细胞周期在肿瘤发生和进展中起着至关重要的作用。长链非编码RNA（lncRNAs）是细胞周期过程的关键调节因子。因此，了解与细胞周期相关的lncRNAs（CCR-lncRNAs）对于确定甲状腺乳头状癌（PTC）的预后至关重要。从癌症基因组图谱获取了PTC的RNA-seq和临床资料，并根据Pearson相关系数选出CCR-lncRNAs。根据单变量Cox回归、最小绝对收缩和选择算子（LASSO）和多元Cox回归分析，建立了一个由五个CCR-lncRNA（FOXD2-AS1、LOC100507156、BSG-AS1、EGOT和TMEM105）组成的签名，用于预测PTC的无进展生存期（PFI）。Kaplan-Meier 生存期、时间依赖的接收器操作特征曲线和多元 Cox 回归分析证明了该签名具有可靠的预后能力。构建了由风险签名和临床特征组成的评分卡，该评分卡可以有效地预测PTC的PFI。功能富集分析表明，该签名涉及细胞周期和免疫相关路经。此外，我们还分析了签名和免疫细胞浸润之间的相关性。最后，我们通过实时荧光定量聚合酶链反应验证了体外CCR-lncRNAs的差异表达。总体而言，基于五个CCR-lncRNAs开发的新型预后风险签名可能成为PTC预测PFI的标记。版权所有© 2023年李，冉和乔。

The cell cycle plays a vital role in tumorigenesis and progression. Long non-coding RNAs (lncRNAs) are key regulators of cell cycle processes. Therefore, understanding cell cycle-related lncRNAs (CCR-lncRNAs) is crucial for determining the prognosis of papillary thyroid carcinoma (PTC). RNA-seq and clinical data of PTC were acquired from The Cancer Genome Atlas, and CCR-lncRNAs were selected based on Pearson's correlation coefficients. According to univariate Cox regression, least absolute shrinkage and selection operator (LASSO), and multivariate Cox regression analyses, a five-CCR-lncRNA signature (FOXD2-AS1, LOC100507156, BSG-AS1, EGOT, and TMEM105) was established to predict the progression-free interval (PFI) in PTC. Kaplan-Meier survival, time-dependent receiver operating characteristic curve, and multivariate Cox regression analyses proved that the signature had a reliable prognostic capability. A nomogram consisting of the risk signature and clinical characteristics was constructed that effectively predicted the PFI in PTC. Functional enrichment analyses indicted that the signature was involved in cell cycle- and immune-related pathways. Furthermore, we also analyzed the correlation between the signature and immune cell infiltration. Finally, we verified the differential expression of CCR-lncRNAs in vitro using quantitative real-time polymerase chain reaction. Overall, the newly developed prognostic risk signature based on five CCR-lncRNAs may become a marker for predicting the PFI in PTC.Copyright © 2023 Li, Ran and Qiao.