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Untitled Document
CANCER GENOMICS & PROTEOMICS
Volume 4, Number
4, July-August 2007
| CONTENTS |
PAGE |
| ABSTRACTS OF THE SECOND INTERNATIONAL CONFERENCE OF THE HELLENIC PROTEOMICS SOCIETY. J.A. HALL, R. BROWN, J. PAUL (Glasgow, UK) |
255 |
| 25-Hydroxyvitamin D3 1α-Hydroxylase Splice Variants in Breast Cell Lines MCF-7 and MCF-10. D. FISCHER, M. SEIFERT, S. BECKER, D. LÜDDERS, T. CORDES, J. REICHRATH, M. FRIEDRICH (Lübeck; Homburg, Germany) |
295 |
| *Interpreting Microarray Data: Towards the Complete Bioinformatics Toolkit for Cancer. M.L. ROBERTS, S.D. KOTTARIDIS (Athens, Greece) |
301 |
| Microarray Analysis of Survival Pathways in Human PC-3 Prostate Cancer Cells. R. TENTA, H. KATOPODIS, A. CHATZIIOANNOU, E. PILALIS, E. CALVO, V. LUU-THE, F. LABRIE, F. KOLISIS, M. KOUTSILIERIS (Athens, Greece; Québec, QC, Canada) |
309 |
*Review
CANCER GENOMICS & PROTEOMICS 4: 255-294 (2007)
ABSTRACTS OF THE SECOND INTERNATIONAL CONFERENCE OF THE HELLENIC PROTEOMICS SOCIETY
From Discovery to Application
Abstract:
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CANCER GENOMICS & PROTEOMICS 4: 295-300 (2007)
25-Hydroxyvitamin D3 1á-Hydroxylase Splice Variants in Breast Cell Lines MCF-7 and MCF-10
D. FISCHER1, M. SEIFERT2, S. BECKER1, D. LUDDERS1, T. CORDES1,
J. REICHRATH2 and M. FRIEDRICH1
1Klinik für Frauenheilkunde und Geburtshilfe, Universitätsklinikum Schleswig-Holstein,
Campus Lübeck, Lübeck;
2Klinik für Dermatologie, Allergologie und Venerologie, Universitätsklinik des Saarlandes,
Homburg, Germany
Abstract: Background: It is known that 25(OH)D3 can be metabolized to 1,25(OH)2D3 by 1á-OHase in breast tissue. This tissue-specific expression of 1á-OHase may act as the pivotal link between vitamin D status (25(OH)D3 levels) and the anticancer effects of 1,25(OH)2D3. Alternative splicing frequently occurs in breast cancer cells; different splice variants of a given protein can display different biological functions and may cause tissue-specific variations. With this study it is the first time that expression and alternative splicing of 1á-OHase in the human breast cancer cell line MCF-7 and thebenign breast cell line MCF-10A are described. Materials and Methods: Expression of 1á-OHase RNA and protein was assessed using a real-time polymerase chain reaction (RT-PCR). The expression of 1á-OHase splice variants was detected by a highly specific PCR that combines nested and touchdown PCR. To determine which variants are translated in protein western blot analysis was carried out. Results: The expression of 1á-OHase was found to be 1.25-fold higher in MCF-7 compared to MCF-10A cells. In MCF-10A cells, at least 6 splice variants were detected whereas MCF-7 showed no or marginal expression levels of these variants. In MCF-7 cells the antibody detected a signal at 56 kDa corresponding to the size of normal 1á-OHase protein. In MCF-10A cells this signal was weaker. In western blot analysis at least two smaller variants at 45 kDa were found in MCF-7 cells. In MCF-10A cells at least 6 proteins between 37 and 56 kDa were detected with an only faint signal. Conclusion: We propose that alternative splicing of 1á-OHase can regulate the level of active enzyme. Splice variants may lead to a reduction of the protein. The significance of the smaller variants in MCF-7 cells has not been clarified either, but it is known that they are not able to use 25(OH)D3 as a substrate to generate 1,25(OH)D3. In MCF10A cells, more splice variants were identified, it may be that malignant cells contain inactive variants. How far they show a reduced activity remains unclear as no activity measurements were performed.
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CANCER GENOMICS & PROTEOMICS 4: 301-308 (2007)
Interpreting Microarray Data: Towards the Complete Bioinformatics Toolkit for Cancer
MICHAEL L. ROBERTS and STAVROS D. KOTTARIDIS
Regulon A.E., Afxentiou 7, Athens 11525, Greece
Abstract: Functional genomics has been applied in the study of human malignancies since the inception of this field nearly a decade ago. Microarray analysis has been specifically used in an attempt to reclassify carcinomas at the molecular level, to aid in diagnosis/prognosis and to predict how various types of tumour respond to different therapeutic agents. Bioinformatics is now at the forefront of the post-genomics era and is providing a number of tools with which to mine the large datasets produced by genome-wide analysis. Of particular importance is the emergence of techniques that give the ability to reveal the transcription regulatory networks that are active in the cell in response to environmental stimuli or disease states. Deciphering the transcription networks that function in malignant cells not only will provide the knowledge to understand how carcinomas progress, but would also allow the construction of useful therapeutic tools for their effective treatment. In this review the recent advances that have been made in functional genomics that allow microarray data to be more fully interpreted and reveal the transcription networks that have gone awry in transformed cells are described.
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CANCER GENOMICS & PROTEOMICS 4: 309-318 (2007)
Microarray Analysis of Survival Pathways in Human PC-3 Prostate Cancer Cells
R. TENTA1, H. KATOPODIS1, A. CHATZIIOANNOU2, E. PILALIS2, E. CALVO3,
VAN LUU-THE3, F. LABRIE3, F. KOLISIS2 and M. KOUTSILIERIS1
1Department of Experimental Physiology, Medical School, University of Athens;
2Metabolic Engineering and Bioinformatics Group, Institute of Biological Research and Biotechnology, National Hellenic Research Foundation;
3Molecular Endocrinology and Oncology Research Center,
Laval University Research Center, (CHUL), Quebec, Canada
Abstract: Background: Insulin-like growth factor 1 (IGF-1), transforming growth factor beta 1 (TGFâ1), and interleukin 6 (IL-6), act as survival factors inhibiting chemotherapy-induced apoptosis in PC-3 human prostate cancer cells, in vitro. Materials and Methods: To study the intracellular pathways activated by these survival factors we performed a comparative genomic analysis using oligonucleotide microarray chips. A validation by real time-PCR was also performed for the genes of interest. Results: The expression data derived were analysed using various normalization algorithms. The differentially expressed genes were clustered and their ontological annotations were statistically tested to provide evidence for possible deregulated biological processes on the action of the aforementioned survival factors. Emphasis was given on the regulation and the role of the genes AKR1C1, SDPR and GADD45B in the survival pathways of prostate cancer cells, whose expression was also validated by real time-PCR. Conclusion: The overall analyses reveal an overrepresentation of differentially expressed genes related to cellular processes such as cell cycle regulation, lipid metabolism and steroid biosynthesis.
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