摘要
目的采用生物学信息方法分析铁死亡相关基因并构建肺鳞癌预后模型。方法在癌症基因组图谱(TCGA)中获得所有肺鳞癌转录组数据及临床资料,从现有文献中查找铁死亡相关基因,使用R软件筛选出与预后相关的铁死亡相关基因,再进一步使用LASSO回归分析的方法筛选关键铁死亡相关基因,进行预后模型的构建。根据每例患者计算得出的LASSO回归系数,按照风险评分高低分为高风险组和低风险组,再对构建的模型进行Kaplan-Meier生存分析、受试者接受特征(ROC)曲线评价。高风险组和低风险组进行差异基因分析,并分析差异基因富集状态和免疫功能状态。鉴定该预后模型是否独立于其他临床特征的预后因子的方法则采用多因素Cox回归。结果单因素Cox回归分析显示,在52个差异表达的铁死亡相关基因中,5个基因可能与预后相关,这5个基因经LASSO回归分析也确认为影响预后的关键基因。此外,生存分析结果显示,低风险组(中位生存期是2.26年)的预后较高风险组好(中位生存期1.65年),差异有统计学意义(P<0.001),ROC曲线显示模型3年生存率的曲线下面积为0.635,5年生存率的曲线下面积为0.619。高风险组和低风险组的差异基因富集于铁代谢生物学功能和免疫相关通路,且两组间存在着免疫功能状态的差异。多因素Cox回归分析结果表明,该预后模型可以作为一个独立的预后因子(HR=2.893,95%置信区间1.687~3.379,P<0.05)。结论通过生物信息学分析,该研究成功建立了基于5个铁死亡相关基因的肺鳞癌预后模型,该模型可以帮助实施患者个体化预后评估和免疫状态的分析,可能对肺鳞癌的个体化治疗提供一定依据。
Objective To analyze iron death related genes and construct prognosis model of lung squamous cell carcinoma by Biological information method.Methods The transcriptome and clinical data of lung squamous cell carcinoma from the Cancer Genome Atals(TCGA)database was downloaded.Searched for iron death-related genes from the existing literature,use R software to screen iron death-related genes related to prognosis,and then use LASSO regression analysis to screen key iron death-related genes,and construct a prognostic model.According to the LASSO regression coefficient,the risk score of each patient was calculated,and the patients were divided into high-risk group and low-risk group.Kaplan-Meier survival analysis and subject acceptance characteristic(receiver operating characteristic,ROC)curve were used to evaluate the model.The differential genes in the high-risk group and the low-risk group were analyzed,and the enrichment analysis and immune function status analysis of the differential genes were carried out.Finally,multivariate Cox regression was used to determine whether the prognostic model was a prognostic factor independent of other clinical features.Results Univariate Cox regression analysis showed that among the 52 differentially expressed genes related to iron death,5 genes may be related to prognosis,and these 5 genes were also confirmed as key genes affecting prognosis by LASSO regression analysis.In addition,the results of the survival analysis showed that the low-risk group had a better prognosis than the high-risk group(Median survival was 1.65 years in the high-risk group and 2.26 years in the low-risk group,P<0.001)The ROC curve showed that the area under the 3-year survival rate was 0.635,and the area under the 5-year survival rate was 0.619.The differential genes between high-risk group and low-risk group were mainly enriched in immune-related pathways,and there were differences in immune function between the two groups.Multivariate Cox regression analysis showed that the prognostic model could be u
作者
胡文龙
梁惠芳
李明
HU Wenlong;LIANG Huifang;LI Ming(Department of Respiratory Medicine,The Affiliated ShunDe Hospital of Ji Nan University,Foshan,Guangdong 528000,China)
出处
《现代医药卫生》
2021年第11期1840-1845,1849,共7页
Journal of Modern Medicine & Health
关键词
铁死亡
预后模型
肺鳞癌
生物信息学
Ferroptosis
Prognostic model
Squamous cell lung carcinoma
Bioinformatics
