基于铁死亡相关长链非编码RNA对子宫内膜癌的预后分析  

Prognostic analysis of endometrial cancer based on long non-coding RNA associated with ferroptosis

作　　者：宋玉莹 崔琳[2] 孙蕾 何玉凤 祝慧颖 王飞[3] 李焱[3] ONG Yuying;CUI Lin;SUN Lei;HE Yufeng;ZHU Huiying;WANG Fei;LI Yan(The First School of Clinical Medicine,Henan University of Chinese Medicine,Henan Province,Zhengzhou 450008,China;Central Laboratory and Cardiac Center,the First Affiliated Hospital of Henan University of Chinese Medicine,Henan Province,Zhengzhou 450000,China;Physical Examination Center,the First Affiliated Hospital of Henan University of Chinese Medicine,Henan Province,Zhengzhou 450000,China)

机构地区：[1]河南中医药大学第一临床医学院,河南郑州450008 [2]河南中医药大学第一附属医院中心实验室及心脏中心,河南郑州450000 [3]河南中医药大学第一附属医院体检中心,河南郑州450000

出　　处：《中国医药导报》2023年第23期28-33,42,共7页China Medical Herald

基　　金：河南省中医药科学研究专项课题(2022JDZX079)。

摘　　要：目的利用子宫内膜癌(EC)的铁死亡相关长链非编码RNA(lncRNA),构建疾病预后模型,为EC提供治疗靶点。方法TCGA中下载EC数据,FerrDb数据库下载铁死亡基因。通过R软件将疾病数据与铁死亡基因取交集得到差异表达基因(DEGs),并对DEGs进行GO和KEGG富集分析。根据Pearson相关分析,得到的lncRNA,通过Lasso-Cox构建模型,筛选用于构建模型的lncRNA,计算风险评分,以中位数区分高、低风险组。利用Kaplan-Meier生存曲线、受试者操作特征(ROC)、决策曲线分析法(DCA)曲线等评估模型的风险评分及临床特征,预测生存效果。应用TIMER、CIBERSORT等算法,对高、低风险组免疫细胞及功能进行分析。用R软件对两组lncRNA进行免疫细胞、功能、检查点及m6A的相关mRNA差异表达分析。结果248个铁死亡基因,与铁死亡相关lncRNA有1616个,80个DEGs。GO分析结果显示,DEGs主要参与氧化应激、调控基底质膜、有机阴离子跨膜转运蛋白活性等。KEEG结果显示,DEGs主要参与铁死亡、谷胱甘肽代谢通路等过程。单因素Cox回归分析初步筛选出45个与铁死亡相关的lncRNA,经Lasso-Cox优化,最终确定14个lncRNA用于构建预后模型。Kaplan-Meier生存曲线显示,低风险组生存状况优于高风险组(P<0.01)。ROC、DCA曲线显示:风险评分预测的生存效果优于传统临床特征;1、2、3年的AUC值分别为0.696、0.715、0.747。单因素、多因素Cox回归分析结果显示,模型风险评分是预测EC患者生存的独立影响因素[HR(95%CI)=1.105(1.081~1.129);HR(95%CI)=1.108(1.081~1.135),均P<0.01]。高、低风险组免疫细胞分布热图显示,两组免疫细胞水平比较,差异有统计学意义(P<0.05)。免疫功能结果显示,高、低风险组APC共同刺激、CCR等比较,差异有统计学意义(P<0.05);免疫检查点结果显示,高、低风险组PDCD-1、CTLA-4比较,差异有统计学意义(P<0.05);m6A甲基化结果显示,高、低风险组YTHDF1、FTO比较,差异有�Objective To construct a disease prognosis model of endometrial carcinoma(EC)by using long non-coding RNA(lncRNA)associated with ferroptosis,and to provide therapeutic targets for EC.Methods EC data was downloaded from TCGA and ferroptosis gene was downloaded from FerrDb database.Through R software,disease data and ferroptosis genes were intersected to obtain differentially expressed genes(DEGs),and GO and KEGG enrichment analysis of DEGs was performed.The lncRNA obtained according to Pearson correlation analysis.The model was constructed by Lasso-Cox,and the lncRNA used to construct the model was screened.The risk score was calculated,and the high and low risk groups were distinguished by the median value.Kaplan-Meier survival curve,receiver operating characteristics(ROC)and decision curve analysis(DCA)curves were used to evaluate the risk score of the model and clinical characteristics,and to predict the survival effect.TIMER,CIBERSORT,and other algorithms were used to analyze the immune cells and functions in high and low risk groups.R software was used to analyze the immune cell,function,checkpoint,and mRNA differential expression of m6A between the two groups of lncRNA.Results There were 248 ferroptosis genes,1616 lncRNA associated with ferroptosis and 80 DEGs.The results of GO analysis showed that DEGs was mainly involved in oxidative stress,regulating the basement membrane and organic anion transmembrane transporter activity.KEEG results showed that DEGs was mainly involved in ferroptosis and glutathione metabolic pathway,and so on.Univariate Cox regression analysis initially screened 45 lncRNA associated with ferroptosis.After Lasso-Cox optimization,14 lncRNA were finally identified for the construction of prognosis models.Kaplan-Meier survival curve showed that the survival status of low risk group was better than that of high risk group(P<0.01).ROC and DCA curves showed that risk score predicted survival better than traditional clinical characteristics;the AUC values of the first,second and third years

关 键 词：子宫内膜癌 铁死亡 长链非编码RNA 免疫浸润 数据挖掘 

分 类 号：R737.33[医药卫生—肿瘤]