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 language | English |
|---|---|
| Pages (from-to) | 534-544 |
| Number of pages | 11 |
| Journal | Journal of Computational Biology |
| Volume | 12 |
| Issue number | 5 |
| DOIs | |
| Publication status | Published - 2005 Jun |
| Externally published | Yes |
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Keywords
- Lung adenocarcinomas
- Machine learning
- Microarrays
- Survival tree
ASJC Scopus subject areas
- Molecular Biology
- Genetics
Cite this
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.
In: Journal of Computational Biology, Vol. 12, No. 5, 06.2005, p. 534-544.Research output: Contribution to journal › Article
}
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
VL - 12
SP - 534
EP - 544
JO - Journal of Computational Biology
JF - Journal of Computational Biology
SN - 1066-5277
IS - 5
ER -