首页|整合数据库筛选胰腺导管腺癌铁死亡核心基因及预后分析

整合数据库筛选胰腺导管腺癌铁死亡核心基因及预后分析

Screening the core genes of ferroptosis in pancreatic ductual adenocarcinoma through intergrated databases and the prognosis analysis

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目的 筛选胰腺导管腺癌(pancreatic ductal adenocarcinoma,PDAC)铁死亡核心基因及预后分析.方法 基于基因表达综合(Gene Expression Omnibus,GEO)数据库,选取GSE71989数据集,借助limma包和铁死亡数据集对差异表达基因(differentially expressed genes,DEGs)进行筛选.利用Metascape数据库进行富集分析、String数据库建立蛋白质互作网络(protein-protein interaction network,PPI),采用最大集团中心性(matthews correlation coefficient,MCC)算法筛选预后相关核心基因.利用Kaplan Meier plotter、R、GEPIA2和TIMER数据库对所筛选的核心基因进行预后分析并验证,基于CMap数据库筛选PDAC潜在治疗药物.结果 筛选共得到2 038个DEGs,其中上调基因1 552个、下调基因486个,与铁死亡数据集相交,得到66个共同DEGs;京都基因和基因组百科全书(Kyoto Encyclopedia of Genes and Genomes,KEGG)通路分析显示,铁死亡相关基因富集通路包括铁死亡途径、白介素-17(interleukin-17,IL-17)信号通路、化学致癌作用-活性氧通路、过氧化物酶体增殖物活化受体(peroxisome proliferator-activated receptor,PPAR)信号通路和低氧诱导因子-1(hypoxia inducible factor-1,HIF-1)信号通路等;MCC算法得到预后核心靶点:还原氢氧化酶4(NADPH oxidase 4,NOX4)、小窝蛋白-1(caveolin-1,CAV1)、缺氧诱导因子 1α(hypoxia inducible factor 1 subunit alpha,HIF1A)、过氧化物酶体增殖因子激活受体 γ(peroxisome proliferator activated receptor gamma,PPARG)、白介素-6(interleukin-6,IL-6)、前列腺素内过氧化物合成酶2(prostaglandin-endoperoxide synthase 2,PTGS2);验证分析提示核心基因均为高表达基因、与患者总生存率密切且均具有诊断价值(P<0.05);免疫浸润表明NOX4、HIFIA和IL-6基因与巨噬细胞、中性粒细胞浸润水平呈正相关关系(P<0.05).NOX4、CAV1和H1F1A基因与CD8+、DC细胞浸润水平呈正相关关系(P<0.05).筛选所得小分子药物包括HU-211、伊斯平斯和熊果酸,且所得小分子均与PDAC有较强相关性.结论 基于整合数据库筛选所得铁死亡相关基因(NOX4、CAV1、HIF1A、PPARG、IL-6、PTGS2)对PDAC有较高诊断价值,可能为预后相关诊断指标.
Objective To screen the core genes of ferroptosis and analyze the prognosis of pancreatic ductal adenocarcinoma(PDAC).Methods Based on the Gene Expression Omnibus(GEO)database,the GSE71989 data set was selected,and the differentially expressed genes(DEGs)were screened by limma package and ferroptosis data set.The Metascape database was used for enrichment analysis,the String database was used to establish a protein-protein interaction network(PPI),and the matthews correlation coefficient(MCC)algorithm was used to screen for prognostic related core genes.The Kaplan Meier plotter,R,GEPIA2 and TIMER databases were used to analyze and verify the prognosis of the selected core genes.The CMap database was used to screen potential therapeutic drugs for PDAC.Results A total of 2 038 DEGs were screened,of which 1 552 genes were up-regulated and 486 genes were down-regulated.Intersecting with the ferroptosis data set,66 common DEGs were obtained.Kyoto Encyclopedia of Genes and Genomes(KEGG)pathway analysis showed that the enrichment pathways of ferroptosis-related genes included ferroptosis pathway,interleukin-17(IL-17)signaling pathway,chemical carcinogenesis-reactive oxygen species pathway,peroxisome proliferator-activated receptor(PPAR)signaling pathway and hypoxia inducible factor-1(HIF-1)signaling pathway.The pre-core targets:NADPH oxidase 4(NOX4),caveolin-1(CAV1),hypoxia inducible factor 1 subunit alpha(HIF1A),peroxisome proliferator activated receptor gamma(PPARG),interleukin-6(IL-6),prostaglandin-endoperoxide synthase 2(PTGS2)were obtained by MCC algorithm.The verification analysis suggested that the core genes were highly expressed genes,closely related to the overall survival rate of patients and had diagnostic value(P<0.05).Immune infiltration showed a significant positive correlation(P<0.05)between NOX4,HIF1A,IL-6 genes and macrophage and neutrophil infiltration levels.The NOX4,CAV1 and H1F1A genes were significantly positively correlated with CD8+and DC cell infiltration levels(P<0.05).The selected small molecule drugs include HU-211,ispinesib,and ursolic acid,all of which have a strong correlation with PDAC.Conclusion The ferroptosis-related genes(NOX4,CAV1,HIF1A,PPARG,IL-6,PTGS2)screened based on the integrated database have a high diagnostic value for PDAC and may be a prognostic diagnostic indicator.

Pancreatic ductal adenocarcinomaFerroptosisPrognostic analysisBioinformatics

牛旭东、许书齐、叶磊、鞠成林、王小艺、包亚男、牛占军

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齐齐哈尔医学院基础医学院(黑龙江齐齐哈尔 161006)

齐齐哈尔医学院药学院(黑龙江齐齐哈尔 161006)

内蒙古医科大学第三附属医院耳鼻咽喉科(内蒙古包头 014030)

胰腺导管腺癌 铁死亡 预后分析 生物信息学

黑龙江省大学生创新训练计划

X202311230009

2024

数理医药学杂志
武汉大学,中国工业与应用数学学会,医药数学专业委员会

数理医药学杂志

影响因子:0.479
ISSN:1004-4337
年,卷(期):2024.37(5)
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