title: Constructing molecular classifiers for the accurate prognosis of lung adenocarcinoma.
abstract: OBJECTIVE
  Individualized therapy of lung adenocarcinoma depends on the accurate classification of patients into subgroups of poor and good prognosis, which reflects a different probability of disease recurrence and survival following therapy. However, it is currently impossible to reliably identify specific high-risk patients. Here, we propose a computational model system which accurately predicts the clinical outcome of individual patients based on their gene expression profiles.
  
  
  METHODS
  Gene signatures were selected using feature selection algorithms random forests, correlation-based feature selection, and gain ratio attribute selection. Prediction models were built using random committee and Bayesian belief networks. The prognostic power of the survival predictors was also evaluated using hierarchical cluster analysis and Kaplan-Meier analysis.
  
  
  RESULTS
  The predictive accuracy of an identified 37-gene survival signature is 0.96 as measured by the area under the time-dependent receiver operating curves. The cluster analysis, using the 37-gene signature, aggregates the patient samples into three groups with distinct prognoses (Kaplan-Meier analysis, P < 0.0005, log-rank test). All patients in cluster 1 were in stage I, with N0 lymph node status (no metastasis) and smaller tumor size (T1 or T2). Additionally, a 12-gene signature correctly predicts the stage of 94.2% of patients.
  
  
  CONCLUSIONS
  Our results show that the prediction models based on the expression levels of a small number of marker genes could accurately predict patient outcome for individualized therapy of lung adenocarcinoma. Such an individualized treatment may significantly increase survival due to the optimization of treatment procedures and improve lung cancer survival every year through the 5-year checkpoint.
authors.affiliation: Mary Babb Randolph Cancer Center, Department of Community Medicine, West Virginia University, Morgantown, West Virginia 26506-9300, USA. lguo@hsc.wvu.edu
authors.forename: Lan
authors.initials: L
authors.lastname: Guo
authors.forename: Yan
authors.initials: Y
authors.lastname: Ma
authors.forename: Rebecca
authors.initials: R
authors.lastname: Ward
authors.forename: Vince
authors.initials: V
authors.lastname: Castranova
authors.forename: Xianglin
authors.initials: X
authors.lastname: Shi
authors.forename: Yong
authors.initials: Y
authors.lastname: Qian
mesh_terms: D000230:Adenocarcinoma / Q000145:classification / Q000175:diagnosis* / Q000235:genetics; D019540:Area Under Curve; D016000:Cluster Analysis; D003198:Computer Simulation; D020869:Gene Expression Profiling; D006801:Humans; D053208:Kaplan-Meier Estimate; D008175:Lung Neoplasms / Q000145:classification / Q000175:diagnosis* / Q000235:genetics; D011237:Predictive Value of Tests; D011379:Prognosis; D012372:ROC Curve; D016019:Survival Analysis; D013997:Time Factors