Panels of tumor-derived RNA markers in peripheral blood of patients with non-small cell lung cancer: their dependence on age, gender and clinical stages

Abstract

// Chih-Feng Chian 1 , Yi-Ting Hwang 2 , Harn-Jing Terng 3 , Shih-Chun Lee 4 , Tsui-Yi Chao 5, 7 , Hung Chang 4 , Ching-Liang Ho 6 , Yi-Ying Wu 6 , Wann-Cherng Perng 1 1 Division of Pulmonary and Critical Care Medicine, Department of Internal Medicine, Tri-Service General Hospital, National Defense Medical Center, Taipei, Taiwan, ROC 2 Department of Statistics, National Taipei University, Taipei, Taiwan, ROC 3 Advpharma, Inc., Taipei, Taiwan, ROC 4 Division of Thoracic Surgery, Department of Surgery, Tri-Service General Hospital, National Defense Medical Center, Taipei, Taiwan, ROC 5 Division of Hematology and Oncology, Department of Internal Medicine, Taipei Medical University-Shuang Ho Hospital, Taipei, Taiwan, ROC 6 Division of Hematology and Oncology, Department of Internal Medicine, Tri-Service General Hospital, National Defense Medical Center, Taipei, Taiwan, ROC 7 Graduate Institute of Life Sciences, National Defense Medical Center, Taipei, Taiwan, ROC Correspondence to: Wann-Cherng Perng, email: wperng@ms27.hinet.net Keywords: circulating tumor cells, gene expression profiling, non-small cell lung cancer Received: July 17, 2015     Accepted: June 29, 2016     Published: July 13, 2016 ABSTRACT Peripheral blood mononuclear cell (PBMC)-derived gene signatures were investigated for their potential use in the early detection of non-small cell lung cancer (NSCLC). In our study, 187 patients with NSCLC and 310 age- and gender-matched controls, and an independent set containing 29 patients for validation were included. Eight significant NSCLC-associated genes were identified, including DUSP6 , EIF2S3 , GRB2 , MDM2 , NF1 , POLDIP2 , RNF4 , and WEE1 . The logistic model containing these significant markers was able to distinguish subjects with NSCLC from controls with an excellent performance, 80.7% sensitivity, 90.6% specificity, and an area under the receiver operating characteristic curve (AUC) of 0.924. Repeated random sub-sampling for 100 times was used to validate the performance of classification training models with an average AUC of 0.92. Additional cross-validation using the independent set resulted in the sensitivity 75.86%. Furthermore, six age/gender-dependent genes: CPEB4 , EIF2S3 , GRB2 , MCM4 , RNF4 , and STAT2 were identified using age and gender stratification approach. STAT2 and WEE1 were explored as stage-dependent using stage-stratified subpopulation. We conclude that these logistic models using different signatures for total and stratified samples are potential complementary tools for assessing the risk of NSCLC.