| 章 静,王黎明,周金宝,刘 贺,潘 越,朱 峰.死亡相关的LncRNAs在甲状腺癌中的预后价值及预后风险模型构建[J].,2022,(2):282-288 |
| 死亡相关的LncRNAs在甲状腺癌中的预后价值及预后风险模型构建 |
| Prognostic Value and Prognostic Risk Modeling of Ferroptosis-Related LncRNAs in Thyroid Cancer |
| 投稿时间：2021-06-13 修订日期：2021-07-07 |
| DOI：10.13241/j.cnki.pmb.2022.02.016 |
| 中文关键词: 甲状腺癌 铁死亡 LncRNAs 生物信息学 预后 风险模型 |
| 英文关键词: Thyroid cancer Ferroptosis LncRNAs Bioinformatics Prognostic Risk model |
| 基金项目:南京医科大学科技发展基金一般项目(NMUB2018302 )；江苏省自然科学基金青年基金项目（BK20171049） |
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| 中文摘要: |
| 摘要 目的：探讨铁死亡相关的长链非编码RNAs( LncRNAs) 在甲状腺癌中的预后价值并构建预后风险模型。方法：从癌症基因组图谱( TCGA) 数据库下载甲状腺癌的转录本数据和临床数据，铁死亡相关的基因数据集是从铁死亡数据库（http://www.zhounan.org/ferrdb/)下载的259个基因集。得到铁死亡相关LncRNAs，与患者临床信息合并后，通过单因素 Cox 回归分析和 Kaplan- Meier生存分析两种方法得到与甲状腺癌预后相关的铁死亡LncRNAs，通过R的survival包构建COX模型以此来建立最佳预后风险模型并予以验证。结果：获得30个铁死亡相关的LncRNAs，多因素cox分析得到10个与甲状腺癌预后相关的铁死亡LncRNAs，包括AL136366.1、AL162231.2、CRNDE、AC004918.3、LINC02471、AC092279.1、AC046143.1、LINC02454、DOCK9-DT、AC008063.1。Kaplan- Meier生存分析表明高风险组预后较差。单因素和多因素Cox分析表明风险评分可以作为独立预后因子。KEGG 通路富集分析发现，差异基因可能与嘧啶代谢、核苷酸切除修复、NOTCH_信号通路等通路有关。结论：通过生物信息学方法筛选出10个与甲状腺癌预后的铁死亡相关LncRNAs，并成功构建预后风险模型。 |
| 英文摘要: |
| ABSTRACT Objective: To explore the prognostic value of ferroptosis-related long chain non-coding RNAs(LncRNAs) in thyroid cancer and construct a prognostic risk model. Methods: Thyroid cancer transcripts and clinical data were downloaded from the Cancer Genome Atlas (TCGA) database, ferroptosis-related gene data set from the iron database (http://www.zhounan.org/ferrdb/) to download a set of 259 genes. The ferroptosis-related LncRNAs were obtained, after combining with clinical information of patients, the LncRNAs associated with thyroid cancer prognosis were obtained by univariate Cox regression analysis and Kaplan-Meier survival analysis. The COX model was constructed by the survival package of R to establish the optimal prognostic risk model and verify it. Results: 30 ferroptosis-related LncRNAs were obtained, and 10 ferroptosis-related LncRNAs were found to be associated with thyroid cancer prognosis by multivariate cox analysis, including AL136366.1, AL162231.2, CRNDE, AC004918.3, LINC02471, AC092279.1, AC046143.1, LINC02454, DOCK9-DT, AC008063.1. Kaplan-Meier survival analysis showed that the high-risk group had a poor prognosis. Univariate and multivariate Cox analysis showed that risk score could be an independent prognostic factor. KEGG pathway enrichment analysis revealed that the differential genes may be related to pyrimidine metabolism, nucleotide excision and repair, and NOTCH signaling pathway and so on. Conclusion: Bioinformatics methods are used to screen out 10 ferroptosis-related LncRNAs associated with thyroid cancer prognosis, and the prognostic risk model is successfully constructed. |
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