非凋亡调节性细胞死亡基因对肺腺癌预后的预测价值及其生物学功能  

Prognostic Prediction Value and Biological Functions of Non-Apoptotic Regulated Cell Death Genes in Lung Adenocarcinoma

作　　者：李浩令 王俊贤 戴恒文 刘俊杰 刘子扬 邹明远 张雷[1,3] 王文锐 Hao-Ling Li;Jun-Xian Wang;Heng-Wen Dai;Jun-Jie Liu;Zi-Yang Liu;Ming-Yuan Zou;Lei Zhang;Wen-Rui Wang(Anhui Province Key Laboratory of Translational Cancer Research,Bengbu Medical College,Bengbu,Anhui 233030,China;Department of Clinical Medicine,Bengbu Medical College,Bengbu,Anhui 233030,China;Department of Life Sciences,Bengbu Medical College,Bengbu,Anhui 233030,China;Department of Thoracic Surgery,First Affiliated Hospital of Bengbu Medical College,Bengbu,Anhui 233030,China)

机构地区：[1]蚌埠医学院癌症转化医学安徽省重点实验室,安徽蚌埠233000 [2]蚌埠医学院临床医学院,安徽蚌埠233000 [3]蚌埠医学院生命科学学院,安徽蚌埠233000 [4]蚌埠医学院第一附属医院胸外科,安徽蚌埠233000

出　　处：《Chinese Medical Sciences Journal》2023年第3期178-190,I0002,共14页中国医学科学杂志（英文版）

基　　金：安徽省自然科学基金(2108085MH294)。

摘　　要：目的探究非凋亡调节性细胞死亡基因(non-apoptotic regulatory cell death genes,NARCDs)预测肺腺癌预后的价值及其潜在生物学功能。方法我们下载癌症基因组图谱(The Cancer Genome Atla,TCGA)及基因表达综合(Gene Expression Omnibus,GEO)数据库中肺腺癌的转录组数据。使用R软件分析在肺腺癌组织和正常组织之间存在差异表达的NARCDs。采用单变量Cox分析和LASSO-Cox回归分析构建NARCDs预后模型。采用生存分析曲线、受试者工作特征曲线、单因素和多因素Cox回归分析评估NARCDs预后模型对肺腺癌预后的预测能力。采用GSVA、GO和KEGG分析NARCDs预后模型的功能富集情况。此外,我们分析了高、低NARCDs评分组间的肿瘤突变负担、肿瘤微环境、肿瘤免疫功能障碍与排斥(Tumor Immune Dysfunction and Exclusion,TIDE)评分和化疗药物敏感性的差异。最后,使用STRING和Cytoscape软件构建NARCDs与免疫相关基因的蛋白-蛋白相互作用网络。结果我们确定了34个与预后相关的差异表达NARCDs,其中16个基因(ATIC、AURKA、CA9、ITGB4、DDIT4、CDK5R1、CAV1、RRM2、GAPDH、SRXN1、NLRC4、GLS2、ADRB2、CX3CL1、GDF15和ADRA1A)被用于构建NARCDs预后模型。NARCDs风险评分是肺腺癌的独立预后因素(P<0.001)。功能分析显示:高NARCDs评分组与低NARCDs评分组间在错配修复、p53信号通路和细胞周期方面存在显著差异(P<0.05)。低NARCD评分组的肿瘤突变负荷较低,免疫评分及TIDE评分较高,对药物的敏感性较低(P<0.05)。此外,蛋白-蛋白相互作用网络的10个关键基因(CXCL5、TLR4、JUN、IL6、CCL2、CXCL2、ILA、IFNG、IL33和GAPDH)均为免疫相关基因。结论基于16个基因构建的NARCDs预后模型是肺腺癌的独立预后因素,其能有效预测患者预后,并为临床治疗提供帮助。Objective To explore the potential biological functions and prognostic prediction values of non-apoptotic regulated cell death genes(NARCDs)in lung adenocarcinoma.Methods Transcriptome data of lung adenocarcinoma were downloaded from The Cancer Genome Atlas and Gene Expression Omnibus databases.We identified differentially expressed NARCDs between lung adenocarcinoma tissues and normal tissues with R software.NARCDs signature was constructed with univariate Cox regression analysis and the least absolute shrinkage and selection operator Cox regression.The prognostic predictive capacity of NARCDs signature was assessed by Kaplan-Meier survival curve,receiver operating characteristic curve,and univariate and multivariate Cox regression analyses.Functional enrichment of NARCDs signature was analyzed with gene set variation analysis,Gene Ontology,and Kyoto Encyclopedia of Genes and Genomes.In addition,differences in tumor mutational burden,tumor microenvironment,tumor immune dysfunction and exclusion score,and chemotherapeutic drug sensitivity were analyzed between the high and low NARCDs score groups.Finally,a protein-protein interaction network of NARCDs and immune-related genes was constructed by STRING and Cytoscape software.Results We identified 34 differentially expressed NARCDs associated with the prognosis,of which 16 genes(ATIC,AURKA,CA9,ITGB4,DDIT4,CDK5R1,CAV1,RRM2,GAPDH,SRXN1,NLRC4,GLS2,ADRB2,CX3CL1,GDF15,and ADRA1A)were selected to construct a NARCDs signature.NARCDs signature was identified as an independent prognostic factor(P<0.001).Functional analysis showed that there were significant differences in mismatch repair,p53 signaling pathway,and cell cycle between the high NARCDs score group and low NARCDs score group(all P<0.05).The NARCDs low score group had lower tumor mutational burden,higher immune score,higher tumor immune dysfunction and exclusion score,and lower drug sensitivity(all P<0.05).In addition,the 10 hub genes(CXCL5,TLR4,JUN,IL6,CCL2,CXCL2,ILA,IFNG,IL33,and GAPDH)in protein-protein interac

关 键 词：肺腺癌 非凋亡调节性细胞死亡 预后模型 肿瘤免疫微环境 免疫治疗 

分 类 号：R734.2[医药卫生—肿瘤]