基于与铁死亡相关基因的肺腺癌的新型预后模型构建  

Construction of A Novel Prognostic Model of Lung Adenocarcinoma Based on Genes Related to Iron Death

作　　者：张祥鑫 董星星 耿涛[2] 赵伯文 魏育涛 ZHANG Xiang-xin;DONG Xing-xing;GENG Tao;ZHAO Bo-wen;WEI Yu-tao(School of Medicine,Shihezi University,Shihezi 832008,Xinjiang,China;Department of Cardiothoracic Surgery,the First Affiliated Hospital of School of Medicine,Shihezi University,Shihezi 832008,Xinjiang,China;Jining NO.1 People's Hospital,Jining 272000,Shandong,China)

机构地区：[1]石河子大学医学院,新疆石河子832008 [2]石河子大学医学院第一附属医院心胸外科,新疆石河子832008 [3]济宁市第一人民医院,山东济宁272000

出　　处：《医学信息》2021年第15期96-101,共6页Journal of Medical Information

基　　金：国家自然科学基金资助项目(编号:81460059);兵团卫生科技-兵团博士基金(编号:2014BB019)。

摘　　要：目的利用TCGA数据库建立肺腺癌患者铁死亡相关基因的预后风险预测模型。方法在TCGA数据库中获取肺腺癌及正常肺组织的基因表达谱及相关临床数据,筛选出铁死亡相关的差异表达基因,采取单、多因素Cox风险回归模型筛选并建立基因预后风险预测模型,分析模型中的基因与临床病理特征相关性。结果共得到铁死亡相关差异基因63个(|logFC|≥1,FDR<0.05),基于单因素及多因素Cox回归分析结果,构建了由5个mRNA的多因素预后风险预测模型:风险评分=0.234×NOX1+0.229×ALOX12B+0.006×SLC2A1+0.019×RRM2+0.003×CAV1。患者风险评分结果提示高评分患者较低评分患者预后较差。结论NOX1、ALOX12B、SLC2A1、RRM2和CAV1的风险预测模型可以有效的对肺腺癌患者的预后进行预测,且NOX1、SLC2A1及RRM2与患者的临床病理特征相关,有望对肺腺癌的临床治疗起到指导作用。Objective To use the TCGA database to establish a prognostic risk prediction model for iron death-related genes in patients with lung adenocarcinoma.Methods To obtain gene expression profiles and related clinical data of lung adenocarcinoma and normal lung tissues from the TCGA database,and screen out differentially expressed genes related to iron death.Adopting single and multivariate Cox risk regression models to screen and establish a genetic prognostic risk prediction model,and analyze the correlation between genes in the model and clinicopathological characteristics.Results A total of 63 differential genes related to iron death(|logFC|≥1,FDR<0.05)were obtained.Based on the results of univariate and multivariate Cox regression analysis,a multivariate prognostic risk prediction model based on 5 mRNAs was constructed:risk score=0.234×NOX1+0.229×ALOX12B+0.006×SLC2A1+0.019×RRM2+0.003×CAV1.The patient risk score results suggest that patients with high scores and low scores had a poorer prognosis.Conclusion The risk prediction models of NOX1,ALOX12B,SLC2A1,RRM2 and CAV1 can effectively predict the prognosis of patients with lung adenocarcinoma.Moreover,NOX1,SLC2A1 and RRM2 are related to the clinicopathological characteristics of patients,and they are expected to play a guiding role in the clinical treatment of lung adenocarcinoma.

关 键 词：肺腺癌 铁死亡 预后模型 

分 类 号：R734.2[医药卫生—肿瘤]