title: Profiling alternatively spliced mRNA isoforms for prostate cancer classification.
abstract: BACKGROUND
  Prostate cancer is one of the leading causes of cancer illness and death among men in the United States and world wide. There is an urgent need to discover good biomarkers for early clinical diagnosis and treatment. Previously, we developed an exon-junction microarray-based assay and profiled 1532 mRNA splice isoforms from 364 potential prostate cancer related genes in 38 prostate tissues. Here, we investigate the advantage of using splice isoforms, which couple transcriptional and splicing regulation, for cancer classification.
  
  
  RESULTS
  As many as 464 splice isoforms from more than 200 genes are differentially regulated in tumors at a false discovery rate (FDR) of 0.05. Remarkably, about 30% of genes have isoforms that are called significant but do not exhibit differential expression at the overall mRNA level. A support vector machine (SVM) classifier trained on 128 signature isoforms can correctly predict 92% of the cases, which outperforms the classifier using overall mRNA abundance by about 5%. It is also observed that the classification performance can be improved using multivariate variable selection methods, which take correlation among variables into account.
  
  
  CONCLUSIONS
  These results demonstrate that profiling of splice isoforms is able to provide unique and important information which cannot be detected by conventional microarrays.
authors.affiliation: Cold Spring Harbor Laboratory, Cold Spring Harbor, NY 11724, USA. zhangc@cshl.edu
authors.forename: Chaolin
authors.initials: C
authors.lastname: Zhang
authors.forename: Hai-Ri
authors.initials: HR
authors.lastname: Li
authors.forename: Jian-Bing
authors.initials: JB
authors.lastname: Fan
authors.forename: Jessica
authors.initials: J
authors.lastname: Wang-Rodriguez
authors.forename: Tracy
authors.initials: T
authors.lastname: Downs
authors.forename: Xiang-Dong
authors.initials: XD
authors.lastname: Fu
authors.forename: Michael Q
authors.initials: MQ
authors.lastname: Zhang
mesh_terms: D000465:Algorithms; D017398:Alternative Splicing / Q000235:genetics; D014408:Biomarkers, Tumor / Q000235:genetics / Q000378:metabolism*; D003936:Diagnosis, Computer-Assisted / Q000379:methods*; D020869:Gene Expression Profiling / Q000379:methods*; D006801:Humans; D008297:Male; D009363:Neoplasm Proteins / Q000235:genetics / Q000378:metabolism*; D011471:Prostatic Neoplasms / Q000145:classification* / Q000175:diagnosis / Q000235:genetics* / Q000378:metabolism; D020033:Protein Isoforms / Q000235:genetics / Q000378:metabolism; D012333:RNA, Messenger / Q000235:genetics; D015203:Reproducibility of Results; D012680:Sensitivity and Specificity; D017423:Sequence Analysis, RNA / Q000379:methods
chemical_list: D014408:Biomarkers, Tumor; D009363:Neoplasm Proteins; D020033:Protein Isoforms; D012333:RNA, Messenger