A two kinase-gene signature model using CDK2 and PAK4 expression predicts poor outcome in non-small cell lung cancers
Abstract:
Risk classification on the basis of specific genomic features can lead to more precise tailoring of treatment for cancer patients. Kinases are potential therapeutic targets and survival factors, but the predictive prognostic potentials of multi-kinase genes have seldom been investigated. In this study, with publicly available microarray data of non-small cell lung cancers (NSCLC), we identified two kinase genes cyclin-dependent kinase 2 (CDK2) and p21 protein (Cdc42/Rac)-activated kinase 4 (PAK4) significantly associated with poor outcome. Then we present a combined gene signature model using CDK2 and PAK4 that can stratify disease poor outcome independently of standard clinical prognostic factors. Next, the predictive robustness of this 2-gene classifier was in silico confirmed in an independent microarray dataset, and experimentally validated in a lung cancer cohort by immunohistochemistry. Therefore, in this study, we demonstrated that the CDK2-PAK4 kinase signature may be a useful prognostic indicator and potential target for NSCLC. We also propose that poor outcome subgroup stratified by this classifier may benefit from the recently developed CDK2 and PAK4 inhibitors.