基于生物信息学整合鉴定与支气管哮喘相关的潜在诊断生物标志物

doi:10.3877/cma.j.issn.1674-6902.2023.03.006

目的

为寻找支气管哮喘患者潜在的诊断生物标志物，基于生物信息学分析方法。

方法

从基因表达芯片(GEO)数据库获得以哮喘患者和健康志愿者为试验对象的GSE41861和GSE64913支气管上皮细胞基因表达数据集。通过对高通量芯片数据进行提取，结合GEO2R、omics Bean、STRING等网站或软件筛选差异表达基因，将二者的共同差异基因排列成蛋白质-蛋白质互相作用(PPI)网络，从而找到关键基因。在DAVID网站中进行基因本体论(GO)分析和京都基因与基因组百科全书(KEGG)通路富集分析，以探索这些基因的功能作用。最后应用ROC曲线分析这些基因对支气管哮喘的诊断价值。

结果

GSE41861和GSE64913数据集中共筛选出11个关键差异基因。GO富集分析显示，差异基因主要与内肽酶、肽酶的活性及调节相关，KEGG结果显示上述差异基因涉及到的信号通路为补体及凝血级联反应。根据ROC曲线显示CEACAM5、GRP、SCGB3A1、KCNA1诊断哮喘的准确度较高(AUC值>0.8)。

结论

哮喘的生物信息学分析结果为探索哮喘的潜在发病机制和关键基因提供证据。其中CEACAM5、GRP、SCGB3A1、KCNA1与哮喘的临床诊断具有意义。

关键词: 支气管哮喘, 生物信息学, 差异基因, GEO数据库

Objective

To find potential diagnostic biomarkers for patients with bronchial asthma.

Methods

this study used bioinformatics analysis to obtain the GSE41861 and GSE64913 bronchial epithelial cell gene expression datasets from the Gene Expression Microarray (GEO) database with bronchial asthma patients and healthy volunteers. The high-throughput microarray data were extracted and differentially expressed genes were screened by websites or software such as GEO2R, omics Bean and STRING. The common differential genes between the two were aligned into a protein-protein interaction (PPI) network to find the key genes. Gene ontology (GO) analysis and Kyoto Encyclopedia of Genomes and Genomes (KEGG) pathway enrichment analysis were also performed in the DAVID website to explore the functional roles of these genes. Finally, ROC curves were applied to analyze the diagnostic value of these genes for bronchial asthma.

Results

The results showed that 11 common differential genes were screened in the GSE41861 and GSE64913 datasets. GO enrichment analysis showed that the differential genes were mainly associated with the activity and regulation of endopeptidase as well as peptidase. KEGG results showed that the above differential genes were involved in the signaling pathways of complement and coagulation cascade. The ROC curves showed that CEACAM5, GRP, SCGB3A1, and KCNA1 were highly accurate in the diagnosis of asthma (AUC values>0.8).

Conclusion

the results of bioinformatics analysis of asthma can provide supporting evidence to explore the potential pathogenesis and key genes of asthma. Among them, CEACAM5, GRP, SCGB3A1, and KCNA1 were correlated with the clinical diagnosis of asthma.

Key words: Bronchial asthma, Bioinformatics, Differential genes, GEO database

图1 共同差异基因GO富集分析

表1 共同差异基因的GO富集分析结果

ONTOLOGY	ID	Description	GeneRatio	BgRatio	pvalue	p.adjust	qvalue
BP	GO：0045745	positive regulation of G protein-coupled receptor signaling pathway	2/11	32/18 670	1.55e-04	0.039	0.023
BP	GO：0009612	response to mechanical stimulus	3/11	210/18 670	2.17e-04	0.039	0.023
BP	GO：0010951	negative regulation of endopeptidase activity	3/11	250/18 670	3.62e-04	0.039	0.023
BP	GO：0010466	negative regulation of peptidase activity	3/11	262/18 670	4.15e-04	0.039	0.023
BP	GO：0010830	regulation of myotube differentiation	2/11	62/18 670	5.85e-04	0.044	0.026
CC	GO：0044224	juxtaparanode region of axon	1/11	10/19 717	0.006	0.073	0.049
CC	GO：0042589	zymogen granule membrane	1/11	11/19 717	0.006	0.073	0.049
CC	GO：0033270	paranode region of axon	1/11	12/19 717	0.007	0.073	0.049
CC	GO：0042588	zymogen granule	1/11	14/19 717	0.008	0.073	0.049
CC	GO：0016324	apical plasma membrane	2/11	318/19 717	0.013	0.073	0.049
MF	GO：0004866	endopeptidase inhibitor activity	3/11	175/17 697	1.48e-04	0.003	0.002
MF	GO：0030414	peptidase inhibitor activity	3/11	182/17 697	1.66e-04	0.003	0.002
MF	GO：0061135	endopeptidase regulator activity	3/11	182/17 697	1.66e-04	0.003	0.002
MF	GO：0061134	peptidase regulator activity	3/11	219/17 697	2.87e-04	0.003	0.002
MF	GO：0004857	enzyme inhibitor activity	3/11	375/17 697	0.001	0.013	0.008

表1 共同差异基因的GO富集分析结果

ONTOLOGY	ID	Description	GeneRatio	BgRatio	pvalue	p.adjust	qvalue
BP	GO：0045745	positive regulation of G protein-coupled receptor signaling pathway	2/11	32/18 670	1.55e-04	0.039	0.023
BP	GO：0009612	response to mechanical stimulus	3/11	210/18 670	2.17e-04	0.039	0.023
BP	GO：0010951	negative regulation of endopeptidase activity	3/11	250/18 670	3.62e-04	0.039	0.023
BP	GO：0010466	negative regulation of peptidase activity	3/11	262/18 670	4.15e-04	0.039	0.023
BP	GO：0010830	regulation of myotube differentiation	2/11	62/18 670	5.85e-04	0.044	0.026
CC	GO：0044224	juxtaparanode region of axon	1/11	10/19 717	0.006	0.073	0.049
CC	GO：0042589	zymogen granule membrane	1/11	11/19 717	0.006	0.073	0.049
CC	GO：0033270	paranode region of axon	1/11	12/19 717	0.007	0.073	0.049
CC	GO：0042588	zymogen granule	1/11	14/19 717	0.008	0.073	0.049
CC	GO：0016324	apical plasma membrane	2/11	318/19 717	0.013	0.073	0.049
MF	GO：0004866	endopeptidase inhibitor activity	3/11	175/17 697	1.48e-04	0.003	0.002
MF	GO：0030414	peptidase inhibitor activity	3/11	182/17 697	1.66e-04	0.003	0.002
MF	GO：0061135	endopeptidase regulator activity	3/11	182/17 697	1.66e-04	0.003	0.002
MF	GO：0061134	peptidase regulator activity	3/11	219/17 697	2.87e-04	0.003	0.002
MF	GO：0004857	enzyme inhibitor activity	3/11	375/17 697	0.001	0.013	0.008

表2 共同差异基因的KEGG富集分析结果

ONTOLOGY	ID	Description	GeneRatio	BgRatio	Pvalue	p.adjust	qvalue
KEGG	hsa04610	Complement and coagulation cascades	2/6	85/8 076	0.002	0.035	0.030

表2 共同差异基因的KEGG富集分析结果

ONTOLOGY	ID	Description	GeneRatio	BgRatio	Pvalue	p.adjust	qvalue
KEGG	hsa04610	Complement and coagulation cascades	2/6	85/8 076	0.002	0.035	0.030

图2 ROC曲线

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Identification of biomarkers associated with diagnosis of bronchial asthma based on integrated bioinformatics analysis

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Identification of biomarkers associated with diagnosis of bronchial asthma based on integrated bioinformatics analysis