基于综合生物信息学分析鉴定致心律失常性右室心肌病相关的核心基因

Identification of Hub Genes Associated with Arrhythmogenic Right Ventricular Cardiomyopathy through Integrated Bioinformatics Analysis

摘要: 目的：利用综合生物信息学分析鉴定致心律失常性右室心肌病(ARVC)相关的核心基因并探讨其潜在机制，以期为ARVC患者的靶向治疗提供重要依据。方法：利用R语言鉴定出数据集GSE29819差异表达基因(DEGs)。采用加权基因相关网络分析(WGCNA)构建共表达网络，识别与ARVC相关的重要相互作用基因，与DEGs取交集，得到ARVC相关核心基因。对核心基因进行免疫浸润分析、功能富集分析和蛋白质互作网络分析识别中枢基因，并探索中枢基因的诊断价值。结果：差异分析共识别出329个DEGs，WGCNA共识别出18个共表达模块，其中绿松石模块(5597个基因，r = −0.95，P = 2e−12)与ARVC相关性最强，表明该模块中的基因可能与疾病状态相关。DEGs和绿松石模块基因取交集共得到254个核心基因。免疫浸润分析显示核心基因在促进ARVC的发生发展中与单核细胞、M1型巨噬细胞、休眠树突状细胞密切相关(P < 0.05)。功能富集分析提示这些核心基因的生物学过程主要集中在趋化性、白细胞迁移、细胞因子活性、肿瘤坏死因子信号通路、脂代谢和粥样硬化、核因子κB信号通路和趋化因子信号通路。蛋白质互作网络分析共识别出9个枢纽基因(IL6、PTGS2、CCR1、CXCL1、IL1B、CCL11、CCL2、CXCR4、CCL19)，均有一定诊断潜力，其中CCR1、CCL19和PTGS2基因表现出强大的诊断能力(AUC = 1)，对于区分ARVC患者与健康人群具有最佳诊断价值。结论：本研究通过综合生物信息学分析筛选出ARVC相关核心基因，揭示其与免疫浸润及多条信号通路的关联。这些核心基因有望作为ARVC诊断与预后评估的新型生物标志物及潜在治疗靶点。

Abstract: Objective: This study aims to identify core genes associated with arrhythmogenic right ventricular cardiomyopathy (ARVC) and investigate their underlying mechanisms using comprehensive bioinformatics approaches, thereby providing a crucial basis for targeted therapy in ARVC patients. Methods: Differentially expressed genes (DEGs) were identified from the dataset GSE29819 using R software. A weighted gene co-expression network analysis (WGCNA) was employed to construct co-expression networks and identify key genes interacting with ARVC. The intersection of DEGs and key module genes yielded ARVC-associated core genes. Subsequently, immune infiltration analysis, functional enrichment analysis, and protein-protein interaction (PPI) network analysis were conducted on these core genes to pinpoint hub genes and explore their diagnostic value. Results: Differential expression analysis identified 329 DEGs. WGCNA revealed 18 co-expression modules, among which the turquoise module (comprising 5597 genes, r = −0.95, P = 2e−12) demonstrated the strongest correlation with ARVC, indicating its potential relevance to the disease state. The intersection of DEGs and turquoise module genes resulted in 254 core genes. Immune infiltration analysis demonstrated that these core genes were significantly associated with monocytes, M1 macrophages, and resting dendritic cells in promoting ARVC pathogenesis (P < 0.05). Functional enrichment analysis revealed that the biological processes of these core genes were primarily concentrated in chemotaxis, leukocyte migration, cytokine activity, TNF signaling pathway, lipid and atherosclerosis metabolism, NF-kappa B signaling pathway, and chemokine signaling pathway. PPI network analysis identified 9 hub genes (IL6, PTGS2, CCR1, CXCL1, IL1B, CCL11, CCL2, CXCR4, CCL19), all exhibiting diagnostic potential. Notably, CCR1, CCL19, and PTGS2 demonstrated powerful diagnostic capability (AUC = 1), indicating optimal value for distinguishing ARVC patients from healthy controls. Conclusion: This study, through integrated bioinformatics analysis, screened out ARVC-related core genes and revealed their associations with immune infiltration and multiple signaling pathways. These core genes, particularly the highlighted hub genes, show promise as novel biomarkers for ARVC diagnosis and prognosis assessment, as well as potential therapeutic targets.