基于生物信息学分析鉴定皮肌炎和动脉粥样硬化的共同机制和生物标志物。

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. METHOD: 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. CONCLUSION: 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.

背景：皮肌炎（DM）表现为一种自身免疫性和炎症性疾病，临床上以亚急性进行性近端肌肉无力、皮疹或两者兼有及肌肉外表现为特征。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，但病因和发病机制仍未完全阐明。本研究旨在利用生物信息学方法阐明影响 DM 和 AS 病理生理学的关键基因和途径。

方法：从基因表达综合数据库（GEO）中检索到 DM（GSE128470、GSE1551、GSE143323）和 AS（GSE100927、GSE28829、GSE43292）的微阵列数据集。使用加权基因共表达网络分析（WGCNA）来揭示它们的共表达模块。使用 R 软件中的“limma”包来识别差异表达基因（DEGs），并通过功能富集分析来确定共同 DEGs 的功能。使用 STRING 数据库建立蛋白质-蛋白质相互作用（PPI）网络，使用 cytoHubba 插件评估核心基因，并通过外部数据集进行验证。使用 CIBERSORT 方法对枢纽基因进行免疫浸润分析，并进行基因集富集分析（GSEA）。最后，使用 NetworkAnalyst 平台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，共确定了 271 个重叠基因。随后的 DEG 分析显示，DM 和 AS 中共有 34 个基因，包括 31 个上调基因和 3 个下调基因。分别对 WGCNA 和 DEG 集应用 Degree Centrality 算法，选择连接性最高的 15 个基因，将两个基因集交叉，得到 3 个枢纽基因（PTPRC、TYROBP、CXCR4）。用外部数据集验证表明，这些基因对 DM 和 AS 具有诊断价值。免疫浸润分析表明，淋巴细胞和巨噬细胞与 DM 和 AS 的发病机制显著相关。此外，GSEA 分析表明，共有基因富集在各种受体相互作用和多种细胞因子和受体信号通路中。我们将这 3 个枢纽基因与其各自的预测基因相结合，确定了一个潜在的关键 TF，CBFB，它与所有 3 个枢纽基因相互作用。

结论：本研究利用综合生物信息学技术探讨了 DM 和 AS 的共同发病机制。PTPRC、TYROBP 和 CXCR4 这三个关键基因与 DM 和 AS 的发病机制有关。核心基因及其与免疫细胞的相关性可能成为潜在的诊断和治疗靶点。