Molecular characterization of papillary thyroid carcinoma: a potential three-lncRNA prognostic signature

• Published in: Cancer management and research Vol. 10; pp. 4297 - 4310
• Main Authors: You, Xin, Yang, Sheng, Sui, Jing, Wu, Wenjuan, Liu, Tong, Xu, Siyi, Cheng, Yanping, Kong, Xiaoling, Liang, Geyu, Yao, Yongzhong
• Format: Journal Article
• Language: English
• Published: New Zealand Dove Medical Press Limited 01.01.2018; Taylor & Francis Ltd; Dove Medical Press
• Subjects: Biological markers; Biomarkers; Breast cancer; Cancer genetics; Cancer research; Carcinoma; Care and treatment; Cell cycle; Committees; Computational biology; Development and progression; DNA binding proteins; Gene expression; Genes; Genetic aspects; Genomes; Genomics; Health aspects; Hospitals; Identification and classification; Kinases; lncRNA; Medical prognosis; Metabolism; Metastasis; Novels; Original Research; Papillary thyroid cancer; prognosis; Proteins; risk score; RNA; Surgery; Survival analysis; Thyroid cancer; Thyroid diseases; Transcription factors; Tumors; China; overall survival; long noncoding RNAs; risk score; The Cancer Genome Atlas
• ISSN: 1179-1322; 1179-1322
• DOI: 10.2147/CMAR.S174874

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. 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). 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. We built a three-lncRNA signature by mining the TCGA database, which could effectively predict the prognosis of PTC.