摘要
目的 基于生物信息学方法筛选并分析与结直肠癌(CRC)预后相关的铁死亡相关基因(FRGs),构建临床预后模型。方法 通过癌症基因组图谱(TCGA)数据库获取结直肠癌预后样本基因,单因素Cox回归分析后得到预后相关基因,从FerrDb数据库中获取FRGs,取交集后得到预后相关FRGs。通过LASSO-Cox回归分析构建预后模型。以预后模型的风险评分的中位数为临界值,将CRC患者分为高风险组和低风险组,并绘制生存曲线,使用受试者工作特征(ROC)曲线评价FRGs模型的诊断效能。采用单因素和多因素Cox回归分析确定影响患者总生存期(OS)的独立预后因素。通过GeneMANIA数据库对预后模型中的基因进行蛋白互作网络分析,同时通过Metascap数据库进行基因本体(GO)功能富集分析和京都基因与基因组百科全书(KEGG)通路富集分析。结果 通过LASSO-Cox回归分析共筛选出11个有预后价值的FRGs,并以此构建预后模型。Kaplan-Meier生存曲线显示,高风险组患者的OS较低风险组更短,差异有统计学意义(P<0.05)。ROC曲线提示预后模型诊断效果良好。单因素和多因素Cox回归分析显示,T分期、N分期、病理分期、年龄、残余瘤及风险评分是影响预后的独立因素(P<0.05)。蛋白互作网络分析结果提示其蛋白互作网络功能主要与大自噬、铁离子转运等相关。功能富集分析显示,11个预后相关FRGs在膜电位调控、自噬和逆行内源性大麻素信号等通路均有显著富集。结论 经生物信息学方法筛选出的11个预后相关FRGs组成的预后模型对CRC患者的预后有较好的预测价值,可能为CRC患者提供预后评估方面的依据。
Objective To screen and analyze ferroptosis-related genes(FRGs)associated with the prognosis of colorectal cancer(CRC)based on bioinformatics method,and to construct a clinical prognosis model.Methods Prognostic sample genes of CRC were obtained through The Cancer Genome Atlas(TCGA)database,prognostic related genes were obtained after univariate Cox regression analysis.FRGs were obtained from FerrDb database,and prognostic related FRGs were obtained after intersection.The prognosis model was constructed by LASSO-Cox regression analysis.With the median risk score of the prognostic model as the critical value,CRC patients were divided into high risk group and low risk group.Survival curves were plotted and receiver operating characteristic(ROC)curves were used to evaluate the diagnostic efficacy of the FRGs model.Univariate and multivariate Cox regression analysis were used to determine independent prognostic factors for overall survival(OS).GeneMANIA database was used for protein interaction network analysis of the genes in the prognostic model,while Metascap database was used for gene ontology(GO)functional enrichment analysis and Kyoto Encyclopedia of Genes and Genomes(KEGG)pathway analysis.Results A total of 11 FRGs with prognostic value were selected and the prognostic model was constructed.The survival curve indicated that the high risk group had worse prognosis than the low risk group,and the difference in OS was statistically significant(P<0.05).The ROC curve suggested that the FRGs model had a good diagnositc effect.Univariate and multivariate Cox regression analysis showed that T stage,N stage,pathological stage,age,residual tumor and risk score were independent prognostic risk factors(P<0.05).The result of protein interaction network analysis suggested that it was mainly related to macroautophagy and iron ion transport.Functional enrichment analysis showed that the 11 prognostic related FRGs were significantly enriched in regulation of membrane potential,autophagy and retrograde endocannabinoid signaling pathways.Conclusion The prognostic model consisting of 11 prognostic related FRGs screened by bioinformatics method has good prognostic value for CRC patients,and may provide basis for treatment and prognostic evaluation for CRC patients.
作者
李雯雯
骆子荣
余卫锋
贝佳敏
何桂花
钟彩玲
黄穗平
张北平
LI Wenwen;LUO Zirong;YU Weifeng;BEI Jiamin;HE Guihua;ZHONG Caiing;HUANG Suiping;ZHANG(Beiping.The Second Clinical Medical College,Guangzhou University of Chinese Medicine,Guangzhou 510405,China)
出处
《临床肿瘤学杂志》
CAS
2024年第2期160-167,共8页
Chinese Clinical Oncology
基金
广东省教育厅高校科研项目(2021ZDZX2059)
广州市科技计划项目(202201020377)
广东省方证重点实验室(2022B1212010012-2)。
关键词
结直肠癌
铁死亡
预后模型
生物信息学
Colorectal cancer
Ferroptosis
Prognostic model
Bioinformatics
