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
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Chih-Feng Chian1, Yi-Ting Hwang2, Harn-Jing Terng3, Shih-Chun Lee4, Tsui-Yi Chao5,7, Hung Chang4, Ching-Liang Ho6, Yi-Ying Wu6, Wann-Cherng Perng1
1Division of Pulmonary and Critical Care Medicine, Department of Internal Medicine, Tri-Service General Hospital, National Defense Medical Center, Taipei, Taiwan, ROC
2Department of Statistics, National Taipei University, Taipei, Taiwan, ROC
3Advpharma, Inc., Taipei, Taiwan, ROC
4Division of Thoracic Surgery, Department of Surgery, Tri-Service General Hospital, National Defense Medical Center, Taipei, Taiwan, ROC
5Division of Hematology and Oncology, Department of Internal Medicine, Taipei Medical University-Shuang Ho Hospital, Taipei, Taiwan, ROC
6Division of Hematology and Oncology, Department of Internal Medicine, Tri-Service General Hospital, National Defense Medical Center, Taipei, Taiwan, ROC
7Graduate Institute of Life Sciences, National Defense Medical Center, Taipei, Taiwan, ROC
Wann-Cherng Perng, email: firstname.lastname@example.org
Keywords: circulating tumor cells, gene expression profiling, non-small cell lung cancer
Received: July 17, 2015 Accepted: June 29, 2016 Published: July 13, 2016
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.
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