Molecular gene signature and prognosis of non-small cell lung cancer
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Poyin Huang1,2,3,4, Chiou-Ling Cheng5, Ya-Hsuan Chang6, Chia-Hsin Liu6, Yi-Chiung Hsu6, Jin-Shing Chen7, Gee-Chen Chang8,9,10, Bing-Ching Ho5, Kang-Yi Su5, Hsuan-Yu Chen6, Sung-Liang Yu5,11,12,13,14
1Department of Neurology, Kaohsiung Municipal Hsiao-Kang Hospital, Kaohsiung Medical University, Kaohsiung, Taiwan
2Department of Neurology, Kaohsiung Medical University Hospital, Kaohsiung Medical University, Kaohsiung, Taiwan
3Ph.D. Program in Translational Medicine, Kaohsiung Medical University and Academia Sinica, Taiwan
4Department of Neurology, Faculty of Medicine, College of Medicine, Kaohsiung Medical University, Kaohsiung, Taiwan
5Department of Clinical Laboratory Sciences and Medical Biotechnology, College of Medicine, National Taiwan University, Taipei, Taiwan
6Institute of Statistical Science, Academia Sinica, Taipei, Taiwan
7Department of Traumatology, National Taiwan University Hospital, Taipei, Taiwan
8Faculty of Medicine, School of Medicine, National Yang-Ming University, Taipei, Taiwan
9Division of Chest Medicine, Department of Internal Medicine, Taichung Veterans General Hospital, Taichung, Taiwan
10Comprehensive Cancer Center, Taichung Veterans General Hospital, Taichung, Taiwan
11Center of Genomic Medicine, National Taiwan University, Taipei, Taiwan
12Center for Optoelectronic Biomedicine, College of Medicine, National Taiwan University, Taipei, Taiwan
13Graduate Institute of Pathology, College of Medicine, National Taiwan University, Taipei, Taiwan
14Department of Laboratory Medicine, National Taiwan University Hospital, Taipei, Taiwan
Hsuan-Yu Chen, email: [email protected]
Sung-Liang Yu, email: [email protected]
Keywords: non–small cell lung cancer, TaqMan Low-Density Array, risk score, gene signature, prognosis
Received: March 28, 2016 Accepted: June 30, 2016 Published: July 16, 2016
The current staging system for non–small cell lung cancer (NSCLC) is inadequate for predicting outcome. Risk score, a linear combination of the values for the expression of each gene multiplied by a weighting value which was estimated from univariate Cox proportional hazard regression, can be useful. The aim of this study is to analyze survival-related genes with TaqMan Low-Density Array (TLDA) and risk score to explore gene-signature in lung cancer. A total of 96 NSCLC specimens were collected and randomly assigned to a training (n = 48) or a testing cohort (n = 48). A panel of 219 survival-associated genes from published studies were used to develop a 6-gene risk score. The risk score was used to classify patients into high or low-risk signature and survival analysis was performed. Cox models were used to evaluate independent prognostic factors. A 6-gene signature including ABCC4, ADRBK2, KLHL23, PDS5A, UHRF1 and ZNF551 was identified. The risk score in both training (HR = 3.14, 95% CI: 1.14–8.67, p = 0.03) and testing cohorts (HR = 5.42, 95% CI: 1.56–18.84, p = 0.01) was the independent prognostic factor. In merged public datasets including GSE50081, GSE30219, GSE31210, GSE19188, GSE37745, GSE3141 and GSE31908, the risk score (HR = 1.50, 95% CI: 1.25–1.80, p < 0.0001) was also the independent prognostic factor. The risk score generated from expression of a small number of genes did perform well in predicting overall survival and may be useful in routine clinical practice.
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