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

研究成果: Article

20 引用 (Scopus)

抄録

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.

元の言語English
ページ(範囲)534-544
ページ数11
ジャーナルJournal of Computational Biology
12
発行部数5
DOI
出版物ステータスPublished - 2005 6
外部発表Yes

Fingerprint

Gene expression
Genes
Modulators

ASJC Scopus subject areas

  • Molecular Biology
  • Genetics

これを引用

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

:: Journal of Computational Biology, 巻 12, 番号 5, 06.2005, p. 534-544.

研究成果: Article

@article{2d332b4a647c4651be7c88ab486f2940,
title = "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",
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.",
keywords = "Lung adenocarcinomas, Machine learning, Microarrays, Survival tree",
author = "Daniel Berrar and Brian Sturgeon and Ian Bradbury and Downes, {C. Stephen} and Werner Dubitzky",
year = "2005",
month = "6",
doi = "10.1089/cmb.2005.12.534",
language = "English",
volume = "12",
pages = "534--544",
journal = "Journal of Computational Biology",
issn = "1066-5277",
publisher = "Mary Ann Liebert Inc.",
number = "5",

}

TY - JOUR

T1 - Survival trees for analyzing clinical outcome in lung adenocarcinomas based on gene expression profiles

T2 - Identification of neogenin and diacylglycerol kinase α expression as critical factors

AU - Berrar, Daniel

AU - Sturgeon, Brian

AU - Bradbury, Ian

AU - Downes, C. Stephen

AU - Dubitzky, Werner

PY - 2005/6

Y1 - 2005/6

N2 - 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.

AB - 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.

KW - Lung adenocarcinomas

KW - Machine learning

KW - Microarrays

KW - Survival tree

UR - http://www.scopus.com/inward/record.url?scp=21044447248&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=21044447248&partnerID=8YFLogxK

U2 - 10.1089/cmb.2005.12.534

DO - 10.1089/cmb.2005.12.534

M3 - Article

C2 - 15952876

AN - SCOPUS:21044447248

VL - 12

SP - 534

EP - 544

JO - Journal of Computational Biology

JF - Journal of Computational Biology

SN - 1066-5277

IS - 5

ER -