背景:川崎病(KD)是一种急性血管炎,也就是说,儿童获得性心脏病的主要原因,约10%-20%的KD患者患有静脉免疫球蛋白(IVIG)耐药。尽管这种现象的潜在机制尚不清楚,最近的研究表明,免疫细胞浸润可能与其发生有关。方法:在本研究中,我们从基因表达综合数据库中的GSE48498和GSE16797数据集下载了表达谱,分析差异表达基因(DEGs),并将DEGs与从ImmPort数据库下载的免疫相关基因相交,以获得差异表达的免疫相关基因(DEIGs)。然后使用CIBERSORT算法计算免疫细胞组成,随后进行WGCNA分析以鉴定与免疫细胞浸润相关的模块基因。接下来,我们选择了所选择的模块基因和DEIG的交叉点,然后进行GO和KEGG富集分析。此外,ROC曲线验证,免疫细胞的Spearman分析,TF,和miRNA调控网络,并对最终获得的hub基因进行了潜在的药物预测。结果:CIBERSORT算法显示,与IVIG反应性患者相比,IVIG耐药患者的中性粒细胞表达明显更高。接下来,我们通过将DEIG与WGCNA获得的中性粒细胞相关模块基因相交,获得了差异表达的中性粒细胞相关基因,作进一步分析。富集分析显示这些基因与免疫通路有关,如细胞因子-细胞因子受体相互作用和中性粒细胞胞外陷阱形成。然后,我们将STRING数据库中的PPI网络与Cytoscape中的MCODE插件相结合,并鉴定了6个hub基因(TLR8,AQP9,CXCR1,FPR2,HCK,和IL1R2),根据ROC分析,对IVIG耐药具有良好的诊断性能。此外,Spearman相关分析证实这些基因与中性粒细胞密切相关。最后,TFs,miRNA,并预测了靶向hub基因的潜在药物,和TF-,miRNA-,并构建了药物基因网络。结论:本研究发现6个hub基因(TLR8、AQP9、CXCR1、FPR2、HCK、和IL1R2)与中性粒细胞浸润显着相关,在IVIG耐药中发挥了重要作用。一句话,这项工作为IVIG耐药患者提供了潜在的诊断生物标志物和前瞻性治疗靶点.
Background: Kawasaki disease (KD) is an acute vasculitis, that is, the leading cause of acquired heart disease in children, with approximately 10%-20% of patients with KD suffering intravenous immunoglobulin (IVIG) resistance. Although the underlying mechanism of this phenomenon remains unclear, recent studies have revealed that immune cell infiltration may associate with its occurrence. Methods: In this study, we downloaded the expression profiles from the GSE48498 and GSE16797 datasets in the Gene Expression Omnibus database, analyzed differentially expressed genes (DEGs), and intersected the DEGs with the immune-related genes downloaded from the ImmPort database to obtain differentially expressed immune-related genes (DEIGs). Then CIBERSORT algorithm was used to calculate the immune cell compositions, followed by the WGCNA analysis to identify the module genes associated with immune cell infiltration. Next, we took the intersection of the selected module genes and DEIGs, then performed GO and KEGG enrichment analysis. Moreover, ROC curve validation, Spearman analysis with immune cells, TF, and miRNA regulation network, and potential drug prediction were implemented for the finally obtained hub genes. Results: The CIBERSORT algorithm showed that neutrophil expression was significantly higher in IVIG-resistant patients compared to IVIG-responsive patients. Next, we got differentially expressed neutrophil-related genes by intersecting DEIGs with neutrophil-related module genes obtained by WGCNA, for further analysis. Enrichment analysis revealed that these genes were associated with immune pathways, such as cytokine-cytokine receptor interaction and neutrophil extracellular trap formation. Then we combined the PPI network in the STRING database with the MCODE plugin in Cytoscape and identified 6 hub genes (TLR8, AQP9, CXCR1, FPR2, HCK, and IL1R2), which had good diagnostic performance in IVIG resistance according to ROC analysis. Furthermore, Spearman\'s correlation analysis confirmed that these genes were closely related to neutrophils. Finally, TFs, miRNAs, and potential drugs targeting the hub genes were predicted, and TF-, miRNA-, and drug-gene networks were constructed. Conclusion: This study found that the 6 hub genes (TLR8, AQP9, CXCR1, FPR2, HCK, and IL1R2) were significantly associated with neutrophil cell infiltration, which played an important role in IVIG resistance. In a word, this work rendered potential diagnostic biomarkers and prospective therapeutic targets for IVIG-resistant patients.