title: Non-overlapping and non-cell-type-specific gene expression signatures predict lung cancer survival.
abstract: OBJECTIVE
  Gene expression profiling for outcome prediction of non-small-cell lung cancer (NSCLC) remains clouded by heterogeneous and unvalidated results. This study applied multivariate approaches to identify and evaluate value-added gene expression signatures in two types of NSCLC.
  
  
  METHODS
  Two NSCLC oligonucleotide microarray data sets of adenocarcinoma and squamous cell carcinoma were used as training sets to select prognostic genes independent of conventional predictors. The top 50 genes from each set were used to predict the outcomes of two independent validation data sets of 84 and 91 NSCLC cases.
  
  
  RESULTS
  Adenocarcinomas with the 50-gene signature from adenocarcinoma in both validation data sets had a 2.4-fold (95% CI, 1.3 to 4.4 and 1.0 to 5.8) increased mortality after adjustment for conventional predictors. Squamous cell carcinoma with this high-risk signature had an adjusted risk of 1.1 (95% CI, 0.4 to 3.2) in one data set and 2.5 (95% CI, 1.1 to 5.8) in another consisting of stage I tumors. Adenocarcinoma with the 50-gene signature from squamous cell carcinoma had an elevated risk of 3.5 (95% CI, 1.4 to 9.0) after adjustment for conventional predictors. Squamous cell carcinoma with this high risk signature had an adjusted risk of 1.8 (95% CI, 0.7 to 4.6). Despite the little overlap in individual genes, the two gene signatures had significant functional connectedness in molecular pathways.
  
  
  CONCLUSIONS
  Two non-overlapping but functionally related gene expression signatures provide consistently improved survival prediction for NSCLC regardless of histologic cell type. Multiple sets of genes may exist for NSCLC with predictive value, but ones with independent predictive value beyond clinical predictors will be required for clinical translation.
authors.affiliation: Department of Health Sciences Research, College of Medicine, Mayo Clinic, 200 First St SW, Rochester, MN 55905, USA. sun.zhifu@mayo.edu
authors.forename: Zhifu
authors.initials: Z
authors.lastname: Sun
authors.forename: Dennis A
authors.initials: DA
authors.lastname: Wigle
authors.forename: Ping
authors.initials: P
authors.lastname: Yang
mesh_terms: D000230:Adenocarcinoma / Q000235:genetics* / Q000401:mortality*; D002282:Adenocarcinoma, Bronchiolo-Alveolar / Q000235:genetics / Q000401:mortality; D002294:Carcinoma, Squamous Cell / Q000235:genetics* / Q000401:mortality*; D005260:Female; D020869:Gene Expression Profiling; D015972:Gene Expression Regulation, Neoplastic; D006801:Humans; D053208:Kaplan-Meier Estimate; D008175:Lung Neoplasms / Q000235:genetics* / Q000401:mortality* / Q000473:pathology; D008297:Male; D008875:Middle Aged; D015999:Multivariate Analysis; D011237:Predictive Value of Tests; D016016:Proportional Hazards Models; D012372:ROC Curve; D013997:Time Factors