Objective To screening and validation of key genes for programmed cell death(PCD)in gout through bioinformatics and machine learning and immunoinfiltration analysis.Methods Gout-related datasets were obtained from the Gene Expression Omnibus(GEO)database,comprising the human gout dataset GSE160170 and murine gout dataset GSE190138,which served as the training cohort.Differentiall expression genes(DEGs)were screened with difference factor>2 and P<0.05.The DEGs of two data sets were intersected to obtain the common DEGs(co-DEGs).The co-DEGs were enriched by GO function and KEGG pathway analysis.The combination of co-DEGs and PCD related gene set was used to obtain PCD related DEGs.A PPI network was built in the STRING database.The key module genes were screened in Cytoscape's MCODE,the hub genes were screened in 12 algorithms built into the Cytohubba plugin,including Degree,MCC,DMNC,MCN,EPC,BottleNeck,EcCentricity,closness,Radiality,Betweeness,Stress and Clusteringcoefficoent,the common genes of the two was as candidate genes.The regression model of least absolute shrinkage and selection operator(LASSO)was used to screen key genes in the GSE160170 and GSE190138 data sets respectively,and the intersection of the two was adopted to obtain key genes.The diagnostic value of key genes in gout was evaluated by receiver operating characteristic(ROC)curve.The expression difference of gout related immune cells was investigated by single sample gene set enrichmemt analysis(ssGSEA)immunoinfiltration analysis.Finally,blood samples from 30 gout patients admitted to the Department of Rheumatology,Xi'an Fifth Hospital from February to April 2024 were collected as the experimental group,while blood samples from 30 healthy subjects were collected as the control group.RNA was extracted from the Peripheral blood mononuclear cell(PBMC).Quantitative real time polymerase chain reaction(RT-qPCR)was used to validate the expression of key genes in clinical samples.Results 53 common DEGs of GSE160170 and GSE190138 were obtained,among which 43 genes were up-regulated and 10 were down-regulated.GO and KEGG indicated that most genes were involved in cell death,apoptosis,interlenkin(IL)-17 signaling pathway,tumor necrosis facter(TNF)signaling pathway and nucleotide-binding oligomerization domain-(NOD)-like receptor signaling pathway.12 co-DEGs of programmed cell death and gout were obtained.A total of 7 candidate genes were screened.LASSO regression model screened 5 genes and 4 genes respectively in two datasets,and 3 key genes were abtained by the intersection of the two datasets,which were IL-6,plasminogen activator urokinase receptor(PLAUR)and NOD-like receptor thermal protein domain associated protein3(NLRP3).Validation within the training set revealed that all three genes attained perfect diagnostic performance for gout,with area under the ROC(AUC-ROC)curve values of 1.00.Immunoinfiltration analysis showed that the changes of activated CD4+T cells,activated CD8+T cells and natural killer cells were closely related to the occurrence and development of gout.In the clinical samples,compared with the control group,PLAUR,NLRP3 and IL-6 were highly expressed in gout patients,and the differences were statistically significant(t=18.852,9.633,8.293,all P＜0.05).Conclusion IL-6,PLAUR and NLRP3 provide potential biomarkers and therapeutic targets for the diagnosis and treatment of gout,offering new directions in this field.

Objective To screening and validation of key genes for programmed cell death(PCD)in gout through bioinformatics and machine learning and immunoinfiltration analysis.Methods Gout-related datasets were obtained from the Gene Expression Omnibus(GEO)database,comprising the human gout dataset GSE160170 and murine gout dataset GSE190138,which served as the training cohort.Differentiall expression genes(DEGs)were screened with difference factor>2 and P<0.05.The DEGs of two data sets were intersected to obtain the common DEGs(co-DEGs).The co-DEGs were enriched by GO function and KEGG pathway analysis.The combination of co-DEGs and PCD related gene set was used to obtain PCD related DEGs.A PPI network was built in the STRING database.The key module genes were screened in Cytoscape's MCODE,the hub genes were screened in 12 algorithms built into the Cytohubba plugin,including Degree,MCC,DMNC,MCN,EPC,BottleNeck,EcCentricity,closness,Radiality,Betweeness,Stress and Clusteringcoefficoent,the common genes of the two was as candidate genes.The regression model of least absolute shrinkage and selection operator(LASSO)was used to screen key genes in the GSE160170 and GSE190138 data sets respectively,and the intersection of the two was adopted to obtain key genes.The diagnostic value of key genes in gout was evaluated by receiver operating characteristic(ROC)curve.The expression difference of gout related immune cells was investigated by single sample gene set enrichmemt analysis(ssGSEA)immunoinfiltration analysis.Finally,blood samples from 30 gout patients admitted to the Department of Rheumatology,Xi'an Fifth Hospital from February to April 2024 were collected as the experimental group,while blood samples from 30 healthy subjects were collected as the control group.RNA was extracted from the Peripheral blood mononuclear cell(PBMC).Quantitative real time polymerase chain reaction(RT-qPCR)was used to validate the expression of key genes in clinical samples.Results 53 common DEGs of GSE160170 and GSE190138 were obtained,among which 43 genes were up-regulated and 10 were down-regulated.GO and KEGG indicated that most genes were involved in cell death,apoptosis,interlenkin(IL)-17 signaling pathway,tumor necrosis facter(TNF)signaling pathway and nucleotide-binding oligomerization domain-(NOD)-like receptor signaling pathway.12 co-DEGs of programmed cell death and gout were obtained.A total of 7 candidate genes were screened.LASSO regression model screened 5 genes and 4 genes respectively in two datasets,and 3 key genes were abtained by the intersection of the two datasets,which were IL-6,plasminogen activator urokinase receptor(PLAUR)and NOD-like receptor thermal protein domain associated protein3(NLRP3).Validation within the training set revealed that all three genes attained perfect diagnostic performance for gout,with area under the ROC(AUC-ROC)curve values of 1.00.Immunoinfiltration analysis showed that the changes of activated CD4+T cells,activated CD8+T cells and natural killer cells were closely related to the occurrence and development of gout.In the clinical samples,compared with the control group,PLAUR,NLRP3 and IL-6 were highly expressed in gout patients,and the differences were statistically significant(t=18.852,9.633,8.293,all P＜0.05).Conclusion IL-6,PLAUR and NLRP3 provide potential biomarkers and therapeutic targets for the diagnosis and treatment of gout,offering new directions in this field.

Objective To explore the influence of apoptosis-associated speck-like protein containing a caspase recruitment do-main(ASC)and Caspase-1 on the pathogenesis of primary biliary cirrhosis(PBC).Methods The real-time PCR,Western blot,py-rophosphate sequencing and ELISA were adopted to respectively detect the relative expressions of mRNA and protein of peripheral blood mononuclear cells(PBMS)Caspase-1 and ASC as well as the methylation status of ASC promoter region and plasma Caspase-1 and IL-18 expression levels in 30 cases of PBC and healthy controls.Results The mRNA and protein expressions of PBMC Caspase-1 and ASC in the PBC group were significantly higher than those in the control group(P<0.05).The methylation rate of ASC promoter Island1(ISI)was significantly lower than that of the control group(P<0.05),which of Island 2(IS2)was smaller than the background value and had no methylation occurrence.The levels of plasma Caspase-1 and IL-18 in the PBC group were sig-nificantly higher than those in the control group(P<0.05).The ASC mRNA in the PBC group was significantly correlated with the Caspase-1 mRNA expression(P<0.05);the methylation rates at loci 1,2,4,5 of ASC promoter region CpG island were nega-tively correlated with ASC mRNA expression(P< 0.05),and which at loci 3,6 had no correlation with their expressions(P>0.05);plasma Caspase-1 and IL-18 levels showed the obviously positive correlation.Conclusion ASC and Caspase-1 are involved in the immune inflammatory response in PBC.

Objective To explore the correlation between NF-κB signaling pathways activated by IL-18 in CD4+ T cells and the pathogenesis of PBC.Methods We detected the expression of IL-18 mRNA in PBMCs,IL-18 level in plasma,receptor IL-18R on surface of CD4+ T cell,proliferation rate of CD4+T cell and its NF-κB signaling pathway protein IκBα and NF-κB p65 by qRT-PCR,ELISA,flow cytometry,MACS and Western blot on 32 cases of patients with PBC (PBC group) and 32 healthy people (control group) in Guizhou provincial people′s hospital.Results The level of IL-18 in PBC group was significantly higher than that in control group (P<0.05).The relative expression of IL-18 mRNA in PBC group was significantly higher than that in control group (P<0.05).The percentage of CD4+T cells expressing IL-18Rα in PBC group was higher than that in control group (P<0.05).The proliferation rate of CD4+T cells stimulated by IL-18 in PBC group was significantly higher than that in healthy control group (P<0.01).The relative expression levels of NF-κB p65 protein were up-regulated in IL-18,and the expression of IκBα protein in each group was significantly increased,especially in PBC group (P<0.01).Conclusion IL-18 can activate NF-κB signal pathway in CD4+ T cells and participate in the pathogenesis of primary biliary cirrhosis.