Text mining-based in bioinformatics analysis of key genes and signal pathways related to immunoreaction in dry age-related macular degeneration

doi:10.3877/cma.j.issn.2095-2007.2022.05.002

Abstract

Abstract:

Objective

To screen the core genes and key pathways related to immunoreaction in dry age-related macular degeneration (AMD) using text mining and bioinformatics methods.

Methods

The gene datasets related to dry AMD and immune reactionwere retrieved through the text mining database pubmed2ensembl. The gene ontology (GO) function enrichment and Kyoto Encyclopedia of genes and genomes (KEGG) were carried out successively by using the GeneCodis tool signal pathway enrichment. The protein-protein interaction (PPI) network relationship was constructed by using the STRING database based on the Internet, and the hub genes and significant gene modules in the PPI network were screened by using the CytoHubba and mcode plug-ins of Cytoscape software. The function enrichment analysis of the screened hub genes and gene modules was carried out through the DAVID platform.

Results

Based on text mining and enrichment analysis, 47 genes related to dry AMD and 2410 genes related to immune response were obtained. Among of them, there were 31 genes related to both.GO functional enrichment and KEGG signal pathway enrichment analysis obtained 66 significantly enriched signal pathways, including 22 genes. The top 10 hub genes screened by Cytohub plug-in were interleukin (IL)10, C-X-C motif chemokine ligand 8 (CXCL8), tumor necrosis factor (TNF), IL18, IL6, tumor Protein P53 (TP53), vascular endothelial growth factor A (VEGFA), mitogen-activated protein kinase 8 (MAPK8), cyclin dependent kinase inhibitor 1A (CDKN1A) and AKT serine-threonine kinase 1 (AKT1). The significant gene module from PPI network screened by MCODE plug-in included IL10, CXCL8, TNF, IL18 and IL6 genes, which coincided with the hub gene. The GO analysis showed that significant gene modules were mainly involved in the inflammatory response, immune response, cell response to lipopolysaccharide and type 2 immune response in the biological processes. KEGG pathway analysis found that they were mainly involved in the interaction of cytokine-cytokine receptor, nucleotide-binding oligomerization domain (NOD)-like receptor signal pathway, inflammatory bowel disease, asthma, graft-versus-host disease and allograft rejection.

Conclusions

The one core gene modules and five key signal pathways related to the immunoreaction process of dry AMD are screened out through text mining and biological information analysis, which provides a potential target for the treatment of dry AMD.

Key words: Dry age-related macular degeneration, Immunoreaction, Core genes, Signaling pathway, Bioinformatics

Hang Wei, Mingwei Zhao, Jinfeng Qu. Text mining-based in bioinformatics analysis of key genes and signal pathways related to immunoreaction in dry age-related macular degeneration[J]. Chinese Journal of Ophthalmologic Medicine(Electronic Edition), 2022, 12(05): 262-267.

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Figures/Tables 4

References 30

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项目	参与过程(或通路)	富集基因数(个)	基因组总基因数(个)	矫正超几何P值	富集基因
生物过程	基因表达的负调控	13	305	1.93e^－19	SERPINF1、KDR、AKT1、ESR1、TNF、BMP4、APOE、TP53、CDKN1A、IL8、PPARG、ACE及VEGFA
	细胞因子相关信号通路	12	313	1.89e^－17	HMOX1、PTGS2、AKT1、TNF、IL18、TP53、CDKN1A、KIT、IL6、IL8、IL10及VEGFA
	基因表达的正调控	12	508	4.32e^－15	AKT1、TNF、BMP4、IL18、TP53、KIT、IL6、MAPK8、IL8、PPARG、CRP及VEGFA
	凋亡过程的负调控	11	526	3.51e^－13	PTGS2、KDR、AKT1、BMP4、TP53、CDKN1A、ALB、IL6、MAPK8、IL10及VEGFA
	平滑肌细胞增殖的正调控	7	63	5.25e^－13	HMOX1、PTGS2、AKT1、TNF、BMP4、IL18及IL6
	正调控DNA结合转录因子的活性	7	117	3.85e^－11	AKT1、ESR1、TNF、KIT、IL6、PPARG及IL10
	氧化应激反应	7	125	5.29e^－11	HMOX1、PTGS2、AKT1、APOE、TP53、MBL2及MAPK8
	MAPK级联正调控	7	164	3.18e^－10	KDR、TNF、BMP4、KIT、IL6、ADRA1D及VEGFA
	对有机物的反应	6	82	4.52e^－10	PTGS2、TIMP3、AKT1、TNF、CDKN1A及IL10
	凋亡过程的正调控	8	375	1.50e^－9	HMOX1、PTGS2、AKT1、TNF、BMP4、TP53、IL6及MAPK8
信号通路	癌症相关通路	13	369	9.01e^－21	HMOX1、PTGS2、AKT1、ESR1、BMP4、TP53、CDKN1A、KIT、IL6、MAPK8、IL8、PPARG及VEGFA
	流体剪切应力与动脉粥样硬化	8	97	2.68e^－15	HMOX1、KDR、AKT1、TNF、BMP4、TP53、MAPK8及VEGFA
	卡波西肉瘤相关疱疹病毒感染	8	134	2.55e^－14	PTGS2、AKT1、TP53、CDKN1A、IL6、MAPK8、IL8及VEGFA
	癌症相关蛋白聚糖	8	142	3.07e^－14	TIMP3、KDR、AKT1、ESR1、TNF、TP53、CDKN1A及VEGFA
	人类巨细胞病毒感染	8	160	4.65e^－14	PTGS2、AKT1、TNF、TP53、CDKN1A、IL6、IL8及VEGFA
	脂质和动脉粥样硬化	8	159	5.15e^－14	AKT1、TNF、IL18、TP53、IL6、MAPK8、IL8及PPARG
	恰加斯病	7	77	6.13e^－14	AKT1、TNF、IL6、MAPK8、IL8、ACE及IL10
	鼠疫感染	7	102	2.92e^－13	AKT1、TNF、IL18、IL6、MAPK8、IL8及IL10
	乙型肝炎	7	129	1.40e^－12	AKT1、TNF、TP53、CDKN1A、IL6、MAPK8及IL8
	非酒精性脂肪性肝病	6	65	3.43e^－12	AKT1、TNF、IL6、MAPK8、IL8及PPARG

项目	参与过程(或通路)	富集基因数(个)	基因组总基因数(个)	矫正超几何P值	富集基因
生物过程	基因表达的负调控	13	305	1.93e^－19	SERPINF1、KDR、AKT1、ESR1、TNF、BMP4、APOE、TP53、CDKN1A、IL8、PPARG、ACE及VEGFA
	细胞因子相关信号通路	12	313	1.89e^－17	HMOX1、PTGS2、AKT1、TNF、IL18、TP53、CDKN1A、KIT、IL6、IL8、IL10及VEGFA
	基因表达的正调控	12	508	4.32e^－15	AKT1、TNF、BMP4、IL18、TP53、KIT、IL6、MAPK8、IL8、PPARG、CRP及VEGFA
	凋亡过程的负调控	11	526	3.51e^－13	PTGS2、KDR、AKT1、BMP4、TP53、CDKN1A、ALB、IL6、MAPK8、IL10及VEGFA
	平滑肌细胞增殖的正调控	7	63	5.25e^－13	HMOX1、PTGS2、AKT1、TNF、BMP4、IL18及IL6
	正调控DNA结合转录因子的活性	7	117	3.85e^－11	AKT1、ESR1、TNF、KIT、IL6、PPARG及IL10
	氧化应激反应	7	125	5.29e^－11	HMOX1、PTGS2、AKT1、APOE、TP53、MBL2及MAPK8
	MAPK级联正调控	7	164	3.18e^－10	KDR、TNF、BMP4、KIT、IL6、ADRA1D及VEGFA
	对有机物的反应	6	82	4.52e^－10	PTGS2、TIMP3、AKT1、TNF、CDKN1A及IL10
	凋亡过程的正调控	8	375	1.50e^－9	HMOX1、PTGS2、AKT1、TNF、BMP4、TP53、IL6及MAPK8
信号通路	癌症相关通路	13	369	9.01e^－21	HMOX1、PTGS2、AKT1、ESR1、BMP4、TP53、CDKN1A、KIT、IL6、MAPK8、IL8、PPARG及VEGFA
	流体剪切应力与动脉粥样硬化	8	97	2.68e^－15	HMOX1、KDR、AKT1、TNF、BMP4、TP53、MAPK8及VEGFA
	卡波西肉瘤相关疱疹病毒感染	8	134	2.55e^－14	PTGS2、AKT1、TP53、CDKN1A、IL6、MAPK8、IL8及VEGFA
	癌症相关蛋白聚糖	8	142	3.07e^－14	TIMP3、KDR、AKT1、ESR1、TNF、TP53、CDKN1A及VEGFA
	人类巨细胞病毒感染	8	160	4.65e^－14	PTGS2、AKT1、TNF、TP53、CDKN1A、IL6、IL8及VEGFA
	脂质和动脉粥样硬化	8	159	5.15e^－14	AKT1、TNF、IL18、TP53、IL6、MAPK8、IL8及PPARG
	恰加斯病	7	77	6.13e^－14	AKT1、TNF、IL6、MAPK8、IL8、ACE及IL10
	鼠疫感染	7	102	2.92e^－13	AKT1、TNF、IL18、IL6、MAPK8、IL8及IL10
	乙型肝炎	7	129	1.40e^－12	AKT1、TNF、TP53、CDKN1A、IL6、MAPK8及IL8
	非酒精性脂肪性肝病	6	65	3.43e^－12	AKT1、TNF、IL6、MAPK8、IL8及PPARG

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