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Gene Expression Profiling Predicts Survival in Conventional Renal Cell Carcinoma

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  • Hongjuan Zhao
  • Börje Ljungberg
  • Kjell Grankvist
  • Torgny Rasmuson
  • Robert Tibshirani
  • James D Brooks

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

Suggested Citation

  • Hongjuan Zhao & Börje Ljungberg & Kjell Grankvist & Torgny Rasmuson & Robert Tibshirani & James D Brooks, 2005. "Gene Expression Profiling Predicts Survival in Conventional Renal Cell Carcinoma," PLOS Medicine, Public Library of Science, vol. 3(1), pages 1-1, December.
    • Handle: RePEc:plo:pmed00:0030013
    DOI: 10.1371/journal.pmed.0030013
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    References listed on IDEAS

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    1. Eric Bair & Robert Tibshirani, 2004. "Semi-Supervised Methods to Predict Patient Survival from Gene Expression Data," PLOS Biology, Public Library of Science, vol. 2(4), pages 1-1, April.
    2. Charles M. Perou & Therese Sørlie & Michael B. Eisen & Matt van de Rijn & Stefanie S. Jeffrey & Christian A. Rees & Jonathan R. Pollack & Douglas T. Ross & Hilde Johnsen & Lars A. Akslen & Øystein Flu, 2000. "Molecular portraits of human breast tumours," Nature, Nature, vol. 406(6797), pages 747-752, August.
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