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细胞周期相关长链非编码 RNA 标志物预测甲状腺乳头状癌无进展间隔。

A cell cycle-related lncRNA signature predicts the progression-free interval in papillary thyroid carcinoma.

机构信息

Department of Endocrinology, The Second Affiliated Hospital of Harbin Medical University, Harbin, China.

College of Bioinformatics Science and Technology, Harbin Medical University, Harbin, China.

出版信息

Front Endocrinol (Lausanne). 2023 Feb 27;14:1110987. doi: 10.3389/fendo.2023.1110987. eCollection 2023.

Abstract

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.

摘要

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。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/f861/10009218/b38f59f95d5b/fendo-14-1110987-g001.jpg

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