Survival trees for analyzing clinical outcome in lung adenocarcinomas based on gene expression profiles: Identification of neogenin and diacylglycerol kinase α expression as critical factors

Daniel Berrar, Brian Sturgeon, Ian Bradbury, C. Stephen Downes, Werner Dubitzky

Research output: Contribution to journalArticle

23 Citations (Scopus)

Abstract

We present survival trees as an exploratory tool for revealing new insights into gene expression profiles in combination with clinical patient data. Survival trees partition the patient data studied into groups with similar survival outcomes and identify characteristic genetic profiles within these groups. We demonstrate the application of survival trees in a study involving the expression profiles of 3,588 genes in 211 lung adenocarcinoma patients. The survival tree identified a group of early-stage cancer patients with relatively low survival rates and another group of advanced-stage patients with remarkably good survival outcome. For both groups, the tree identified characteristic expression profiles of genes that might play a role in cancerogenesis and disease progression, notably the genes for the netrin receptor neogenin and the Ras/Rho kinase modulator diacylglycerol kinase α.

Original languageEnglish
Pages (from-to)534-544
Number of pages11
JournalJournal of Computational Biology
Volume12
Issue number5
DOIs
Publication statusPublished - 2005 Jun 1

Keywords

  • Lung adenocarcinomas
  • Machine learning
  • Microarrays
  • Survival tree

ASJC Scopus subject areas

  • Modelling and Simulation
  • Molecular Biology
  • Genetics
  • Computational Mathematics
  • Computational Theory and Mathematics

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