| 张雯雯,王 骁,周道安,等.乳腺癌铜死亡相关lncRNA预后风险模型的构建[J].肿瘤学杂志,2023,29(8):687-697. |
| 乳腺癌铜死亡相关lncRNA预后风险模型的构建 |
| Construction of A Prognostic Risk Model for Breast Cancer Based on Cuproptosis-Related lncRNA |
| 投稿时间：2023-06-21 |
| DOI：10.11735/j.issn.1671-170X.2023.08.B009 |
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| 中文关键词: 乳腺癌 铜死亡 lncRNA 预后 风险模型 |
| 英文关键词:breast cancer cuproptosis lncRNA prognosis risk model |
| 基金项目:江苏省无锡市科技发展基金指导性计划项目（202029） |
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| 摘要点击次数: 272 |
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| 中文摘要: |
| 摘 要：［目的］ 基于铜死亡相关lncRNA构建乳腺癌预后风险模型并探索其潜在的生物学意义。［方法］ 从TCGA数据库下载乳腺癌数据，利用R软件获取铜死亡相关lncRNA。用Cox回归及Lasso回归分析筛选铜死亡预后相关lncRNA，建立预后风险模型。运用Kaplan-Meier分析和ROC曲线对模型进行评估，并将此模型与临床病理特征进行整合，Cox回归分析寻找独立预后因素，建立列线图。对高低风险组之间差异基因进行基因本体（Gene Ontology，GO）和京都基因与基因组百科全书（Kyoto Encyclopedia of Genes and Genomes，KEGG）富集分析、免疫功能分析，并探讨两组间TMB和突变基因的表达。［结果］ 单因素Cox回归分析显示19个铜死亡预后相关lncRNA，Lasso回归分析筛选出14个lncRNA，多因素Cox回归分析确定8个lncRNA（AL590434.1、AC105398.1、AL137847.1、AC004982.1、AC107993.1、MECOM-AS1、U73166.1、HECW2-AS1）用于构建预后风险模型。风险评分=（-1.45494698784031×AL590434.1）+（-3.67358360093321×AC105398.1）+（-1.10778423160903×AL137847.1）+（0.661226964250153×AC004982.1）+（-1.46582205700717×AC107993.1）+（0.825809098216395×MECOM-AS1）+（-0.651012875897201×U73166.1）+（0.249130715504216×HECW2-AS1）。高风险组患者的总生存时间（overall survival，OS）及无进展生存期（progression-free survival，PFS)明显低于低风险组（P<0.05）。预后风险模型预测乳腺癌患者1年、3年和5年生存效能较好（AUC值分别为0.727、0.688、0.706）。风险评分可作为乳腺癌患者的独立预后因素（P<0.01），列线图证实该模型具有良好的预后预测能力。GO分析主要富集包括肌肉系统过程、富含胶原的细胞外基质、肌动蛋白结合；KEGG分析主要富集在钙离子信号通路（P<0.05）。免疫功能分析发现T细胞共抑制、抗原呈递共刺激及共抑制在高、低风险组之间存在显著差异（P<0.01）。高风险组TMB较低风险组高（P<0.05），高TMB组预后较低TMB组差（P<0.05）。两组中PIK3CA是突变频率最高的基因，其次为TP53。［结论］ 8个铜死亡相关lncRNA组成的预后风险模型是乳腺癌的独立预后因素，可以有效预测乳腺癌患者的生存预后。 |
| 英文摘要: |
| Abstract：［Objective］ To construct a prognostic risk model of breast cancer based on cuproptosis-related lncRNA and explore its potential biological significance. ［Methods］ The data of breast cancer was downloaded from TCGA database， and R software was used to obtain cuproptosis-related lncRNA. Cox regression and Lasso regression analysis were adopted to screen cuproptosis-related lncRNA， and a prognostic risk model was established. Kaplan-Meier method and ROC curve were used to evaluate the model. Integrated with other clinicopathologic features， Cox regression analysis were used to find independent prognostic factors， and a nomogram was established. GO， KEGG enrichment and immune functional analysis were performed on differentially expressed genes between high and low risk groups. Expressions of TMB and mutant genes between the two groups were explored. ［Results］ 19 prognostic cuproptosis-related lncRNA were obtained by univariate Cox analysis， 14 lncRNA were screened by Lasso analysis， and 8 lncRNA (AL590434.1， AC105398.1， AL137847.1， AC004982.1， AC107993.1， MECOM-AS1， U73166.1， HECW2-AS1) were identified by multivariate Cox analysis for constructing a prognostic risk model. Risk score = (-1.45494698784031×AL590434.1)+(-3.67358360093321×AC105398.1)+(-１.10778423160903×AL137847.1)+(0.661226964250153×AC004982.1)+(-１.46582205700717×AC107993.1)+(0.825809098216395×MECOM-AS1)+(-0.651012875897201×U73166.1)+(0.249130715504216×HECW2-AS1). The OS and PFS of patients in the high risk group were significantly lower than those in the low risk group(P<0.05). The risk model predicted the prognosis of breast cancer patients had a good survival efficacy at 1-， 3- and 5- year(AUC were 0.727， 0.688 and 0.706， respectively). Risk score could be an independent prognostic factor for breast cancer patients(P<0.01)， and the nomogram confirmed that this model had a good prognostic prediction ability. GO analysis were mainly enriched in muscle system process， collagen-containing extracellular matrix and actin binding； KEGG analysis were mainly enriched in calcium signal pathway(P<0.05). There were significant differences in T cell co-inhibition， APC co-stimulation and co-inhibition between high and low risk groups from immunological function analysis(P<0.01). The high risk group had higher TMB compared to the low risk group， and prognosis of the high TMB group was worse than that of the low TMB group(P<0.05). PIK3CA was the gene with the highest mutation frequency in both high and low risk groups， followed by TP53. ［Conclusion］ The prognostic risk model composed of eight cuproptosis-related lncRNA was an independent prognostic factor for breast cancer， and could effectively predict the survival prognosis of breast cancer patients. |
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