12 research outputs found

    Immunization with UV-induced apoptotic cells generates monoclonal antibodies against proteins differentially expressed in hepatocellular carcinoma cell lines

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    Early and differential diagnosis of hepatocellular carcinoma (HCC) requires sensitive and specific tissue and serum markers. On the other hand, proteins involved in tumorigenesis are extensively modelated on exposure to apoptotic stimuli, including ultraviolet (UVC) irradiation. Hence, we generated monoclonal antibodies by using UVC-irradiated apoptotic cells of an HCC cell line, HUH7, aiming to explore proteins differentially expressed in tumors and apoptosis. We obtained 18 hybridoma clones recognizing protein targets in apoptotic HUH7 cells, and clone 6D5 was chosen for characterization studies because of its strong reactivity in cell-ELISA assay. Subtype of the antibody was IgG3 (κ). Targets of 6D5 antibody were found to be abundantly expressed in all HCC cell lines except FLC4, which resembles normal hepatocytes. We also observed the secretion of 6D5 ligands by some of the HCC cell lines. Moreover, cellular proteins recognized by the antibody displayed a late upregulation in UVC-induced apoptotic cells. We concluded that 6D5 target proteins are modulated in liver tumorigenesis and apoptotic processes. We therefore propose the validation of our antibody in tissue and serum samples of HCC patients to assess its potential use for the early diagnosis of HCC and to understand the role of 6D5 ligands in liver carcinogenesis. © Mary Ann Liebert, Inc

    Quantification of SLIT-ROBO transcripts in hepatocellular carcinoma reveals two groups of genes with coordinate expression

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    <p>Abstract</p> <p>Background</p> <p>SLIT-ROBO families of proteins mediate axon pathfinding and their expression is not solely confined to nervous system. Aberrant expression of <it>SLIT-ROBO </it>genes was repeatedly shown in a wide variety of cancers, yet data about their collective behavior in hepatocellular carcinoma (HCC) is missing. Hence, we quantified <it>SLIT-ROBO </it>transcripts in HCC cell lines, and in normal and tumor tissues from liver.</p> <p>Methods</p> <p>Expression of <it>SLIT-ROBO </it>family members was quantified by real-time qRT-PCR in 14 HCC cell lines, 8 normal and 35 tumor tissues from the liver. ANOVA and Pearson's correlation analyses were performed in R environment, and different clinicopathological subgroups were pairwise compared in Minitab. Gene expression matrices of cell lines and tissues were analyzed by Mantel's association test.</p> <p>Results</p> <p>Genewise hierarchical clustering revealed two subgroups with coordinate expression pattern in both the HCC cell lines and tissues: <it>ROBO1</it>, <it>ROBO2</it>, <it>SLIT1 </it>in one cluster, and <it>ROBO4</it>, <it>SLIT2</it>, <it>SLIT3 </it>in the other, respectively. Moreover, <it>SLIT-ROBO </it>expression predicted <it>AFP</it>-dependent subgrouping of HCC cell lines, but not that of liver tissues. <it>ROBO1 </it>and <it>ROBO2 </it>were significantly up-regulated, whereas <it>SLIT3 </it>was significantly down-regulated in cell lines with high-<it>AFP </it>background. When compared to normal liver tissue, <it>ROBO1 </it>was found to be significantly overexpressed, while <it>ROBO4 </it>was down-regulated in HCC. We also observed that <it>ROBO1 </it>and <it>SLIT2 </it>differentiated histopathological subgroups of liver tissues depending on both tumor staging and differentiation status. However, <it>ROBO4 </it>could discriminate poorly differentiated HCC from other subgroups.</p> <p>Conclusion</p> <p>The present study is the first in comprehensive and quantitative evaluation of <it>SLIT-ROBO </it>family gene expression in HCC, and suggests that the expression of <it>SLIT-ROBO </it>genes is regulated in hepatocarcinogenesis. Our results implicate that <it>SLIT-ROBO </it>transcription profile is bi-modular in nature, and that each module shows intrinsic variability. We also provide quantitative evidence for potential use of <it>ROBO1</it>, <it>ROBO4 </it>and <it>SLIT2 </it>for prediction of tumor stage and differentiation status.</p
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