Department of Endocrinology, The Second Affiliated Hospital of Harbin Medical University, Harbin, China.
College of Bioinformatics Science and Technology, Harbin Medical University, Harbin, China.
Front Endocrinol (Lausanne). 2023 Feb 27;14:1110987. doi: 10.3389/fendo.2023.1110987. eCollection 2023.
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 (, , , , and ) 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 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.
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(lncRNA)是细胞周期过程的关键调节剂。因此,了解与细胞周期相关的 lncRNA(CCR-lncRNA)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(TCGA)中获取了 PTC 的 RNA-seq 和临床数据,并根据 Pearson 相关系数选择了 CCR-lncRNA。根据单因素 Cox 回归、最小绝对值收缩和选择算子(LASSO)以及多因素 Cox 回归分析,建立了一个由五个 CCR-lncRNA 组成的特征(、、、、和),用于预测 PTC 无进展间隔(PFI)。Kaplan-Meier 生存分析、时间依赖性接收器工作特征曲线和多因素 Cox 回归分析证明了该特征具有可靠的预后能力。构建了一个由风险特征和临床特征组成的列线图,可有效预测 PTC 的 PFI。功能富集分析表明该特征与细胞周期和免疫相关途径有关。此外,我们还分析了特征与免疫细胞浸润的相关性。最后,我们使用实时定量聚合酶链反应验证了 CCR-lncRNA 的差异表达。总之,基于五个 CCR-lncRNA 的新开发的预后风险特征可能成为预测 PTC PFI 的标志物。