背景:皮肌炎(DM)表现为自身免疫性和炎症性疾病,临床特征为亚急性进行性近端肌无力,皮疹或两者兼有肌外表现。文献表明DM与动脉粥样硬化(AS)有共同的危险因素,它们经常同时发生,然而,病因和发病机制仍有待充分阐明。这项研究旨在利用生物信息学方法来阐明影响DM和AS病理生理学的关键基因和途径。
方法:从基因表达综合(GEO)数据库检索DM(GSE128470,GSE1551,GSE143323)和AS(GSE100927,GSE28829,GSE43292)的微阵列数据集。加权基因共表达网络分析(WGCNA)用于揭示它们的共表达模块。使用R软件中的“limma”软件包鉴定差异表达基因(DEGs),并通过功能富集分析确定常见DEGs的功能。使用STRING数据库建立了蛋白质-蛋白质相互作用(PPI)网络,具有由cytoHubba插件评估的中心基因,并通过外部数据集进行验证。使用CIBERSORT方法对hub基因进行免疫浸润分析,以及基因集富集分析(GSEA)。最后,NetworkAnalyst平台用于检查负责调节关键串扰基因的转录因子(TFs)。
结果:利用WGCNA分析,共定位了271个重叠基因.随后的DEG分析揭示了DM和AS中常见的34个基因,包括31个上调基因和3个下调基因。度中心性算法分别应用于WGCNA和DEG集合,以选择具有最高连通性的15个基因,杂交两个基因集产生了3个中心基因(PTPRC,TYROBP,CXCR4)。外部数据集的验证显示了它们对DM和AS的诊断价值。免疫浸润分析表明,淋巴细胞和巨噬细胞与DM和AS的发病机制显着相关。此外,GSEA分析表明,共享基因富含各种受体相互作用以及多种细胞因子和受体信号通路。我们将3个hub基因与它们各自的预测基因耦合,识别潜在的密钥TF,CBFB,与所有3个hub基因相互作用。
结论:本研究利用综合生物信息学技术探索DM和AS的共同发病机制。三个关键基因,包括PTPRC,TYROBP,CXCR4与DM和AS的发病机制有关。中心基因及其与免疫细胞的相关性可以作为潜在的诊断和治疗靶标。
BACKGROUND: Dermatomyositis (DM) manifests as an autoimmune and inflammatory condition, clinically characterized by subacute progressive proximal muscle weakness, rashes or both along with extramuscular manifestations. Literature indicates that DM shares common risk factors with atherosclerosis (AS), and they often co-occur, yet the etiology and pathogenesis remain to be fully elucidated. This investigation aims to utilize bioinformatics methods to clarify the crucial genes and pathways that influence the pathophysiology of both DM and AS.
METHODS: Microarray datasets for DM (GSE128470, GSE1551, GSE143323) and AS (GSE100927, GSE28829, GSE43292) were retrieved from the Gene Expression Omnibus (GEO) database. The weighted gene co-expression network analysis (WGCNA) was used to reveal their co-expressed modules. Differentially expression genes (DEGs) were identified using the \"limma\" package in R software, and the functions of common DEGs were determined by functional enrichment analysis. A protein-protein interaction (PPI) network was established using the STRING database, with central genes evaluated by the cytoHubba plugin, and validated through external datasets. Immune infiltration analysis of the hub genes was conducted using the CIBERSORT method, along with Gene Set Enrichment Analysis (GSEA). Finally, the NetworkAnalyst platform was employed to examine the transcription factors (TFs) responsible for regulating pivotal crosstalk genes.
RESULTS: Utilizing WGCNA analysis, a total of 271 overlapping genes were pinpointed. Subsequent DEG analysis revealed 34 genes that are commonly found in both DM and AS, including 31 upregulated genes and 3 downregulated genes. The Degree Centrality algorithm was applied separately to the WGCNA and DEG collections to select the 15 genes with the highest connectivity, and crossing the two gene sets yielded 3 hub genes (PTPRC, TYROBP, CXCR4). Validation with external datasets showed their diagnostic value for DM and AS. Analysis of immune infiltration indicates that lymphocytes and macrophages are significantly associated with the pathogenesis of DM and AS. Moreover, GSEA analysis suggested that the shared genes are enriched in various receptor interactions and multiple cytokines and receptor signaling pathways. We coupled the 3 hub genes with their respective predicted genes, identifying a potential key TF, CBFB, which interacts with all 3 hub genes.
CONCLUSIONS: This research utilized comprehensive bioinformatics techniques to explore the shared pathogenesis of DM and AS. The three key genes, including PTPRC, TYROBP, and CXCR4, are related to the pathogenesis of DM and AS. The central genes and their correlations with immune cells may serve as potential diagnostic and therapeutic targets.