CN114645088B - 克罗恩病进展风险相关评估基因集、试剂盒、应用和系统 - Google Patents
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Abstract
本发明提供了一种克罗恩病进展风险相关评估基因集、试剂盒、应用和系统。其中克罗恩病中NLRP3炎症小体相关基因的评估基因集,用于评估克罗恩病进展的高风险表型或低风险表型,评估基因集包括14个基因,14个基因为CARD8、CASP1、GBP5、HSP90AB1、MEFV、NFKB1、NFKB2、NLRC3、DHX33、PANX1、PSTPIP1、RELA、TXN和TLR6。本发明能更可靠地应用到临床实践中,相较于现有技术,可以更准确地评估克罗恩病进展的高风险表型或低风险表型,同时能够为医疗决策提供科学依据。
Description
技术领域
本发明涉及一种克罗恩病中NLRP3炎症小体相关基因的评估基因集、用于评估克罗恩病进展的高风险表型或低风险表型的试剂盒、应用以及用于评估个体是否处于克罗恩病进展的高风险或低风险的系统。
背景技术
克罗恩病是炎症性肠病(IBD)的两种主要形式之一,可导致复发和缓解消化道炎症。据估计,在欧洲和北美分别有超过200万和150万人患有IBD;虽然在西方国家,IBD的发病率相对稳定,但在西方化社会较多的新工业化国家,克罗恩病的发病率仍在上升。尽管治疗取得了进展,但克罗恩病的发病率很高,医疗费用也很高。
在过去的20年里,在克罗恩病的治疗领域取得了重大进展。对克罗恩病发病机制中所必需的炎症级联反应、细胞因子和粘附分子的深入了解在很大程度上促进了药理设备的发展,例如,抗肿瘤坏死因子(TNF)药物英夫利昔单抗对治疗克罗恩病有效,具有良好的安全性(帕帕米克尔等,2019年)。然而,相当比例的患者证明生物制剂没有临床益处,反应可能随着时间的推移而消失(Danese等人,2019年;帕帕迈克尔等人,2019年)。确定新的靶点和开发新的治疗方法对帮助治疗克罗恩病至关重要。
克罗恩病的病因仍然很大程度上是未知的,因此,明确其发病机制是重要的。众所周知,炎症小体参与了各种系统中的炎症性疾病(Huangetal.,2021),nod样受体家族,含pyrin结构域3(NLRP3)炎症小体,作为检测广泛信号的细胞内传感器,在所有类型的炎症小体中研究最为广泛(Swansonetal.,2019)。据报道NLRP3炎症小体参与了克罗恩病的发病机制、进展和治疗反应(Shaoetal.,2019),但是克罗恩病中NLRP3炎症小体及其相关分子的相关性仍为清晰,需要进一步研究。
发明内容
本发明的目的在于提供克罗恩病中NLRP3炎症小体相关基因的评估基因集、试剂盒、应用和系统,以用于评估克罗恩病进展的高风险表型或低风险表型。
本发明的第一目的是提供一种克罗恩病中NLRP3炎症小体相关基因的评估基因集,其用于评估克罗恩病进展的高风险表型或低风险表型,评估基因集包括14个基因,14个基因为CARD8、CASP1、GBP5、HSP90AB1、MEFV、NFKB1、NFKB2、NLRC3、DHX33、PANX1、PSTPIP1、RELA、TXN和TLR6。
其中评估基因集中的14个基因选自25个NLRP3炎症小体相关基因,25个NLRP3炎症小体相关基因分别为GBP5、CASP1、CARD8、TLR4、TLR4、PANX1、NLRP1、NLRP3、TLR6、PSTPIP1、NFKB1、NFKB2、HSP90AB1、NLRB3、RELA、MEFV、DHX33、TXN、APP、SIRT2、SUGT1、P2RX7、EIF2AK2、CD36、TXNIP、GSDMD和PYCARD。
本发明的第二目的是提供一种用于评估克罗恩病进展的高风险表型或低风险表型的试剂盒,其包括检测前述评估基因集中14个基因表达水平的试剂,具体的,14个基因为CARD8、CASP1、GBP5、HSP90AB1、MEFV、NFKB1、NFKB2、NLRC3、DHX33、PANX1、PSTPIP1、RELA、TXN和TLR6。
本发明的第三目的是提供一种检测14个NLRP3炎症小体相关基因表达水平的试剂盒在克罗恩病诊断产品中的应用,其中试剂盒为前述包括检测前述评估基因集中14个基因表达水平的试剂的试剂盒。
本发明的第四目的是提供一种用于评估个体是否处于克罗恩病进展的高风险或低风险的系统,系统包括计算机,计算机被配置为使用计算机模型和机器学习模型,计算机模型基于从已知患有克罗恩病的个体获得基因表达数据,根据个体的多基因表达数据计算克罗恩病进展风险评分以确定该个体是否具有高风险表型或低风险表型,其中个体的多基因表达数据为前述评估基因集中14个基因表达数据,14个基因及其对应的权重系数如下所示:
编号 | 基因 | 权重系数 | 编号 | 基因 | 权重系数 | 编号 | 基因 | 权重系数 |
1 | CARD8 | 0.139 | 6 | NFKB1 | 0.224 | 11 | PSTPIP1 | 0.140 |
2 | CASP1 | 0.943 | 7 | NFKB2 | 0.173 | 12 | RELA | -0.077 |
3 | GBP5 | 0.578 | 8 | NLRC3 | -0.365 | 13 | TXN | -0.395 |
4 | HSP90AB1 | 0.334 | 9 | DHX33 | -0.581 | 14 | TLR6 | 0.492 |
5 | MEFV | -0.329 | 10 | PANX1 | 0.676 | / | / | / |
进一步的,机器学习模型包括单因素logistic回归分析模型和Lasso回归模型。
进一步的,机器学习模型还包括无监督的k均值共识聚类模型,其中相较于低风险表型,评估基因集中的14个基因在高风险表型中的表达水平上调。
本发明具备以下有益效果:
本发明所提供的一种克罗恩病中NLRP3炎症小体相关基因的评估基因集以及相应的试剂盒和应用,能更可靠地应用到临床实践中,相较于现有技术,可以更准确地评估克罗恩病进展的高风险表型或低风险表型,同时提供了一种用于评估个体是否处于克罗恩病进展的高风险或低风险的系统,能够为医疗决策提供科学依据。
下面结合附图对本发明作进一步的详细说明。
附图说明
图1是GSE100833样本的临床信息汇总。
图2是25个NLRP3炎症小体相关基因的特征及相互作用,其中2A中的Circos图显示了24条染色体中NLRP3炎性小体基因的位置,2B中显示了蛋白-蛋白相互作用网络,其中红色表示正相关,蓝色表示负相关。
图3是NLRP3炎症小体相关基因的表达,其中3A中Box图显示了GSE100833数据集的基因表达概述,蓝色:健康(非炎症)组织;红色:病变(炎症)组织;3B显示的火山图说明了GSE100833数据集中的差异表达基因(DEGs),绿色:下调,红色:上调,灰色:没有明显的变化;3C显示了GSE100833数据集中的DEG的热图,蓝色:来自健康对照组的组织;红色:来自克罗恩病患者的组织,ns:P≥0.05;*:P<0.05;**P≤0.01;***P≤0.001;****P≤0.0001。
图4是25个NLRP3炎症小体相关基因表达的相关性,其中4A是相关图显示基因间显著相关,紫色:正相关,黄色:负相关,颜色较深的圆圈表示更强的相关性;4B显示了全体样本中CARD8和GBP5表达的相关性;4C显示了疾病样本中TLR4和PANX1表达的相关性。
图5是建立LASSO回归模型和受试者工作特征(ROC)曲线,其中5A森林图显示了每个基因预测克罗恩病的比值比和95%置信区间;5B显示了与最小交叉验证误差点对应的基因的对数值;5C是选择具有非零系数的基因来构建模型;5D是预测模型中每个基因的系数;5E是Box图,显示疾病组(红色)和对照组(蓝色)的得分;5F是预测克罗恩病的ROC曲线,ns:P≥0.05;*:P<0.05;**P≤0.01;***P≤0.001;****P≤0.0001。
图6显示了NLRP3炎症小体相关基因和免疫微环境中的免疫细胞浸润,其中6A是两组间免疫细胞浸润水平的比较;6B是相关图显示基因与免疫细胞浸润显著相关,紫色:正相关,黄色:负相关,颜色较深的圆圈表示更强的相关性;6C是GBP5表达与CD4+T细胞浸润的相关性,6D和6E是小提琴图,分别显示了两组中激活的CD4+T细胞(6D)和GBP5的表达水平(6E);6F是CARD8表达与CD56暗淡自然杀伤细胞浸润的相关性;6G-6H是小提琴图,分别显示了两组中CD56dim的自然杀伤细胞(6G)和CARD8表达(6H)的水平,R表示皮尔逊相关系数,*:P<0.05;**P≤0.01;***P≤0.001;****P≤0.0001。
图7是NLRP3炎症小体相关基因及免疫相关通路,其中7A是两组间免疫相关通路激活情况的比较,7B是相关图,显示基因与免疫相关通路显著相关,紫色:正相关,黄色:负相关,颜色较深的圆圈表示更强的相关性;7C是GBP5表达与抗原加工及呈递途径的相关性;7D和7E是小提琴图,分别显示了两组中抗原加工和呈递途径(7D)和GBP5的表达水平(7E);7F是CASP1表达与TGFβ家族成员的相关性;7G和7H是小提琴图,分别显示了两组中TGFβ家族成员(7G)和CASP1表达(7H)的水平,R表示皮尔逊相关系数,*:P<0.05;**P≤0.01;***P≤0.001;****P≤0.0001。
图8是NLRP3炎症小体相关基因和HLA相关基因,其中8A是两组间HLA相关基因表达情况的比较;8B是相关图,显示NLRP3炎症小体与HLA相关基因之间存在显著相关性,紫色:正相关,黄色:负相关,颜色较深的圆圈表示更强的相关性;8C是CASP1表达与抗原加工及呈递途径的相关性;8D和8E是小提琴图,分别显示了两组中HLA-DMA(8D)和CASP1的表达(8E);8F是TXN和HLA-DMA表达之间的相关性;8G和8H是小提琴图,分别显示了两组中HLA-DMA(8G)和TXN(8H)的表达,R表示皮尔逊相关系数,ns:P≥0.05;*:P<0.05;**P≤0.01;***P≤0.001;****P≤0.0001。
图9是集群间免疫微环境的比较,其中9A是Box图,显示两簇免疫细胞浸润水平;9B是Box图,显示了HLA在两个簇中的表达;9C是Box图,说明了两个簇中的免疫反应,ns:P≥0.05;*:P<0.05;**P≤0.01;***P≤0.001;****P≤0.0001。
图10是基于NLRP3炎症小体相关基因的克罗恩病分类,其中10A是无监督共识聚类矩阵和最优聚类;10B是项目-共识图,显示了每个聚类之间的关系;10C是基于聚类结果的主成分分析(PCA);10D是热图和盒图,显示了每个簇中NLRP3炎症小体相关基因的表达。
图11是两个克罗恩病聚类的通路富集分析和基因集变异分析(GSVA),其中11A是根据GO途径(生物过程、分子功能和细胞成分)和KEGG途径的富集评分分析的前10个富集基因集;图11B是热图,显示了单个枢纽基因通过GSVA富集的通路。
图12是药物-基因相互作用网络和潜在的药物预测,其中图12A是蛋白质间相互作用的网络;图12B是关键基因的药物相互作用预测,DGIdb数据库中以4个关键基因(APP、HSP90AB1、NFKB1和TLR4)为靶点,从该数据库中预测了9种潜在的靶标药物,蓝色:NLRP3炎性小体相关基因,橙色:非NLRP3炎性小体相关基因,该点的大小表明药物与NLRP3炎症小体相关基因相互作用。
具体实施方式
为了能够更清楚地理解本发明的上述目的、特征和优点,下面结合附图和具体实施方式对本发明进行进一步的详细描述。需要说明的是,在不冲突的情况下,本申请的实施例及实施例中的特征可以相互组合。
在下面的描述中阐述了很多具体细节以便于充分理解本发明,但是,本发明还可以采用其他不同于在此描述的其他方式来实施,因此,本发明的保护范围并不限于下面公开的具体实施例的限制。
实施例中如无特殊说明,所采用的实验方法,均为常规方法;所用的实验材料、试剂等,均可通过商业途径得到。
实施例1
数据集和NLRP3炎症小体相关基因集
GSE100833和GSE16879的基因芯片数据及相应的临床信息从基因表达综合数据库(GEO)中下载。
数据集GSE100833包括159个来自克罗恩病(CD)患者炎症区域的样本和168个来自结肠癌患者非参与区域的正常组织样本(Petersetal.,2017)。
数据集GSE16879包括12个来自健康对照的样本和73个来自CD的样本(Arijsetal.,2009)。
数据集GSE100833的样本来自诊断时的外科手术切除样本,而数据集GSE16879的样本来自诊断时的内镜肠道活检样本(Arijs等人,2009;Peters等人,2017)。GSE100833样本临床信息汇总见附图1。人类NLRP3炎性小体基因从GO:0044546、GO:0072559以及R-HSA-844456获取,最终得到30个基因(PMID:33212483),其中GSE100833数据中有25个基因具有表达数据,因此本实施例基于这25个基因进行。
其中25个NLRP3炎症小体相关基因包括GBP5、CASP1、CARD8、TLR4、TLR4、PANX1、NLRP1、NLRP3、TLR6、PSTPIP1、NFKB1、NFKB2、HSP90AB1、NLRB3、RELA、MEFV、DHX33、TXN、APP、SIRT2、SUGT1、P2RX7、EIF2AK2、CD36、TXNIP、GSDMD和PYCARD,NLRPS炎性小体相关基因的PPI网络,其中NLRP3、toll样受体4(TLR4)和Caspase1(CASP1)具有更多的相互作用蛋白(图2B)。
NLRP3炎性小体基因PPI网络与染色体位置信息
在STRING数据库https://string-db.org/中获取NLRP3炎性小体基因的相互作关系,然后在Cytoscape中进行可视化,交互作用评分>0.4被认为具有统计学意义(Szklarczyk et al.,2019),利用R的Circos包对NLRP3炎性小体基因在染色质上的具体分布位置进行了展示(Krzywinski etal.,2009)。
NLRP3炎性小体差异基因表达鉴定及模型构建
对于GSE100833数据和GSE16879数据,发明人首先使用Wilcoxon检测分析了病变样本和正常组织中NLRP3炎症小体相关基因的表达,表示NLRP3炎性小体基因在疾病样本和正常样本间分布的差异一致性。随后,利用R的limma包,以校正后的P值adj.P<0.05为阈值筛选疾病和正常样本间的差异表达基因,并利用火山图和热图进行展示((Ritchie etal.,2015)。
在25个NLRP3炎症小体相关基因的基础上,与GSE100833的非炎症样本相比,共鉴定出14个DEGs(CARD8、CASP1、GBP5、HSP90AB1、MEFV、NFKB1、NFKB2、NLRC3、NLRP3、PANX1、PSTPIP1、RELA、TLR4和TLR6)(图3A),所有非炎症组织中的DEGs均下调(图3B-3C)。
在克罗恩病样本和正常组织样本中,发明人进一步验证了25个NLRP3炎症小体相关基因的相关性(图4A),在病变样本和正常样本中,CARD8和GBP5的表达之间存在很强的正相关关系(r=0.73,P<2.2e-16)(图4B),对于病变样本,TLR4和PANX1的表达之间存在很强的正相关关系(r=0.63,P<2.2e-16)(图4C)。
此外,在整体样本和疾病样本中基于表达水平分别描绘NLRP3炎性小体基因两两相关性,采用单因素logistic回归分析对与克罗恩病发展相关的deg进行分类;使用glmnet软件包进行LASSO cox回归,以去除冗余基因,并构建一个完善的模型,在prism8.0中进行了受试者工作特征(ROC)曲线和曲线下面积(AUC)的计算。
单因素logistic回归分析显示16个DEGs与克罗恩病相关(图5A),为了进一步选择预测基因,发明人进行了Lasso回归分去冗余,16个基因中有14个被纳入了预测模型(图5B,图5C),各基因的系数如图5D和下表所示:
由图5E可以看出,通过预测模型计算出的患病样本得分明显较高,ROC曲线显示,预测模型的AUC为0.87(图5F)。
免疫微环境分析
单样本基因集富集分析(ssGSEA)由GSVA软件包(汉泽尔曼等人,2013年)进行,以评估免疫景观的差异。评估两组间免疫细胞(24种类型)的浸润、免疫相关通路的改变和HLA基因的表达。
首先,发明人利用ssGSEA计算24种免疫细胞丰度,并利用wilcox-test比较不同样本免疫浸润水平的差异,发现所有类型的免疫细胞在疾病样本的微环境中都显著富集(图5A),大多数NLRP3炎性小体相关基因的高表达与免疫细胞浸润的增加显著相关(图6B),GBP5表达与活化CD4+T细胞浸润的正相关最为显著(r=0.81,P<2.2e-16;图6C-6E),CARD8表达与CD56暗自然杀伤细胞浸润的负相关最为显著(r=-0.53,P<2.2e-16;图6F-6H)。
在疾病样本中,免疫相关通路普遍上调(图7A),大部分NLRP3炎症小体相关基因的上调与免疫相关通路活性的增加显著相关(图7B),在NLRP3炎症小体相关基因中,GBP5与抗原加工和呈递途径的激活相关性最为显著(r=0.62,P<2.2e-16;图7C-7E),CASP1与TGFb家族成员表达下调的相关性最为显著(r=-0.38,P=2.2e-12;图7F-7H)。
在疾病组中,大部分人类白细胞抗原(HLA)相关基因的表达均增加(图8A),大多数NLRP3炎症小体相关基因与HLA相关基因的表达量增加显著相关(图8B),CASP1与HLA-DMA的上调相关性最为显著(r=0.76,P<2.2e-16;图8C-8E)。相比之下,TXN与TGFb家族成员下调的相关性最为显著(r=-0.23,P=4e-05;图8F-8H)。
为了研究集群间免疫浸润的差异,发明人进行了ssGSEA分析,Cluster2的免疫细胞浸润水平普遍高于cluster 1(图9A),同样,与cluster 1相比,cluster2具有更高的HLA表达(图9B)和免疫相关通路(图9C)。
基于NLRP3炎性小体基因对克罗恩病样本进行分型
对克罗恩病样本进行疾病分型并对不同分子亚型间临床特征进行刻画,发明人使用了ConsensusClusterPlus包进行一致性聚类分析,聚类使用的距离是euclidean,聚类方法是km,并进行了100次重复,以保证分类的稳定性(Wilkerson and Hayes,2010),两个亚组之间的deg通过limma包(调整后的P<0.05,|log2FC|>1)(Ritchieetal.,2015)。
利用无监督的k-均值共识聚类,发明人基于14个NLRP3炎症小体相关基因确定了克罗恩病的两种亚型,当K=2时,得到了最优聚类(图10A-10B),主成分分析(PCA)显示了这两个聚类有很好的区别(图10C),与cluster 1相比,14个NLRP3炎症小体相关基因在cluster 2中普遍上调(图10D),两组患者的临床病理特征见附图1,组织位置、年龄、性别的分布均无显著性差异。
功能富集分析、基因集变异分析(GSVA)和药物-基因相互作用网络
对DEGs使用聚类分析器软件包进行了GO和KEGG途径富集分析(Yuetal.,2012),GSVA也通过GSVA软件包来阐明机器模型(汉泽尔曼等人,2013年)。使用字符串评分>为900的PPIs构建交互网络,并通过cytoscape进行可视化(Shannonetal.,2003),在药物-基因相互作用数据库中排除所有针对>10基因的非特异性药物后,药物-基因相互作用数据库(DGIdb)探索药物-基因相互作用网络(Griffithetal.,2013)。
通过通路富集分析和GSVA分析,阐明了这两个聚类的基因的功能,如图11A所示,GO通路分析显示,cluster 2中中性粒细胞调控、白细胞调控、细胞因子活性等免疫相关通路表达上调;KEGG通路分析显示,cluster2中TLR-、TNF-、IL-17-和趋化因子信号通路被激活(图11A),GSVA分析显示了cluster 1VS cluster 2间的77个显著差异的信号通路(图11B)。
差异表达基因的PPI网络见图12A,进一步选择9个NLRP3炎性小体相关基因(APP、CASP1、EIF2AK2、HSP90AB1、NFKB1、RELA、SUGT1、TLR4和TXN)作为hub基因,利用DGIdb数据库进一步筛选hub基因相互作用的药物,筛选出4个关键的NLRP3炎症小体相关基因(APP、HSP90AB1、NFKB和TLR4)作为克罗恩病治疗的潜在药物靶点(图12B),有两种药物针对APP,五种药物靶向HSP90AB1,一种药物靶向NFKB,一种药物靶向TLR4。
统计分析
采用R(版本4.1.0)和SPSS(版本25.0)软件进行统计分析。连续变量的正态性分析采用t检验或Wilcoxon秩和检验,分类变量的差异采用皮尔逊卡方检验,所有显著性阈值均设置为双侧P<值为0.05。
分析验证显示,NLRP3炎症小体相关基因的失调及其与免疫微环境、患者亚型、表型相关枢纽基因和克罗恩病潜在药物的关联,其中14个基因所形成的评估基因集(CARD8、CASP1、GBP5、HSP90AB1、MEFV、NFKB1、NFKB2、NLRC3、DHX33、PANX1、PSTPIP1、RELA、TXN和TLR6),可以用于评估克罗恩病进展的高风险表型或低风险表型,具有预后价值,可用于治疗反应预测。
虽然本发明以较佳实施例揭露如上,但并非用以限定本发明实施的范围。任何本领域的普通技术人员,在不脱离本发明的发明范围内,当可作些许的改进,即凡是依照本发明所做的同等改进,应为本发明的范围所涵盖。
Claims (6)
1.克罗恩病中NLRP3炎症小体相关基因的评估基因集,其用于评估克罗恩病进展的高风险表型或低风险表型,其特征在于,所述评估基因集包括14个基因,14个基因为CARD8、CASP1、GBP5、HSP90AB1、MEFV、NFKB1、NFKB2、NLRC3、DHX33、PANX1、PSTPIP1、RELA、TXN和TLR6。
2.用于评估克罗恩病进展的高风险表型或低风险表型的试剂盒,其特征在于,包括检测权利要求1所述评估基因集中14个基因表达水平的试剂。
3.权利要求2所述的试剂盒在制备克罗恩病诊断产品中的应用。
4.用于评估个体是否处于克罗恩病进展的高风险或低风险的系统,其特征在于,所述系统包括计算机,所述计算机被配置为使用计算机模型和机器学习模型,所述计算机模型基于从已知患有克罗恩病的个体获得基因表达数据,根据个体的多基因表达数据计算克罗恩病进展风险评分以确定该个体是否具有高风险表型或低风险表型,其中个体的多基因表达数据为权利要求1所述评估基因集中14个基因表达数据,14个基因及其对应的权重系数如下所示:
。
5.如权利要求4所述的系统,其特征在于,所述机器学习模型包括单因素logistic回归分析模型和Lasso回归模型。
6.如权利要求4所述的系统,其特征在于,所述机器学习模型还包括无监督的k均值共识聚类模型,其中相较于低风险表型,所述评估基因集中的14个基因在高风险表型中的表达水平上调。
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