Transcription profiling of renal cell carcinoma.
ABSTRACT Our aim was to prepare a comprehensive catalogue of the changes in gene expression accompanying the development and progression of renal cell carcinoma, and to correlate these with histo-pathological, cytogenetic and clinical findings.
mRNA samples from paired neoplastic and non-cancerous human kidney tissue were labeled and hybridized in duplicate against high-density cDNA arrays. Two array technologies were used: 31,500-element transcriptome-wide nylon arrays for hybridization with 37 radioactively labelled sample pairs, and 4200-element kidney- and cancer-specific glass microarrays for hybridization with 19 fluorescently labelled sample pairs.
We identified more than 1700 cDNA clones that show differential transcription levels in kidney tumor tissue compared to normal kidney tissue. The functional classification of 389 annotated genes provided views of the changes in the activities of specific biological processes in renal cancer. Among the biological processes with a large proportion of up-regulated genes we found cell adhesion, signal transduction, and nucleotide metabolism. Down-regulated processes included small molecule transport, ion homeostasis, and oxygen and radical metabolism. Furthermore, we explored the feasibility of molecular diagnosis for renal cell tumors using cDNA microarrays on glass slides, investigating the association of transcription levels with tumor type, progression, and a putative prognostic variable. The experimental data is available from the GEO gene expression database (http://www.ncbi.nlm.nih.gov/geo; accession no. GSE3), and a comprehensive presentation of the results is available in the web supplement (http://www.dkfz-heidelberg.de/abt0840/whuber/rcc).
Transcription profiling using high-density cDNA arrays is a powerful method with the potential to improve cancer diagnosis and prognosis. The identification and classification of differentially transcribed genes, as described in our study, is the beginning of a more complete understanding of kidney cancer.
- SourceAvailable from: Patrizia Boracchi
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- "The Kidney and Dilution data sets available in the vsn and affydata Bioconductor packages (Huber et al., 2002b; Bioconductor, 2006) were selected for the analysis. The Kidney data set is a cDNA slide with 8704 spots of two adjacent tissue samples from a nephrectomy, published by Huber et al. (2002a). For each spot, a background estimate from a surrounding region was subtracted. "
ABSTRACT: Current gene intensity-dependent normalization methods, based on regression smoothing techniques, usually approach the two problems of reducing location bias and data rescaling without taking into account the censoring that is characteristic of certain gene expressions, produced by experimental measurement constraints or by previous normalization steps. Moreover, control of normalization procedures for balancing bias versus variance is often left to the user's experience. An approximate maximum likelihood procedure for fitting a model smoothing the dependences of log-fold gene expression differences on average gene intensities is presented. Central tendency and scaling factor are modeled by means of the B-spline smoothing technique. As an alternative to the outlier theory and robust methods, the approach presented looks for suitable distributional models, possibly generalizing the classical Gaussian and Laplacian assumptions, controlling for different types of censoring. The Bayesian information criterion is adopted for model selection. Distributional assumptions are tested using goodness-of-fit statistics and Monte Carlo evaluation. Randomization quantiles are proposed to produce normally distributed adjusted data. Three publicly available data sets are analyzed for demonstration purposes. Student's t error models reveal best performances in all of the data sets considered. More validating evidence is needed to evaluate the Asymmetric Laplace distribution, which showed interesting results in one data set.Computational Statistics & Data Analysis 03/2009; 53(5):1906-1922. DOI:10.1016/j.csda.2008.11.026 · 1.15 Impact Factor
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ABSTRACT: Proteome analysis has rapidly developed in the post-genome era and is now widely accepted as the complementary technology for genetic profiling. It has been shown to be a powerful tool for studying human diseases and for identifying novel prognostic, diagnostic and therapeutic markers. This review focuses on the identification of new biomarkers and therapeutic targets for renal cell carcinoma using different 'ome'-based technologies.Briefings in Functional Genomics and Proteomics 11/2003; 2(3):194-212.
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ABSTRACT: The molecular analysis of serum is an important field for the definition of potential diagnostic markers or disease-related protein alterations. Novel proteomic technologies such as the mass spectrometric-based surface-enhanced laser desorption/ionization (SELDI) ProteinChip technique facilitate a rapid and reproducible analysis of such protein mixtures and affords the researcher a new dimension in the search for biomarkers of disease. Here, we have applied this technology to the study of a cohort of serum samples from well-characterized renal cell carcinoma patients for the identification of such proteins by comparison to healthy controls. We detected and characterized haptoglobin 1 alpha and serum amyloid alpha-1 (SAA-1) as disease related, in addition to an as-yet-unidentified marker of 10.84 kDa. Of particular note is the detection of multiple variants of SAA-1 in multiplex that have not been described in the sera of cancer patients. SAA-1 is detected as full-length protein, des-Arginine and des-Arginine/des-Serine variants at the N terminus by SELDI. In addition, we could also detect a low-abundant variant minus the first five N-terminal amino acids. Such variants may impact the function of the protein. We conclude the technique to be a reproducible, fast and simple mode for the discovery and analysis of marker proteins of disease in serum.Laboratory Investigation 08/2004; 84(7):845-56. DOI:10.1038/labinvest.3700097 · 3.83 Impact Factor