Yin Jianrong, Zhu Qiqiang, Yu Chunqiang, Wang Lianhuan, Ge Rongling, Wang Lei, Wang Jin
Department of Cardiology, Pizhou People's Hospital, Xuzhou Medical University Affiliated Pizhou Hospital, Pizhou, Jiangsu, China.
Medicine (Baltimore). 2025 Feb 14;104(7):e41430. doi: 10.1097/MD.0000000000041430.
The purpose of our study is to utilize bioinformatics methods to pinpoint genes linked to autophagy that may influence the progression of arrhythmogenic right ventricular cardiomyopathy (ARVC). By doing so, we hope to enhance the clinical intervention and handling of this cardiac condition by offering more informed guidance. The transcriptomic data corresponding to GSE29819 were accessed via the GEO repository. Utilizing R programming, we analyzed and searched genes associated with autophagy that might be relevant to ARVC. Subsequently, the identified genes underwent protein-protein interaction network and co-expression analysis, while GO and KEGG pathway enrichment analysis was employed to investigate the signaling cascades they may implicate. We intersected the down-regulated genes in GSE29819 with 222 autophagy-related genes, and finally got 12 differentially expressed autophagy-related genes. Examination of the Gene Ontology (GO) and the Kyoto Encyclopedia of Genes and Genomes (KEGG) indicates that diverse genetic activity plays a role across numerous biological functions and systems. These include cytokine-related genes, lipid metabolism and atherogenesis, nucleotide oligomerization domain-like receptor signaling, chemokine-induced pathway, autophagic genes, apoptosis, natural killer cells-induced cell death, signal transduction involving tumor necrosis factor, and the activation of C-type lectin receptors which may influence the diverse clinical presentations of ARVC. Cytoscape software constructed a protein mutual aid network of common differentially expressed genes, and obtained a Cluster with a high score and 7 key genes, including CCR2, FAS, PRKCD, CASP1, CCL2, NAMPT and TNFSF10. Utilizing bioinformatics methods to identify genes involved in autophagy that exhibit fluctuating expression levels augments our understanding of the intricate aspects of ARVC. At the same time, combined with previous research reports in cardiomyopathy, we can speculate that Fas may affect the occurrence and development of ARVC through tumor necrosis factor signaling pathway mediating apoptosis. These results further illuminate our understanding of the origins and potential treatment focal points for ARVC.
我们研究的目的是利用生物信息学方法来找出与自噬相关且可能影响致心律失常性右室心肌病（ARVC）进展的基因。通过这样做，我们希望通过提供更明智的指导来加强对这种心脏疾病的临床干预和处理。通过GEO数据库获取了与GSE29819对应的转录组数据。利用R编程，我们分析并搜索了可能与ARVC相关的自噬相关基因。随后，对鉴定出的基因进行了蛋白质-蛋白质相互作用网络和共表达分析，同时采用GO和KEGG通路富集分析来研究它们可能涉及的信号级联反应。我们将GSE29819中下调的基因与222个自噬相关基因进行交叉分析，最终得到12个差异表达的自噬相关基因。对基因本体论（GO）和京都基因与基因组百科全书（KEGG）的研究表明，多种基因活动在众多生物学功能和系统中发挥作用。这些包括细胞因子相关基因、脂质代谢和动脉粥样硬化形成、核苷酸寡聚化结构域样受体信号传导、趋化因子诱导途径、自噬基因、凋亡、自然杀伤细胞诱导的细胞死亡、涉及肿瘤坏死因子的信号转导以及可能影响ARVC不同临床表现的C型凝集素受体的激活。Cytoscape软件构建了常见差异表达基因的蛋白质互助网络，并获得了一个高分聚类和7个关键基因，包括CCR2、FAS、PRKCD、CASP1、CCL2、NAMPT和TNFSF10。利用生物信息学方法识别参与自噬且表达水平波动的基因，增强了我们对ARVC复杂方面的理解。同时，结合先前心肌病的研究报告，我们可以推测Fas可能通过肿瘤坏死因子信号通路介导凋亡来影响ARVC的发生和发展。这些结果进一步阐明了我们对ARVC起源和潜在治疗重点的理解。
