| 卫鑫,吴波,陈勇全,等.构建铜代谢相关基因的肾透明细胞癌预后模型[J].安徽医药,2024,28(9):1801-1805. |
| 构建铜代谢相关基因的肾透明细胞癌预后模型 |
| The prognosis model of copper metabolism-related genes was established to predict the survival of patients with renal clear cell carcinoma |
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| DOI：10.3969/j.issn.1009-6469.2024.09.023 |
| 中文关键词: 肾透明细胞癌 铜代谢 基因组 癌症基因组图谱数据库 基因表达综合数据库 预后模型 |
| 英文关键词: Renal clear cell carcinoma Copper metabolism Genome TCGA database Gene expression omnibus Prognostic model |
| 基金项目:山西省 “1331工程”重点创新团队建设计划项目( 3c332019001) |
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| 中文摘要: |
| 目的构建基于铜代谢相关基因和临床病理学特征的新型肾透明细胞癌( ccRCC)预后模型。方法 2022年 6月至 2023年 1月从基因集数据库( MSigDB)5.1版整理出铜代谢相关基因，应用癌症基因组图谱( TCGA)中 ccRCC数据对获取的铜代谢相关基因进行生物信息学分析，得到 32个铜代谢相关差异基因；用单因素比例风险回归( Cox)分析铜代谢基因，筛选出具有预后价值的 60个铜代谢相关基因，两者取交集，得到 14个关键基因，对其进行基因本体论( GO)分析、京都基因和基因组数据库( KEGG)通路分析及关联性分析。之后对关键基因进行套索回归分析( LASSO)结果显示有 3个基因被纳入模型，计算公式为:风险分数=HAMP×0.205+TMPRSS6×0.097-CCND1×0.033。分别应用 TCGA数据，和基因表达综合数据库( GEO)中的 ccRCC数据集 GSE22541进行内部模型验证和外部模型验证；利用乘积极限法( Kaplan-Meier)生存曲线验证模型预后价值；利用受试者操作特征曲线( ROC曲线)下面积验证模型预测的准确性。结果生存状态图表明，高风险组死亡病例数多于低风险组； ROC曲线表明风险评分模型具备较好的预测能力，曲线下面积( AUC)均大于 0.6；Kaplan-Meier生存分析显示，高风险组总体生存率低于低风险组( P<0.05)。结合临床病理学特征(年龄、肿瘤分级、肿瘤分期)，构建诺莫( Nomogram)列线图，对病人预后具有较好的预测效果。结论基于铜代谢相关基因的预后风险评分模型可用于 ccRCC的预后预测，针对铜代谢相关基因设计靶点可能是治疗 ccRCC的一种新选择。 |
| 英文摘要: |
| Objective To construct a novel prognostic model of renal clear cell carcinoma (ccRCC) based on copper metabolism-relat- ed genes and clinicopathological features.Methods From June 2022 to January 2023, this study compiled the genes related to coppermetabolism from the Gene Set Database (MSigDB) version 5.1, and 32 copper metabolism-related differential genes were obtained bybioinformatics analysis using ccRCC data from the Cancer Genome Atlas (TCGA). The copper metabolism-related genes were analyzedby univariate proportional hazard regression (Cox), and 60 genes related to copper metabolism with prognostic value were screened out.The intersection of the two sets of genes was used to obtain 14 key genes, which were analyzed through Gene Ontology (GO), Kytot En-cyclopedia of Genes and Genomes (KEGG) pathway analysis and relationship analysis respectively. Then LASSO regression analysis(LASSO) was performed for key genes, and the results showed that three genes were included in the model. The calculation formulawas: risk score =HAMP×0.205+TMPRSS6×0.097-CCND1×0.033. TCGA data and ccRCC dataset GSE22541 in GEO database were used for internal and external model validation, respectively. The product limit method (Kaplan-Meier) survival curve was used to verifythe prognostic value of the model. The area under receiver operating curve (ROC) was used to verify the accuracy of the model predic-tion.Results The survival diagram showed that the number of deaths in the high-risk group was higher than that in the low-risk group.The ROC curve showed that the risk scoring model had good predictive ability, and the area under the curve (AUC) was greater than 0.6. Kaplan-Meier analysis showed that the overall survival rate of the high-risk group was lower than that of the low-risk group (P< 0.05). Then by combining the clinicopathological features (age, tumor grade, and tumor stage), constructed a Nomogram map to predictthe prognosis of patients, which proved to be effective.Conclusion The prognostic risk score model based on copper metabolism-relat- ed genes can be used to predict the prognosis of ccRCC, and the design of targets for copper metabolism-related genes may be a new treatment option for ccRCC. |
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