An expression signature model to predict lung adenocarcinoma-specific survival
Received 11 December 2017
Accepted for publication 9 April 2018
Published 24 September 2018 Volume 2018:10 Pages 3717—3732
Checked for plagiarism Yes
Review by Single-blind
Peer reviewer comments 2
Editor who approved publication: Professor Lu-Zhe Sun
Xiaoshun Shi,1,2,* Haoming Tan,3 Xiaobing Le,4,5,* Haibing Xian,6,* Xiaoxiang Li,1 Kailing Huang,4,5 Viola Yingjun Luo,4,5 Yanhui Liu,4,5 Zhuolin Wu,7 Haiyun Mo,8 Allen M Chen,4,5,* Ying Liang,9 Jiexia Zhang1
1National Clinical Research Center for Respiratory Disease, State Key Laboratory of Respiratory Disease, Department of Medicine, Guangzhou Institute of Respiratory Disease, Guangzhou Institute of Respiratory Health, Guangzhou 510120, China; 2Department of Thoracic Surgery, Nanfang Hospital, Southern Medical University, Guangzhou 510515, China; 3Department of Thoracic Surgery, Shunde Lecong Affiliated Hospital of Guangzhou Medical University, Guangdong 528315, China; 4Mendel Genes Inc, Guangzhou 510515, China; 5Mendel Genes Inc, Manhattan Beach, CA 90266, USA; 6Department of Head and Neck/Thoracic Medical Oncology, The First People’s Hospital of Foshan, Guangdong 528000, China; 7Department of Biomedical Engineering, University of Minnesota, Twin Cities, MN, USA; 8Department of Public Health, Guangzhou Medical University, Guangzhou 510000, China; 9Department of Medical Oncology, Sun Yat-sen University Cancer Center, State Key Laboratory of Oncology in South China, Collaborative Innovation Center for Cancer Medicine, Guangzhou 510060, China
*These authors contributed equally to this work
Background: The current TNM staging system plays a central role in lung adenocarcinoma (LUAD) prognosis. However, it may not adequately stratify the risk of tumor recurrence. With the aid of gene expression profiling, we identified 31 lncRNAs whose expressions in tumor tissues could be used as a risk indicator for the guidance of lung cancer therapy. This exploratory analysis may shed new light on identification of potential prognostic factors.
Materials and methods: A survival prediction scoring model was developed from the data that are publicly available in The Cancer Genome Atlas (TCGA) LUAD RNA Sequencing dataset. Multivariate Cox regression analysis and Kaplan–Meier analysis were performed on a cohort of 254 stage I lung carcinoma patients with survival records.
Results: Our model indicates that the panels comprising 31 lncRNAs are highly associated with overall survival (OS): 18.9% (95% CI: 10.4%–34.5%) and 89.5% (95% CI: 80.7%–99.2%) for the high- and low-risk group, respectively. The specificity and sensitivity of the model are verified, which show that the area under receiver operating characteristic curve yields 0.881, meaning our model has good accuracy and it is feasible for further applications.
Conclusion: The 31-lncRNA model might be able to predict OS in patients with LUAD with high accuracy. Its further applications in biomolecular experiments using clinical samples with independent cohorts of patients are needed to verify the results.
Keywords: lung adenocarcinoma, lncRNA, signature, survival analysis, prognosis, RNA-seq
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