An expression signature model to predict lung adenocarcinoma-specific survival

• Published in: Cancer management and research 2018-01, Vol.10, p.3717-3732
• Main Authors: Shi, Xiaoshun, Tan, Haoming, Le, Xiaobing, Xian, Haibing, Li, Xiaoxiang, Huang, Kailing, Luo, Viola Yingjun, Liu, Yanhui, Wu, Zhuolin, Mo, Haiyun, Chen, Allen M, Liang, Ying, Zhang, Jiexia
• Format: Article
• Language: English
• Subjects: Adenocarcinoma; Biochemistry; Biomarkers; Breast cancer; Cancer genetics; Cancer staging; Cancer treatment; Carcinoma; Cell growth; Criminal investigation; Gastric cancer; Gene expression; Genes; Genetic aspects; Genomes; Genomics; Hospitals; Laboratories; lncRNA; Lung adenocarcinoma; Lung cancer; Medical prognosis; Medical research; Metastasis; Oncology; Original Research; Patients; Prognosis; Recurrence (Disease); Regression analysis; Respiratory diseases; RNA; RNA sequencing; RNA-seq; signature; survival analysis; Thoracic surgery; Tumors
• ISSN: 1179-1322; 1179-1322
• DOI: 10.2147/CMAR.S159563

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. 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. 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. 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.