| Objective:Eosinophilic granulomatosis with polyangiitis(EGPA)is a multisystemic disorder,the prognosis is generally good by the use of corticosteroids and immunosuppressants.Disease activity is often in EGPA during clinical course,which increases the number of clinical visits,affects the quality of life,increases the economic burden,and the mortality of patients in active period is high,and the prognosis is poor.Until now,there is no standardized disease-specific activity assessment tool for EGPA.We used RNA sequencing and proteomic analysis to screen the differential genes and proteins related to the activity of EGPA.And screen disease-specific activity biomarkers.This study would provide new insight into the mechanism of active EGPA and novel treatment.Methods:1.Analysis of clinical characteristics of active EGPA: Compared the differences of demographic characteristics,main clinical manifestations,inflammatory indicators in blood,humoral immunity,tumor markers,airway inflammation,lung function,and CT imaging between active and inactive EGPA patients,the differential indicator was analyzed by logistic regression.2.Transcriptomic characteristics analysis of active EGPA: Peripheral blood of patients in active and inactive phase was collected,and white blood cells were isolated.We performed RNA sequencing to compare the genes' different transcriptomic expressions between the two groups.KEGG and GO analysis identify the main signal pathways and related biological processes involved in the differential expression genes.3.Analysis of cytokine expression in active EGPA: peripheral blood of patients in the active phase and inactive phase were collected,serum was separated to detect the expression of 160 kinds of cytokines in serum.Bioinformatics analysis to screen the main signal pathways and biological functions of differential expression proteins(DEPs),ROC was used to analyze the diagnostic efficacy of DEPs,correlation analysis to analyze the correlation with clinical indicators.Bio-plex and ELISA to confirm the DEPs,correlation analysis between confirmed DEPs and concentration after treatment were analyzed.4.Correlation analysis of transcriptomics and proteomics and study on the mechanism of differential biomarkers: analyzed the potential biomarkers or early identification of disease activity with correlation analysis,immunohistochemistry,immunefluorescence staining,and Western blot to study the expression of potential biomarkers in tissue to explore the possible mechanism of biomarkers in the active disease.Results:1.Compared with inactive EGPA,the percentage of eosinophils,eosinophil counts,neutrophil counts,ESR,CRP,immunoglobulin G,CH50,? 2-microglobulin,ceruloplasmin,neuron-specific enolase,CA125,and total Ig E were increased in active EGPA(P < 0.05).The number of hemoglobin and complement C3 in inactive EGPA was increased than that in active EGPA(P < 0.05).The proportion of eosinophils in active EGPA sputum was higher than that in inactive EGPA(P < 0.05),In contrast,the percentage of neutrophils and macrophages in the inactive EGPA was higher than that in active EGPA(P < 0.05).Fe NO of active patients was higher than that in inactive EGPA(P < 0.05).The pulmonary function of patients in inactive EGPA was higher than that of active patients(P < 0.05),but FEV1/FVC% and FEV1/FVC% pre were not significantly different(P > 0.05).No significant abnormality was showed in the other clinical indicators between the two groups(P > 0.05).Logistic regression to analyze the difference indicators,after adjusting other confounding indicators,the risk of patients with increased eosinophil proportion,increased eosinophil count and decreased FVC% pre was5.7*107?3.0 and 1.4*10-757048550 times higher than that of other patients,but there was no statistical significance(P = 0.995)2.We discovered 36 up-regulated genes and 19 down-regulated genes with transcriptomics analysis.KEGG analysis showed the main signal pathway involved in active EGPA is asthma.GO KEGG analysis showed the biological process involved in active EGPA are an immune response,inflammatory response,chemotaxis,and migration of immune cells,immune defense,secretion of cytokines,et.al.3.19 DEPs were obtained by cytokine detection and analysis,and their main signal pathways included MAPK,PI3 K Akt,RAS,cytokine receptor related effects and Rap1 related pathways.Further verification showed that the serum levels of Axl,OPN,HCC-4,GDNF and MCP-3 were increased in patients with active EGPA(P < 0.05),and the diagnostic efficiency was Axl(AUC = 0.94),OPN(AUC = 0.83),HCC-4(AUC = 0.81),GDNF(AUC = 0.81)and MCP-3(AUC = 0.87),respectively.The serum levels of Axl(r = 0.744,P < 0.001),OPN(r = 0.503,P = 0.006),HCC-4(r =0.473,P = 0.011)and MCP-3(r =-0.616,P < 0.001)were correlated with disease activity,but GDNF was not correlated with disease activity(P > 0.05).4.Correlation analysis of omics showed that Axl and SCF were potential biomarkers for identifying active EGPA.Serum concentration was correlated with disease activity(r=0.744,P< 0.001)and SCF(r=0.451,P=0.016),and concentration was decreased remission(P < 0.05).ROC analysis showed the diagnostic efficiency was Axl(AUC = 0.94)and SCF(AUC = 0.91),respectively.Immunohistochemical,immunofluorescence staining,and Western blot analysis revealed strong expression of SCF/C-kit in the active lung tissue and bronchial mucosa(P < 0.05),and had no significant differential expression of Gas6/Ckit in active and inactive tissue(P >0.05).Conclusions:The active EGPA often presents as eosinophilic inflammation.Logistic regression analysis showed that there were no specific clinical indicators to identify the active EGPA.Transcriptome and proteomic correlation analysis showed that Axl and SCF were associated with disease activity,which had higher diagnostic efficiency.The expression of GAS6/Axl in lung tissue and bronchial mucosa had no significant difference,and SCF /C-kit was increased in the tissue,suggesting that there were differences in the biological target organs of Axl and SCF. |
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