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Abstract：
Behçet\'s disease (BD) is a multifaceted autoimmune disorder affecting multiple organ systems. Vascular complications, such as venous thromboembolism (VTE), are highly prevalent, affecting around 50% of individuals diagnosed with BD. This study aimed to identify potential biomarkers for VTE in BD patients. Three microarray datasets (GSE209567, GSE48000, GSE19151) were retrieved for analysis. Differentially expressed genes (DEGs) associated with VTE in BD were identified using the Limma package and weighted gene co-expression network analysis (WGCNA). Subsequently, potential diagnostic genes were explored through protein-protein interaction (PPI) network analysis and machine learning algorithms. A receiver operating characteristic (ROC) curve and a nomogram were constructed to evaluate the diagnostic performance for VTE in BD patients. Furthermore, immune cell infiltration analyses and single-sample gene set enrichment analysis (ssGSEA) were performed to investigate potential underlying mechanisms. Finally, the efficacy of listed drugs was assessed based on the identified signature genes. The limma package and WGCNA identified 117 DEGs related to VTE in BD. A PPI network analysis then selected 23 candidate hub genes. Four DEGs (E2F1, GATA3, HDAC5, and MSH2) were identified by intersecting gene sets from three machine learning algorithms. ROC analysis and nomogram construction demonstrated high diagnostic accuracy for these four genes (AUC: 0.816, 95% CI: 0.723-0.909). Immune cell infiltration analysis revealed a positive correlation between dysregulated immune cells and the four hub genes. ssGSEA provided insights into potential mechanisms underlying VTE development and progression in BD patients. Additionally, therapeutic agent screening identified potential drugs targeting the four hub genes. This study employed a systematic approach to identify four potential hub genes (E2F1, GATA3, HDAC5, and MSH2) and construct a nomogram for VTE diagnosis in BD. Immune cell infiltration analysis revealed dysregulation, suggesting potential macrophage involvement in VTE development. ssGSEA provided insights into potential mechanisms underlying BD-induced VTE, and potential therapeutic agents were identified.
摘要：
Behçet病（BD）是一种影响多器官系统的多方面自身免疫性疾病。血管并发症,如静脉血栓栓塞（VTE），非常普遍，影响约50%的被诊断患有BD的个体。本研究旨在确定BD患者VTE的潜在生物标志物。检索三个微阵列数据集(GSE209567、GSE48000、GSE19151)用于分析。使用Limma包和加权基因共表达网络分析（WGCNA）鉴定了BD中与VTE相关的差异表达基因（DEGs）。随后，通过蛋白质-蛋白质相互作用(PPI)网络分析和机器学习算法探索潜在的诊断基因.构建受试者工作特征（ROC）曲线和列线图以评估BD患者对VTE的诊断性能。此外，我们进行了免疫细胞浸润分析和单样本基因集富集分析(ssGSEA),以研究潜在的潜在机制.最后,所列药物的疗效是根据所鉴定的特征基因进行评估的.Limma包和WGCNA确定了与BD中VTE相关的117个DEG。然后通过PPI网络分析选择了23个候选集线器基因。通过使来自三种机器学习算法的基因集相交来鉴定四个DEGs(E2F1、GATA3、HDAC5和MSH2)。ROC分析和列线图构建显示了这四个基因的高诊断准确性(AUC:0.816,95%CI:0.723-0.909)。免疫细胞浸润分析显示，失调的免疫细胞与四个hub基因之间呈正相关。ssGSEA提供了对BD患者VTE发展和进展的潜在机制的见解。此外,治疗剂筛选确定了靶向四个hub基因的潜在药物。这项研究采用了系统的方法来鉴定四个潜在的hub基因（E2F1，GATA3，HDAC5和MSH2），并构建了用于BD中VTE诊断的列线图。免疫细胞浸润分析显示失调，提示潜在的巨噬细胞参与VTE的发展。ssGSEA提供了对BD诱导的VTE潜在机制的见解，并确定了潜在的治疗药物。
