SARS-CoV-2病毒感染潜在关键分子生物标志物及免疫浸润特征分析

doi:10.19586/j.2095-2341.2022.0067

生物技术进展 ›› 2022, Vol. 12 ›› Issue (5): 760-768.DOI: 10.19586/j.2095-2341.2022.0067

SARS-CoV-2病毒感染潜在关键分子生物标志物及免疫浸润特征分析

于敏¹(), 王敏², 魏延焕¹, 刘毅毅¹

^1.日照市人民医院急诊医学科，山东日照 276800
^2.海军军医大学第三附属医院检验科，上海 200438

收稿日期:2022-05-05 接受日期:2022-06-30 出版日期:2022-09-25 发布日期:2022-09-30
作者简介:于敏 E-mail：ym19912522707@163.com

Analysis of Potential Key Molecular Biomarkers and Immune Infiltration Characteristics of SARS-CoV-2 Virus Infection

Min YU¹(), Min WANG², Yanhuan WEI¹, Yiyi LIU¹

^1.Department of Emergency Medicine，Rizhao People's Hospital，Shandong Rizhao 276800，China
^2.Department of Laboratory Medicine，the Third Affiliated Hospital of Naval Military Medical University，Shanghai 200438，China

Received:2022-05-05 Accepted:2022-06-30 Online:2022-09-25 Published:2022-09-30

摘要/Abstract

摘要：

应用生物信息学方法筛选新型冠状病毒肺炎（corona virus disease 2019，COVID-19）感染的潜在关键分子生物标志物并分析其免疫浸润特征。从GEO数据库下载GSE152418数据集，其中COVID-19患者17例，健康对照17例。用加权基因共表达网络分析（weighted gene co-expression network analysis，WGCNA）方法筛选出COVID-19最相关的模块基因。与差异基因取交集得到共同基因，进行功能及信号通路富集分析，构建蛋白互作网络筛选关键基因，构建关键基因的miRNA-TF-mRNA调控网络，用CIBERSORT算法预测样本免疫细胞浸润特征。差异分析得到2 049个差异基因。WGCNA分析7个模块中“土耳其蓝色”模块与COVID-19相关性最高（r=0.91，P<0.001）。模块中基因显著性和模块隶属度呈显著正相关（r=0.96，P<0.001）。得到共同基因766个，主要参与有丝分裂、微管结合、阳离子通道活性及卵母细胞减数分裂、细胞衰老等。蛋白互作网络筛选到前10位关键基因分别为CDK1、BUB1、CCNA2、CDC20、KIF11、BUB1B、CDCA8、TOP2A、CCNB2、KIF20A，构建的miRNA-TF-mRNA网络包含51个miRNA、5个TF、10个mRNA。COVID-19患者较健康对照组幼稚B细胞、嗜酸性粒细胞浸润水平显著降低（P<0.05），浆细胞、活化肥大细胞浸润水平显著升高（P<0.05）。通过WGCNA及蛋白互作网络分析筛选出10个关键基因，并预测到调控关键基因的5个TF及51个miRNA，且COVID-19患者与健康对照的免疫浸润特征存在统计学差异，这些与免疫细胞相关的分子标志物可能作为COVID-19免疫治疗的潜在靶标。

关键词: 新型冠状病毒肺炎, 生物标志物, 免疫浸润, 生物信息学, 感染

Abstract:

To screen potential key molecular biomarkers of corona virus disease 2019 （COVID-19） infection and analyze their immune infiltration characteristics by using bioinformatics methods. The GSE152418 dataset was obtained from the GEO database， including 17 COVID-19 patients and 17 healthy controls. The module genes most related to COVID-19 were screened out by weighted gene co-expression network analysis （WGCNA）， then intersected with differentially expressed genes （DEGs） to obtain common genes （CGs）. Perform function and signal pathway enrichment analysis of CGs， construct protein interaction （PPI） network to screen key （Hub） genes， construct miRNA-transcription factor （TF）-mRNA regulatory network of Hub genes， and use CIBERSORT algorithm to predict immune cells in samples infiltration characteristics. Difference analysis yielded 2 049 DEGs. Among the seven modules analyzed by WGCNA， the "turquoise" module had the highest correlation with COVID-19 （r=0.91， P<0.001）. The gene significance in the module and the module membership were significantly positively correlated （r=0.96， P<0.001）. A total of 766 CGs were obtained， which were mainly involved in mitosis， microtubule binding， cation channel activity， oocyte meiosis， and cell senescence. The Top 10 Hub genes screened by PPI network were CDK1， BUB1， CCNA2， CDC20， KIF11， BUB1B， CDCA8， TOP2A， CCNB2， KIF20A. The constructed miRNA-TF-mRNA network included 51 miRNAs， 5 TFs， 10 mRNA. Compared with the healthy control group， the infiltration levels of naive B cells and eosinophils in COVID-19 patients were decreased （P<0.05）， and the infiltration levels of plasma cells and activated mast cells were increased （P<0.05）. In this study， 10 Hub genes were screened through WGCNA and PPI network analysis， and 5 TFs and 51 miRNAs were predicted to regulate Hub genes. There were significant differences in immune infiltration characteristics between COVID-19 patients and healthy controls. These immune cells relevant molecular markers may serve as potential targets for COVID-19 immunotherapy.

Key words: COVID-19, biomarkers, immune infiltration, bioinformatics, infection

中图分类号:

R373.1

于敏, 王敏, 魏延焕, 刘毅毅. SARS-CoV-2病毒感染潜在关键分子生物标志物及免疫浸润特征分析[J]. 生物技术进展, 2022, 12(5): 760-768.

Min YU, Min WANG, Yanhuan WEI, Yiyi LIU. Analysis of Potential Key Molecular Biomarkers and Immune Infiltration Characteristics of SARS-CoV-2 Virus Infection[J]. Current Biotechnology, 2022, 12(5): 760-768.

图/表 9

图1 差异基因的可视化图A：火山图；B：热图。红色为上调基因，绿色为下调基因。COVID-19：新型冠状病毒肺炎。

Fig. 1 Visualization of differential genes

图2 WGCNA分析及韦恩图A：最佳软阈值选择图；B：动态剪切树模块划分图；C：模块基因与临床特征相关性分析图；D：“土耳其蓝色”模块MM 和GS相关性分析图；E：韦恩图，重叠部分为CGs。COVID-19：新型冠状病毒肺炎；MM：模块隶属度；GS：基因显著性。

Fig. 2 WGCNA analysis and Venn diagram

表1 GO功能注释及富集分析

Table 1 GO functional annotation and enrichment analysis

分类	ID	描述	基因占比	P值	基因数
生物学过程	GO:0048285	细胞器裂变	61/632	6.88E-19	61
	GO:0000280	核分裂	60/632	1.57E-20	60
	GO:0140014	有丝分裂核分裂	49/632	4.19E-21	49
	GO:0007059	染色体分离	49/632	1.37E-17	49
	GO:0044772	有丝分裂细胞周期相变	48/632	1.97E-13	48
细胞组分	GO:0098687	染色体区域	45/666	1.36E-14	45
	GO:0062023	含胶原细胞外基质	42/666	6.75E-10	42
	GO:0000793	浓缩染色体	35/666	5.97E-13	35
	GO:0005819	主轴	35/666	2.12E-07	35
	GO:0000775	染色体，着丝粒区	34/666	5.88E-15	34
分子功能	GO:0046873	金属离子跨膜转运蛋白活性	28/651	0.001 538	28
	GO:0008017	微管结合	24/651	3.80E-05	24
	GO:0005261	阳离子通道活性	24/651	0.000 911	24
	GO:0005539	糖胺聚糖结合	20/651	0.000 210	20
	GO:0015291	二级主动跨膜转运蛋白活性	20/651	0.000 368	20

表2 KEGG信号通路富集分析

Table 2 KEGG signaling pathway enrichment analysis

ID	描述	基因占比	P值	基因数
hsa04110	细胞周期	25/308	5.45E-12	25
hsa04114	卵母细胞减数分裂	20/308	8.51E-08	20
hsa04510	粘着斑	20/308	6.93E-05	20
hsa05166	人类 T 细胞白血病病毒 Ⅰ型感染	18/308	0.001 840	18
hsa04512	ECM-受体相互作用	16/308	1.46E-07	16
hsa04914	孕酮介导的卵母细胞成熟	15/308	5.86E-06	15
hsa04261	心肌细胞中的肾上腺素信号传导	15/308	0.000 529	15
hsa04218	细胞衰老	15/308	0.000 800	15
hsa05410	肥厚型心肌病	13/308	3.00E-05	13
hsa05414	扩张型心肌病	13/308	6.02E-05	13

图3 CGs的PPI网络图

Fig. 3 PPI network diagram of CGs

图4 Hub基因的miRNA-TF-mRNA调控网络图注：菱形代表TF，圆形代表miRNA，三角形代表mRNA。

Fig. 4 miRNA-TF-mRNA regulatory network of Hub genes

图5 免疫细胞相对百分比图

Fig. 5 Relative percentage of immune cells

图6 两组样本间免疫细胞浸润水平比较图注：红色代表COVID-19样本，蓝色代表健康对照样本。

Fig. 6 Comparison of immune cell infiltration levels between the two groups of samples

图7 免疫浸润细胞亚型间相关性分析图注：红色为正相关，蓝色为负相关。

Fig. 7 Correlation analysis of immune infiltrating cell subtypes

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SARS-CoV-2病毒感染潜在关键分子生物标志物及免疫浸润特征分析

Analysis of Potential Key Molecular Biomarkers and Immune Infiltration Characteristics of SARS-CoV-2 Virus Infection

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