Molecular characterization of papillary thyroid carcinoma: a potential three-lncRNA prognostic signature
Authors You X, Yang S, Sui J, Wu W, Liu T, Xu S, Cheng Y, Kong X, Liang G, Yao Y
Received 22 May 2018
Accepted for publication 16 July 2018
Published 8 October 2018 Volume 2018:10 Pages 4297—4310
Checked for plagiarism Yes
Review by Single-blind
Peer reviewer comments 3
Editor who approved publication: Professor Raphael Catane
Xin You,1,2,* Sheng Yang,3,* Jing Sui,3 Wenjuan Wu,3 Tong Liu,3 Siyi Xu,3 Yanping Cheng,3 Xiaoling Kong,3 Geyu Liang,3 Yongzhong Yao1,2
1Department of General Surgery, Nanjing Drum Tower Hospital, The Affiliated Hospital of Nanjing University Medical School, Nanjing, Jiangsu, People’s Republic of China; 2Department of General Surgery, School of Medicine, Southeast University, Nanjing, Jiangsu, People’s Republic of China; 3Key Laboratory of Environmental Medicine Engineering, Ministry of Education, School of Public Health, Southeast University, Nanjing, Jiangsu, People’s Republic of China
*These authors contributed equally to this work
Purpose: Papillary thyroid carcinoma (PTC), the most frequent type of malignant thyroid tumor, lacks novel and reliable biomarkers of patients’ prognosis. In the current study, we mined The Cancer Genome Atlas (TCGA) to develop lncRNA signature of PTC.
Patients and methods: The intersection of PTC lncRNAs was obtained from the TCGA database using integrative computational method. By the univariate and multivariate Cox analysis, key lncRNAs were identified to construct the prognostic model. Then, all patients were divided into the high-risk group and low-risk group to perform the Kaplan–Meier (K–M) survival curves and time-dependent receiver operating characteristic (ROC) curve, estimating the prognostic power of the prognostic model. Functional enrichment analysis was also performed. Finally, we verified the results of the TCGA analysis by the Gene Expression Omnibus (GEO) databases and quantitative real-time PCR (qRT-PCR).
Results: After the comprehensive analysis, a three-lncRNA signature (PRSS3P2, KRTAP5-AS1 and PWAR5) was obtained. Interestingly, patients with low-risk scores tended to gain obviously longer survival time, and the area under the time-dependent ROC curve was 0.739. Furthermore, gene ontology (GO) and pathway analysis revealed the tumorigenic and prognostic function of the three lncRNAs. We also found three potential transcription factors to help understand the mechanisms of the PTC-specific lncRNAs. Finally, the GEO databases and qRT-PCR validation were consistent with our TCGA bioinformatics results.
Conclusion: We built a three-lncRNA signature by mining the TCGA database, which could effectively predict the prognosis of PTC.
Keywords: The Cancer Genome Atlas, long noncoding RNAs, overall survival, risk score
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