title: A gene expression signature predicts survival of patients with stage I non-small cell lung cancer.
abstract: BACKGROUND
  Lung cancer is the leading cause of cancer-related death in the United States. Nearly 50% of patients with stages I and II non-small cell lung cancer (NSCLC) will die from recurrent disease despite surgical resection. No reliable clinical or molecular predictors are currently available for identifying those at high risk for developing recurrent disease. As a consequence, it is not possible to select those high-risk patients for more aggressive therapies and assign less aggressive treatments to patients at low risk for recurrence.
  
  
  RESULTS
  In this study, we applied a meta-analysis of datasets from seven different microarray studies on NSCLC for differentially expressed genes related to survival time (under 2 y and over 5 y). A consensus set of 4,905 genes from these studies was selected, and systematic bias adjustment in the datasets was performed by distance-weighted discrimination (DWD). We identified a gene expression signature consisting of 64 genes that is highly predictive of which stage I lung cancer patients may benefit from more aggressive therapy. Kaplan-Meier analysis of the overall survival of stage I NSCLC patients with the 64-gene expression signature demonstrated that the high- and low-risk groups are significantly different in their overall survival. Of the 64 genes, 11 are related to cancer metastasis (APC, CDH8, IL8RB, LY6D, PCDHGA12, DSP, NID, ENPP2, CCR2, CASP8, and CASP10) and eight are involved in apoptosis (CASP8, CASP10, PIK3R1, BCL2, SON, INHA, PSEN1, and BIK).
  
  
  CONCLUSIONS
  Our results indicate that gene expression signatures from several datasets can be reconciled. The resulting signature is useful in predicting survival of stage I NSCLC and might be useful in informing treatment decisions.
authors.affiliation: Department of Surgery, Washington University School of Medicine, St. Louis, Missouri, United States of America.
authors.forename: Yan
authors.initials: Y
authors.lastname: Lu
authors.forename: William
authors.initials: W
authors.lastname: Lemon
authors.forename: Peng-Yuan
authors.initials: PY
authors.lastname: Liu
authors.forename: Yijun
authors.initials: Y
authors.lastname: Yi
authors.forename: Carl
authors.initials: C
authors.lastname: Morrison
authors.forename: Ping
authors.initials: P
authors.lastname: Yang
authors.forename: Zhifu
authors.initials: Z
authors.lastname: Sun
authors.forename: Janos
authors.initials: J
authors.lastname: Szoke
authors.forename: William L
authors.initials: WL
authors.lastname: Gerald
authors.forename: Mark
authors.initials: M
authors.lastname: Watson
authors.forename: Ramaswamy
authors.initials: R
authors.lastname: Govindan
authors.forename: Ming
authors.initials: M
authors.lastname: You
mesh_terms: D000465:Algorithms; D000704:Analysis of Variance; D002289:Carcinoma, Non-Small-Cell Lung / Q000188:drug therapy / Q000235:genetics* / Q000401:mortality* / Q000473:pathology; D020869:Gene Expression Profiling; D015972:Gene Expression Regulation, Neoplastic / Q000235:genetics; D006801:Humans; D008175:Lung Neoplasms / Q000188:drug therapy / Q000235:genetics* / Q000401:mortality* / Q000473:pathology; D015233:Models, Statistical; D009367:Neoplasm Staging; D020411:Oligonucleotide Array Sequence Analysis; D016016:Proportional Hazards Models; D012372:ROC Curve; D020133:Reverse Transcriptase Polymerase Chain Reaction; D018570:Risk Assessment; D016019:Survival Analysis