid-grade (anaplastic astrocytoma and anaplastic oligodendroglioma), and high-grade (glioblastoma multiforme) based on morphological features. A strict classification scheme has limitations because a specific glioma can be at any stage of the continuum of cancer progression and may contain mixed features. Thus, a more comprehensive classification based on molecular signatures may reflect the biological nature of specific tumors more accurately. In this study, we used microarray technology to profile the gene expression of 49 human brain tumors and applied the k-nearest neighbor algorithm for classification. We first trained the classification gene set with 19 of the most typical glioma cases and selected a set of genes that provide the lowest cross-validation classification error with k=5. We then applied this gene set to the 30 remaining cases, including several that do not belong to gliomas such as atypical meningioma. The results showed that not only does the algorithm correctly classify most of the gliomas, but the detailed voting results also provide more subtle information regarding the molecular similarities to neighboring classes. For atypical meningioma, the voting was equally split among the four classes, indicating a difficulty in placement of meningioma into the four classes of gliomas. Thus, the actual voting results, which are typically used only to decide the winning class label in k-nearest neighbor algorithms, provide a useful method for gaining deeper insight into the stage of a tumor in the continuum of cancer development.

Algorithms↗

PubMed2004

Pathway analysis of informative genes from microarray data reveals that metabolism and signal transduction genes distinguish different subtypes of lymphomas.

Recent clinicopathological studies identified a unique subgroup of diffuse large B-cell lymphoma (DLBCL) that expresses CD5 on the cell surface. This 'de novo CD5+ DLBCL' comprises 10% of all DLBCL and has a poorer prognosis than CD5- DLBCL. Comparison of gene expression profiles between de novo CD5+ DLBCLs and CD5- DLBCLs shows that de novo CD5+ DLBCL expresses high levels of integrin beta1 in tumor cells and CD36 in the vascular cells. On the other hand, comparison between mantle cell lymphomas (MCLs) and DLBCLs expectedly identified cyclin D1 as a top feature gene. To gain insight into the molecular pathway differences among the three types of lymphoma, we evaluated the functional categories of groups of genes important for the discrimination among the three groups. We first selected 280 (from 2,142) genes, according to their individual discriminatory power. We then used the gene-shaving clustering algorithm and identified 22 clusters of genes. Of the 22 clusters, six were highly correlated with the class labels of the patients and the top three clusters accounted for the major difference among the three lymphoma subtypes. A multidimensional scaling (MDS) analysis using the average genes from the top three clusters separated the three lymphoma subtypes quite well. The functions of the genes in the top three gene clusters showed a significant enrichment of metabolism and signal transduction. To further examine whether genes of particular functions reflect more faithfully the difference between the subtypes of lymphomas, we separated the 280 informative genes into six different functional groups and performed MDS analysis using each of the gene groups. Four of the gene-function groups (metabolism, signal transduction pathway, transcriptional factors, cell adhesion and migration), separated the three lymphoma subtypes well, whereas apoptosis genes and cell cycle genes did not result in good separation.

Algorithms↗