Genomic characterization of multiple clinical phenotypes of cancer using multivariate linear regression models.
BACKGROUND
The development of gene expression microarray technology has allowed the identification of differentially expressed genes between different clinical phenotypic classes of cancer from a large pool of candidate genes. Although many class comparisons concerned only a single phenotype, simultaneous assessment of the relationship between gene expression and multiple phenotypes would be warranted to better understand the underlying biological structure.


RESULTS
We develop a method to select genes related to multiple clinical phenotypes based on a set of multivariate linear regression models. For each gene, we perform model selection based on the doubly-adjusted R-square statistic and use the maximum of this statistic for gene selection. The method can substantially improve the power in gene selection, compared with a conventional method that uses a single model exclusively for gene selection. Application to a bladder cancer study to correlate pre-treatment gene expressions with pathological stage and grade is given. The methods would be useful for screening for genes related to multiple clinical phenotypes.


BACKGROUND
SAS and MATLAB codes are available from author upon request.
Matsui
Shigeyuki
S
Department of Pharmacoepidemiology, Graduate School of Public Health, Kyoto University, Kyotom, Japan. matsui@pbh.med.kyoto-u.ac.jp
Ito
Masaaki
M
Nishiyama
Hiroyuki
H
Uno
Hajime
H
Kotani
Hirokazu
H
Watanabe
Jun
J
Guilford
Parry
P
Reeve
Anthony
A
Fukushima
Masanori
M
Ogawa
Osamu
O
D014408:Biomarkers, Tumor / Q000032:analysis*; D002874:Chromosome Mapping / Q000379:methods*; D003198:Computer Simulation; D003936:Diagnosis, Computer-Assisted / Q000379:methods; D020869:Gene Expression Profiling / Q000379:methods*; D006801:Humans; D016014:Linear Models; D008957:Models, Genetic; D015999:Multivariate Analysis; D009363:Neoplasm Proteins / Q000378:metabolism*; D020411:Oligonucleotide Array Sequence Analysis / Q000379:methods*; D010641:Phenotype; D012044:Regression Analysis; D001749:Urinary Bladder Neoplasms / Q000175:diagnosis / Q000235:genetics* / Q000378:metabolism*
D014408:Biomarkers, Tumor; D009363:Neoplasm Proteins