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      Gene Expression Profiling Predicts Survival in Conventional Renal Cell Carcinoma

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          Abstract

          Background

          Conventional renal cell carcinoma (cRCC) accounts for most of the deaths due to kidney cancer. Tumor stage, grade, and patient performance status are used currently to predict survival after surgery. Our goal was to identify gene expression features, using comprehensive gene expression profiling, that correlate with survival.

          Methods and Findings

          Gene expression profiles were determined in 177 primary cRCCs using DNA microarrays. Unsupervised hierarchical clustering analysis segregated cRCC into five gene expression subgroups. Expression subgroup was correlated with survival in long-term follow-up and was independent of grade, stage, and performance status. The tumors were then divided evenly into training and test sets that were balanced for grade, stage, performance status, and length of follow-up. A semisupervised learning algorithm (supervised principal components analysis) was applied to identify transcripts whose expression was associated with survival in the training set, and the performance of this gene expression-based survival predictor was assessed using the test set. With this method, we identified 259 genes that accurately predicted disease-specific survival among patients in the independent validation group ( p < 0.001). In multivariate analysis, the gene expression predictor was a strong predictor of survival independent of tumor stage, grade, and performance status ( p < 0.001).

          Conclusions

          cRCC displays molecular heterogeneity and can be separated into gene expression subgroups that correlate with survival after surgery. We have identified a set of 259 genes that predict survival after surgery independent of clinical prognostic factors.

          Abstract

          Molecular heterogeneity of renal cell carcinomas can be used to distinguish subgroups that correlated with long term survival.

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          Most cited references20

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          Cluster analysis and display of genome-wide expression patterns.

          A system of cluster analysis for genome-wide expression data from DNA microarray hybridization is described that uses standard statistical algorithms to arrange genes according to similarity in pattern of gene expression. The output is displayed graphically, conveying the clustering and the underlying expression data simultaneously in a form intuitive for biologists. We have found in the budding yeast Saccharomyces cerevisiae that clustering gene expression data groups together efficiently genes of known similar function, and we find a similar tendency in human data. Thus patterns seen in genome-wide expression experiments can be interpreted as indications of the status of cellular processes. Also, coexpression of genes of known function with poorly characterized or novel genes may provide a simple means of gaining leads to the functions of many genes for which information is not available currently.
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            HIFalpha targeted for VHL-mediated destruction by proline hydroxylation: implications for O2 sensing.

            HIF (hypoxia-inducible factor) is a transcription factor that plays a pivotal role in cellular adaptation to changes in oxygen availability. In the presence of oxygen, HIF is targeted for destruction by an E3 ubiquitin ligase containing the von Hippel-Lindau tumor suppressor protein (pVHL). We found that human pVHL binds to a short HIF-derived peptide when a conserved proline residue at the core of this peptide is hydroxylated. Because proline hydroxylation requires molecular oxygen and Fe(2+), this protein modification may play a key role in mammalian oxygen sensing.
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              Mutations of the VHL tumour suppressor gene in renal carcinoma.

              Multiple, bilateral renal carcinomas are a frequent occurrence in von Hippel-Lindau (VHL) disease. To elucidate the aetiological role of the VHL gene in human kidney tumorigenesis, localized and advanced tumours from 110 patients with sporadic renal carcinoma were analysed for VHL mutations and loss of heterozygosity (LOH). VHL mutations were identified in 57% of clear cell renal carcinomas analysed and LOH was observed in 98% of those samples. Moreover, VHL was mutated and lost in a renal tumour from a patient with familial renal carcinoma carrying the constitutional translocation, t(3;8)(p14;q24). The identification of VHL mutations in a majority of localized and advanced sporadic renal carcinomas and in a second form of hereditary renal carcinoma indicates that the VHL gene plays a critical part in the origin of this malignancy.
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                Author and article information

                Contributors
                Role: Academic Editor
                Journal
                PLoS Med
                pmed
                PLoS Medicine
                Public Library of Science (San Francisco, USA )
                1549-1277
                1549-1676
                January 2006
                6 December 2005
                : 3
                : 1
                : e13
                Affiliations
                [1] 1Department of Urology, Stanford University School of Medicine, Stanford, California, United States of America
                [2] 2Departments of Surgical and Perioperative Sciences, Urology, and Andrology, Medical Biosciences, Clinical Chemistry, and Radiation Sciences, Oncology, Umeȧ University, Umeȧ, Sweden
                [3] 3Department of Health Research and Policy, Stanford University School of Medicine, Stanford, California, United States of America
                National Institutes of Health United States of America
                Author notes
                *To whom correspondence should be addressed. E-mail: jdbrooks@ 123456stanford.edu

                Competing Interests: The authors have declared that no competing interests exist.

                Author Contributions: BL and JDB designed the study. HZ, BL, KG, and TR performed the experiments. HZ, RT, and JDB analyzed the data. HZ, BL, RT, and JDB contributed to writing the paper.

                Article
                10.1371/journal.pmed.0030013
                1298943
                16318415
                c490a64a-5ff6-40ca-acc2-bcc34ff7add5
                Copyright: © 2006 Zhao et al. This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
                History
                : 18 July 2005
                : 12 October 2005
                Categories
                Research Article
                Bioinformatics/Computational Biology
                Biotechnology
                Cancer Biology
                Genetics/Genomics/Gene Therapy
                Medical Informatics
                Nephrology
                Oncology
                Pathology
                Urology
                Genetics
                Medical Informatics
                Oncology
                Pathology
                Renal Medicine

                Medicine
                Medicine

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