| 徐春林,庞琳,王凌霄,等.基于铜死亡相关长链非编码 RNA的结直肠癌预后模型构建及分析[J].安徽医药,2025,29(6):1185-1189. |
| 基于铜死亡相关长链非编码 RNA的结直肠癌预后模型构建及分析 |
| Construction and analysis of a prognostic model for colorectal cancer based on cuproptosis-related lncRNA |
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| DOI：10.3969/j.issn.1009-6469.2025.06.024 |
| 中文关键词: 结直肠肿瘤 铜死亡 长链非编码 RNA 预后模型 生物标志物 |
| 英文关键词: Colorectal neoplasms Cuproptosis Long-stranded non-coding RNA Prognostic model Biomarkers |
| 基金项目:山西省医学重点科研项目计划( 2022XM25)；山西省基础研究计划项目( 202103021224379) |
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| 摘要点击次数: 62 |
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| 中文摘要: |
| 目的基于铜死亡相关长链非编码 RNA(lncRNA)构建结直肠癌预后风险评分模型，评估病人预后，为结直肠癌病人的治疗提供新靶点。方法 2022年 10月至 2023年 1月，从 TCGA数据库中下载结直肠癌 RNA的测序数据及临床信息，通过 Pearson相关性计算筛选出铜相关的 lncRNA并使用 “ConsensusClusterPlus”包进行无监督共识聚类，识别铜死亡 lncRNA的潜在亚型，对亚型间的差异 lncRNA进行单因素 Cox和 LASSO回归筛选预后相关的 lncRNA构建风险评分，根据风险评分的中位数，将病人划分为高、低风险两组，进行生存差异分析；通过绘制 Kaplan-Meier曲线、受试者操作特征曲线( ROC曲线)、生存状态图等评估模型的预测能力，并在 GEO数据集上进行验证。最终对高低风险组差异基因进行功能富集。结果共筛选出 2 508个铜死亡相关基因，通过无监督聚类识别了铜死亡相关 lncRNA的潜在亚型，两亚组之间存在生存差异；根据亚组间的差异基因与 GEO数据集的基因取交集，共得到 122个铜死亡相关 lncRNA，通过单因素 Cox分析、 LASSO回归筛选出 6个预后相关的 lncRNA构建预后风险评分模型。通过 log-rank检验发现高低风险两组存在明显的生存差异。 TCGA数据集 1、3、5年的 ROC曲线曲线下面积( AUC)分别为 0.63(0.54，0.72)、 0.67(0.59，0.74)、 0.69(0.57，0.77)GEO数据集 1、3、5年的 ROC曲线 AUC分别为 0.66(0.56，0.76)、 0.66(0.59，0.76)、 0.68(0.60，0.78)；联合临床信息多因素 Cox结果，显示，预后风险评分可以作为较好的独立预后因子。结论该研究构建了包含 RPARP-AS1、ZEB1-AS1、LINC01138、ZNF674-AS1、ALMS1-IT1、LINC01410等 6种铜死亡相关的 lncRNA的风险特征，可作为结直肠癌潜在的分子生物标志物和治疗靶点。此外，联合风险特征与临床信息构建的 Cox回归预测模型将在临床工作中辅助判断结直肠癌病人的预后，帮助病人制定个体化治疗策略。 |
| 英文摘要: |
| Objective To construct a prognostic risk score model for colorectal cancer based on cuproptosis-related long-stranded non-coding RNAs (lncRNAs) to assess patient prognosis and provide new targets for the treatment of colorectal cancer patients.Methonds From October 2022 to January 2023, the sequencing data and clinical information of colorectal cancer RNAs were down-loaded from TCGA database, and cuproptosis-related lncRNAs were screened by Pearson correlation calculation and unsupervised con-sensus clustering was performed using "ConsensusClusterPlus" package to identify potential isoforms of cuproptosis-related lncRNAs, and the differences between the isoforms were analyzed. The prognosis-related lncRNAs were screened by one-way Cox and LASSO re-gression to construct risk scores, and patients were divided into two groups of high and low risk according to the median risk scores, andsurvival difference analysis was performed; the predictive ability of the model was evaluated by plotting Kaplan-Meier curves, ROCcurves, and survival status maps, and validated on the GEO dataset. Finally, functional enrichment was performed for differential genesin high and low risk groups.Results A total of 2 508 cuproptosis-related genes were screened, and potential isoforms of cuproptosis-re-lated lncRNAs were identified by unsupervised clustering, and survival differences existed between two subgroups; a total of 122 cupro-ptosis-related lncRNAs were obtained based on the differential genes between subgroups and the GEO dataset by taking the intersec-tion, and 6 prognosis-related lncRNAs were screened by one-way Cox analysis and LASSO regression lncRNAs to construct a prognos-tic risk score model. Significant survival differences were found between the two groups of high and low risk by log-rank test.The area under the ROC going curve AUC for 1,3,5 years in TCGA dataset was 0.63(0.54, 0.72), 0.67(0.59, 0.74) and 0.69(0.57, 0.77), respec-tively, and the area under the ROC curve AUC for 1,3,5 years in GEO dataset was 0.66(0.56, 0.76), 0.66(0.59, 0.76) and 0.68(0.60,0.78), respectively; the combined clinical information multi factor Cox results showed that prognostic risk scores could be used as inde-pendent prognostic factors independently of other clinical traits.Conclusions In this study, we constructed a risk profile containing six copper death-associated lncRNAs, including RPARP-AS1, ZEB1-AS1, LINC01138, ZNF674-AS1, ALMS1-IT1, and LINC01410,which can be used as potential molecular biomarkers and therapeutic targets for colorectal cancer. In addition, the Cox regression pre-diction model constructed by combining the risk profiles with the clinical information will assist in the clinical work of judging the clini-cal prognosis of colorectal cancer patients and help patients to develop individualized treatment strategies. |
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