基于缺氧相关LncRNA胰腺癌预后模型的研究

摘要/Abstract

摘要： 目的旨在利用缺氧相关LncRNA对胰腺癌患者预后进行前瞻性评估。方法在TCGA数据库中获得所有胰腺癌转录组数据及临床资料,从GSEA中查找缺氧相关基因,通过共表达的方法找出与缺氧相关的LncRNA。单因素和多因素Cox分析筛选出预后相关的缺氧LncRNA进行预后模型构建。运用生存曲线和ROC曲线分析胰腺癌中缺氧相关LncRNA特征随时间推移的预后价值。最后利用特征LncRNA的表达量对胰腺癌肿瘤进行分型,分析验证特征LncRNA的表达与胰腺癌密切相关。lasso回归模型进一步分析筛选具有显著预后价值和研究意义的关键缺氧相关LncRNA。结果单因素Cox回归分析发现,在差异表达的缺氧相关LncRNA中有29个LncRNA可能与预后相关,这29个LncRNA经多因素Cox分析及优化得到6个影响预后的特征LncRNA。进而6个LncRNA构建的预后模型生存曲线也显示低风险组的预后较高风险组好,差异有统计学意义（P<0.05）。同时,独立预后分析表明该预后模型可以作为一个独立的预后因子（P<0.05）。最后lasso回归模型进一步筛选删减得到4个具有显著预后价值的缺氧相关LncRNA。结论本研究成功构建了基于4个缺氧相关LncRNA的胰腺癌预后模型,对提高胰腺癌患者预测预后及指导个体化治疗具有重要临床意义。该模型所包含的4个关键特征LncRNA可能是预测胰腺癌预后的潜在新型标志物。

关键词: 胰腺癌, 缺氧相关基因, LncRNA, 预后

Abstract: Objective The goal was to use hypoxia-associated LncRNAs to predict the prognosis of pancreatic cancer patients. Methods The TCGA database provided all of the transcriptome data and clinical information linked to pancreatic cancer. GSEA was used to identify hypoxia-associated genes, and co-expression was used to identify hypoxia-related LncRNAs.For the purpose of creating the prognostic model, prognostic-related hypoxic LncRNAs were filtered out using one-way and multifactorial Cox analyses. The predictive significance of hypoxia-associated LncRNA characteristics in pancreatic cancer was examined over time using survival curves and ROC curves. Ultimately, the expression of the distinctive LncRNAs was used to classify pancreatic cancer tumors, and the research confirmed that there was a strong correlation between the expression of these LncRNAs and pancreatic cancer. In order to identify the important hypoxia-associated LncRNAs with substantial prognostic value and research significance, the lasso regression model was further examined. Results A total of 29 of the differentially expressed hypoxia-associated LncRNAs were found to be potentially associated with prognosis by one-way Cox regression analysis. These 29 LncRNAs were then optimized by multifactorial Cox analysis to yield 6 distinctive LncRNAs influencing prognosis. The prognostic model survival curves created by the 6 LncRNAs also demonstrated that the low-risk group's prognosis was better than the high-risk group's, with a statistically significant difference between the two groups. statistically noteworthy. The prognostic model could be utilized as an independent prognostic factor, according to the independent prognostic analysis. Ultimately, four hypoxia-related LncRNAs with considerable predictive significance were obtained by further filtering and censoring the lasso regression model. Conclusion With the use of four hypoxia-related LncRNAs, we were able to effectively build a predictive model for pancreatic cancer in this work. This model is crucial for enhancing prognosis prediction and directing patients' individualized treatment. The four essential distinctive LncRNAs in this model could be unique markers for predicting pancreatic cancer prognosis.

Key words: pancreatic cancer, hypoxia related genes, LncRNA, prognosis

中图分类号:

R364.4

郑艳, 韩丽红, 闫斌, 林翠英. 基于缺氧相关LncRNA胰腺癌预后模型的研究[J]. 中华养生保健, 2024, 42(7): 4-9.

ZHENG Yan, HAN Li-hong, YAN Bin, LIN Cui-ying. Model of Pancreatic Cancer Prognosis Based on LncRNA Linked to Hypoxia[J]. ZHONGHUA YANGSHENG BAOJIAN, 2024, 42(7): 4-9.

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