| 金银铃,郑金华.基于生物信息学的葡萄膜黑色素瘤缺氧相关lncRNA预后模型的构建.[J].中南医学科学杂志.,2024,(4):554-558. |
| 基于生物信息学的葡萄膜黑色素瘤缺氧相关lncRNA预后模型的构建 |
| Construction of prognostic model for hypoxia-related lncRNA in uveal melanoma based on bioinformatics |
| 投稿时间：2023-02-19 修订日期：2024-03-18 |
| DOI：10.15972/j.cnki.43-1509/r.2024.04.011 |
| 中文关键词: 葡萄膜黑色素瘤 缺氧 lncRNA 预后模型 |
| 英文关键词:uveal melanoma hypoxia lncRNA prognostic model |
| 基金项目:贵阳市科技计划项目(筑科合同[2019]9-1-21号) |
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| 中文摘要: |
| 目的基于生物信息学构建葡萄膜黑色素瘤(UM)缺氧相关长链非编码RNA(lncRNA)的预后模型。 方法从TCGA数据库中筛选UM lncRNA表达谱及临床信息数据。采用Lasso回归、单因素与多因素Cox回归鉴定UM缺氧相关lncRNA预后标志物并构建预后模型；ROC评估预后模型灵敏度和特异度并绘制列线图。根据中位风险评分将UM分为高风险组和低风险组；采用Kaplan-Meier比较高、低风险组患者总生存期。采用KEGG对基于缺氧相关lncRNA差异基因进行功能富集分析。 结果共获得983个缺氧相关的lncRNA。共筛选出7个具有独立预后意义的UM缺氧相关lncRNA预后标志物(AC100791.3、SOS1-IT1、LHFPL3-AS1、AP005121.1、AL121820.2、LINC01006、AC104825.1)并构建其预后模型。预后模型的风险评分是UM患者的独立预后因素(P＜0.001)。ROC结果显示,预后模型具有较高的准确性。高风险组总生存期低于低风险组(P＜0.001)。KEGG富集分析结果显示,氧化磷酸化通路、乙醛酸与二羧酸代谢、系统性红斑狼疮、抗原的加工与呈递、P53信号通路富集于高风险组人群。 结论基于7个缺氧相关lncRNA构建的预后模型可预测UM患者的预后价值。 |
| 英文摘要: |
| AimTo construct a prognostic model for hypoxia-related long non-coding RNA (lncRNA) in uveal melanoma (UM) based on bioinformatics. MethodsThe lncRNA expression profiles and clinical information data of uveal melanoma were selected from the TCGA database.Lasso regression, univariate and multivariate Cox were used to identify prognostic markers of hypoxia-related lncRNA in UM, and a prognostic model was constructed. ROC curves were used to evaluate the sensitivity and specificity of the prognostic model, and a nomogram was estab-lished.According to the median risk score, UM was divided into high-risk and low-risk group.Kaplan-Meier analysis was used to compare the overall survival of patients in high- and low-risk groups.The functional enrichment analysis of differential genes of hypoxia-related lncRNAs was based on KEGG. Results983 hypoxia-related lncRNAs were obtained. 7 hypoxia-related lncRNA prognostic markers in UM (AC100791.3, SOS1-IT1, LHFPL3-AS1, AP005121.1, AL121820.2, LINC01006, AC104825.1) with independent prognostic significance were screened and a prognostic model was constructed. The risk score of the prognostic model is an independent prognostic factor for UM patients (P＜0.001). The ROC curve indicates that the prognostic model has high accuracy. The high risk group had a lower overall survival time than the low risk group (P＜0.001). KEGG enrichment analysis showed that oxidative phosphorylation pathway, glyoxylic acid and dicarboxylic acid metabolism, systemic lupus erythematosus, antigen processing and presentation, P53 signaling pathway were enriched in the high-risk group. ConclusionThe prognostic model based on 7 hypoxia-related lncRNAs can predict the prognostic value of UM patients. |
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