基于GEO数据库和临床样本验证CXCL9作为类风湿关节炎生物标志物的研究

doi:10.3969/j.issn.1673-8640.2023.12.003

摘要/Abstract

摘要：

目的基于生物信息学分析和临床样本验证，寻找类风湿关节炎（RA）新型生物标志物。方法从基因表达综合数据库（GEO）中获取RA和骨关节炎（OA）患者的转录组芯片数据（GSE12021数据集和GSE55235数据集），筛选差异表达基因。对筛选出的差异表达基因进行基因本体（GO）富集分析和京都基因与基因组数据库（KEGG）通路富集分析，构建蛋白互作（PPI）网络，并鉴别关键基因。选取2023年3—9月无锡市第九人民医院RA患者30例（RA组）、OA患者30例（OA组）和健康体检者30名（正常对照组），收集所有研究对象相关临床资料，检测外周血单个核细胞（PBMC）中关键基因的表达情况。采用受试者工作特征（ROC）曲线评价关键基因对RA的诊断效能。采用Spearman相关分析评估关键基因与RA临床症状评价指标[关节压痛计数（TJC）、关节肿胀计数（SJC）和基于红细胞沉降率（ESR）的疾病活动指数28（DAS28）评分（DAS28-ESR评分）]的相关性。结果共筛选出120个差异表达基因，其中表达上调56个、表达下调64个。GO富集和KEGG通路富集分析结果显示，筛选出的差异表达基因主要集中于免疫调节、白细胞增殖、适应性免疫、细胞因子和趋化因子介导信号通路等生物学进程。通过PPI网络鉴定出3个关键基因[C-X-C基序趋化因子配体（CXCL）9、CXCL11和鸟苷酸结合蛋白1（GBP1）]。RA组PMBC中CXCL9相对表达量显著高于OA组和正常对照组（P<0.01），RA组PMBC中CXCL11相对表达量显著高于正常对照组（P<0.05）；3组之间GBP1相对表达量差异无统计学意义（P>0.05）；ROC曲线分析结果显示，以正常对照组为对照，CXCL9相对表达量诊断RA的曲线下面积（AUC）为0.890；以OA组为对照，CXCL9相对表达量诊断RA的AUC为0.660。相关性分析结果显示，CXCL9相对表达量与TJC、SJC、DAS28-ESR评分均呈正相关（r值分别为0.604 6、0.752 6、0.789 6，P<0.001）。结论 RA患者PMBC中CXCL9表达显著升高，且与RA严重程度有关。CXCL9或可作为新的RA诊断生物标志物。

关键词: C-X-C基序趋化因子配体9, 类风湿关节炎, 生物信息学, 生物标志物, 临床样本

Abstract:

Objective To find the biomarkers of rheumatoid arthritis（RA） through bioinformatics analysis and experimental verification. Methods Transcriptome microarray data（GSE12021 and GSE55235）of RA and osteoarthritis（OA） were downloaded from Gene Expression Omnibus（GEO） database，and differentially expressed genes（DEG） were screened. Enrichment analysis was conducted using Kyoto Encyclopedia of Genes and Genomes（KEGG） and Gene Ontology（GO） pathways，and the protein-protein interaction（PPI） network was constructed to identify hub genes. Peripheral blood mononuclear cell（PBMC） samples of 30 patients with RA（RA group），30 patients with OA（OA group） and 30 healthy subjects（healthy control group） who were admitted to Wuxi Ninth People's Hospital from March 2023 to September 2023 were collected to verify the expression level of hub genes，and receiver operating characteristic（ROC） curve was constructed to evaluate the diagnostic efficiency of the hub genes. Spearman correlation analysis was used to evaluate the correlation between hub genes and clinical RA symptom evaluation indicators，including tender joint count（TJC），swollen joint count（SJC） and 28-joint disease activity score-erythrocyte sedimentation rate（DAS28-ESR） score. Results A total of 120 DEG were screened，of which 56 were up-regulated and 64 were down-regulated. The results of GO and KEGG pathway enrichment analysis indicated that the DEG mostly enriched in biological processes such as immune regulation，leukocyte proliferation，adaptive immunity，signal pathway mediated by cytokines and chemokines and so on. Three hub genes [C-X-C motif chemokine ligand（CXCL） 9，CXCL11 and guanylate binding protein 1（GBP1）] were identified by PPI network. The relative expression of CXCL9 in PMBC in RA group was higher than those in OA group and healthy control group（P<0.01），and the relative expression of CXCL11 in PMBC in RA group was higher than that in healthy control group（P<0.05）. There was no statistical significance in the relative expression of GBP1 among the 3 groups（P>0.05）. ROC curve analysis showed that the areas under curves（AUC） of relative expression of CXCL9 in diagnosing RA were 0.890 and 0.660，respectively，compared with healthy control group and OA group. Correlation analysis showed that the relative expression of CXCL9 was positively correlated with TJC，SJC and DAS28-ESR score（r values were 0.604 6，0.752 6 and 0.789 6，respectively，P<0.001）. Conclusions The expression of CXCL9 in PMBC of RA patients is increased，which was related to the severity of RA. CXCL9 may be a potential diagnostic marker of RA.

Key words: C-X-C motif chemokine ligand 9, Rheumatoid arthritis, Bioinformatics, Diagnostic marker, Clinical specimen

中图分类号:

R446.1

刘庆阳, 袁建明, 夏进军, 姜风英, 王秋波, 王晓明. 基于GEO数据库和临床样本验证CXCL9作为类风湿关节炎生物标志物的研究[J]. 检验医学, 2023, 38(12): 1121-1129.

LIU Qingyang, YUAN Jianming, XIA Jinjun, JIANG Fengying, WANG Qiubo, WANG Xiaoming. CXCL9 as a potential diagnostic marker of rheumatoid arthritis based on GEO database and experimental verification[J]. Laboratory Medicine, 2023, 38(12): 1121-1129.

图/表 10

参考文献 25

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数据集	检测平台	例数		平均年龄		性别
		RA/例	OA/例	RA/岁	OA/岁	RA		OA
		RA/例	OA/例	RA/岁	OA/岁	男/例	女/例	男/例	女/例
GSE12021	GPL96	12	10	64.8	71.9	3	9	2	8
GSE55235	GPL96	10	10

数据集	检测平台	例数		平均年龄		性别
		RA/例	OA/例	RA/岁	OA/岁	RA		OA
		RA/例	OA/例	RA/岁	OA/岁	男/例	女/例	男/例	女/例
GSE12021	GPL96	12	10	64.8	71.9	3	9	2	8
GSE55235	GPL96	10	10

基因名称	正向引物序列（5'~3'）	反向引物序列（5'~3'）
CXCL9	GTGGAAGCATGATTGGTGCC	TTCTGGCCACAGACAACCTC
CXCL11	GAGTGTGAAGGGCATGGCTA	AGGGCCTATGCAAAGACAGC
GBP1	GACAGGGTCCAGTTGCTGAA	GTGACAGGAAGGCTCTGGTC
GAPDH	GTCAAGGCTGAGAACGGGAA	AAATGAGCCCCAGCCTTCTC

基因名称	正向引物序列（5'~3'）	反向引物序列（5'~3'）
CXCL9	GTGGAAGCATGATTGGTGCC	TTCTGGCCACAGACAACCTC
CXCL11	GAGTGTGAAGGGCATGGCTA	AGGGCCTATGCAAAGACAGC
GBP1	GACAGGGTCCAGTTGCTGAA	GTGACAGGAAGGCTCTGGTC
GAPDH	GTCAAGGCTGAGAACGGGAA	AAATGAGCCCCAGCCTTCTC

富集名称	编码	P值	q值
生物学进程
免疫效应过程调节	GO：0002697	0.000 01	0.000 01
白细胞增殖	GO：0070661	0.000 06	0.000 05
基于免疫球蛋白超家族结构域介导的适应性免疫应答	GO：0002460	0.000 30	0.000 24
脂多糖反应	GO：0032496	0.000 51	0.000 41
趋化因子介导的信号传导	GO：0070098	0.000 52	0.000 42
细菌来源分子反应调节	GO：0002237	0.000 56	0.000 45
趋化因子反应调节	GO：1990868	0.000 56	0.000 45
胞内趋化因子反应调节	GO：1990869	0.000 56	0.000 45
白细胞黏附	GO：0007159	0.000 56	0.000 45
自身免疫调节	GO：0045088	0.000 63	0.000 50
细胞组分
质膜外侧	GO：0009897	0.000 60	0.000 55
含胶原的细胞外基质	GO：0062023	0.000 60	0.000 55
网格蛋白包被囊泡膜	GO：0030665	0.003 76	0.003 45
高密度脂蛋白颗粒	GO：0034364	0.022 09	0.020 25
包被囊泡膜	GO：0030662	0.024 29	0.022 27
网格蛋白包被囊泡	GO：0030136	0.025 96	0.023 80
血浆脂蛋白颗粒	GO：0034358	0.025 96	0.023 80
脂蛋白颗粒	GO：1990777	0.025 96	0.023 80
蛋白质-脂质复合物	GO：0032994	0.029 17	0.026 74
血液微粒	GO：0072562	0.030 08	0.027 57
分子功能
糖胺聚糖结合	GO：0005539	0.000 00	0.000 00
肝素结合	GO：0008201	0.000 00	0.000 00
含硫化合物结合	GO：1901681	0.000 00	0.000 00
免疫受体活性	GO：0140375	0.000 00	0.000 00
细胞因子受体活性	GO：0004896	0.000 01	0.000 01
趋化因子受体结合	GO：0042379	0.000 01	0.000 01
趋化因子活性	GO：0008009	0.000 02	0.000 02
细胞因子活性	GO：0005125	0.000 07	0.000 06
CXCR趋化因子受体结合	GO：0045236	0.000 13	0.000 11
生长因子结合	GO：0019838	0.000 62	0.000 52
信号通路
细胞因子-细胞因子受体相互作用	hsa04060	0.000 02	0.000 02
趋化因子信号通路	hsa04062	0.009 59	0.008 51
JAK-STAT信号通路	hsa04630	0.011 31	0.010 04
Toll样受体信号通路	hsa04620	0.044 82	0.039 78
免疫缺陷	hsa05340	0.067 06	0.059 52
破骨细胞分化	hsa04380	0.080 19	0.071 17
造血细胞谱系	hsa04640	0.099 02	0.087 88
Wnt信号通路	hsa04310	0.119 85	0.106 37
胞质DNA感应途径	hsa04623	0.130 93	0.116 20
花生四烯酸代谢	hsa00590	0.130 93	0.116 20