Bladder carcinoma (B-TCC) is the fifth most prevalent carcinoma in the United States (US) or Europe. In addition, B-TCC is the most expensive carcinoma per patient between diagnosis and death, because of its 50-80 % r...
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Bladder carcinoma (B-TCC) is the fifth most prevalent carcinoma in the United States (US) or Europe. In addition, B-TCC is the most expensive carcinoma per patient between diagnosis and death, because of its 50-80 % recurrence rate. B-TCC is an optimal carcinoma for which to detect DNA alterations in urine, which is easily obtainable. Chromosomal aberrations in tumors have been closely related to the carcinogenesis process. We developed a highly specific and sensitive oligo-cgh-array for the diagnosis and follow-up of B-TCC, based on the detection of chromosomal aberrations in urine samples. One hundred and sixty-four urine samples were analyzed. The qualitative results, including chromosomal aberrations, were obtained. Quantitative results are expressed as a percentage of chromosomal alterations on the autosomes. From the urine samples, we were able to differentiate B-TCC from non-malignant conditions with an accuracy of 100 % for patients without history of B-TCC. For follow-up of B-TCC in clinical practice, at least a deletion (8p;9p;9q) or a cut-off of > 2 % of chromosomal imbalance was considered as a positive test. According to our criteria, 100 % of high-grade tumors were diagnosed, and the sensitivity to predict positive cystoscopy was 95 % (specificity 73 %). A cut-off > 9 % was a strong signature of high-grade TCC (odds ratio 53 CI 95 % 7-417;p = 0.0002). We developed a sensitive clinical tool for the detection of B-TCC using DNA extracted from patient urine. This tool is also able to identify low-grade B-TCC and identify high-risk patients harboring a high-grade disease.
The Gene Expression Profile Analysis Suite, GEPAS, has been running for more than three years. With > 76000 experiments analysed during the last year and a daily average of almost 300 analyses, GEPAS can be conside...
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The Gene Expression Profile Analysis Suite, GEPAS, has been running for more than three years. With > 76000 experiments analysed during the last year and a daily average of almost 300 analyses, GEPAS can be considered a well-established and widely used platform for gene expression microarray data analysis. GEPAS is oriented to the analysis of whole series of experiments. Its design and development have been driven by the demands of the biomedical community, probably the most active collective in the field of microarray users. Although clustering methods have obviously been implemented in GEPAS, our interest has focused more on methods for finding genes differentially expressed among distinct classes of experiments or correlated to diverse clinical outcomes, as well as on building predictors. There is also a great interest in cgh-arrays which fostered the development of the corresponding tool in GEPAS: InSilicocgh. Much effort has been invested in GEPAS for developing and implementing efficient methods for functional annotation of experiments in the proper statistical framework. Thus, the popular FatiGO has expanded to a suite of programs for functional annotation of experiments, including information on transcription factor binding sites, chromosomal location and tissues. The web-based pipeline for microarray gene expression data, GEPAS, is available at http://***.
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