Background. Thyroid cancer (TC) is one of the most common type of endocrine tumors. Long noncoding RNAs had been demonstrated to play key roles in TC. Material and Methods. The lncRNA expression data were downloaded from Co-lncRNA database. The raw data was normalized using the limma package in R software version 3.3.0. The differentially expressed mRNA and lncRNAs were identified by the linear models for the microarray analysis (Limma) method. The DEGs were obtained with thresholds of and . The hierarchical cluster analysis of differentially expressed mRNAs and lncRNAs was performed using CLUSTER 3.0, and the hierarchical clustering heat map was visualized by Tree View. Results. In the present study, we identified 6 upregulated and 85 downregulated lncRNAs in TC samples. Moreover, we for the first time identified 16 downregulated lncRNAs was correlated to longer disease-free survival time in patients with TC, including ATP1A1-AS1, CATIP-AS1, FAM13A-AS1, LINC00641, LINC00924, MIR22HG, NDUFA6-AS1, RP11-175K6.1, RP11-727A23.5, RP11-774O3.3, RP13-895J2.2, SDCBP2-AS1, SLC26A4-AS1, SNHG15, SRP14-AS1, and ZNF674-AS1. Conclusions. Bioinformatics analysis revealed these lncRNAs were involved in regulating the RNA metabolic process, cell migration, organelle assembly, tRNA modification, and hormone levels. This study will provide useful information to explore the potential candidate biomarkers for diagnosis, prognosis, and drug targets for TC.

中文翻译：

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计算机分析将不同表达的 lncRNA 识别为甲状腺癌预后的新型生物标志物。

背景。甲状腺癌（TC）是最常见的内分泌肿瘤之一。已证明长链非编码 RNA 在 TC 中起关键作用。材料和方法。lncRNA 表达数据从 Co-lncRNA 数据库下载。使用 R 软件 3.3.0 版中的 limma 包对原始数据进行标准化。通过微阵列分析（Limma）方法的线性模型鉴定差异表达的mRNA和lncRNA。DEGs 的阈值为和. 使用 CLUSTER 3.0 对差异表达的 mRNA 和 lncRNA 进行层次聚类分析，并通过 Tree View 可视化层次聚类热图。结果。在本研究中，我们在 TC 样本中鉴定了 6 个上调和 85 个下调的 lncRNA。此外，我们首次发现 16 个下调的 lncRNA 与 TC 患者较长的无病生存时间相关，包括 ATP1A1-AS1、CATIP-AS1、FAM13A-AS1、LINC00641、LINC00924、MIR22HG、NDUFA6-AS1、RP11- 175K6.1、RP11-727A23.5、RP11-774O3.3、RP13-895J2.2、SDCBP2-AS1、SLC26A4-AS1、SNHG15、SRP14-AS1 和 ZNF674-AS1。结论. 生物信息学分析显示，这些 lncRNA 参与调节 RNA 代谢过程、细胞迁移、细胞器组装、tRNA 修饰和激素水平。本研究将为探索 TC 诊断、预后和药物靶点的潜在候选生物标志物提供有用的信息。