title: Genomic characterization of multiple clinical phenotypes of cancer using multivariate linear regression models.
abstract: 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.
authors.affiliation: Department of Pharmacoepidemiology, Graduate School of Public Health, Kyoto University, Kyotom, Japan. matsui@pbh.med.kyoto-u.ac.jp
authors.forename: Shigeyuki
authors.initials: S
authors.lastname: Matsui
authors.forename: Masaaki
authors.initials: M
authors.lastname: Ito
authors.forename: Hiroyuki
authors.initials: H
authors.lastname: Nishiyama
authors.forename: Hajime
authors.initials: H
authors.lastname: Uno
authors.forename: Hirokazu
authors.initials: H
authors.lastname: Kotani
authors.forename: Jun
authors.initials: J
authors.lastname: Watanabe
authors.forename: Parry
authors.initials: P
authors.lastname: Guilford
authors.forename: Anthony
authors.initials: A
authors.lastname: Reeve
authors.forename: Masanori
authors.initials: M
authors.lastname: Fukushima
authors.forename: Osamu
authors.initials: O
authors.lastname: Ogawa
mesh_terms: 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*
chemical_list: D014408:Biomarkers, Tumor; D009363:Neoplasm Proteins