Department of Orthopedics, The First Affiliated Hospital of Nanchang University, Nanchang, Jiangxi, China.
The First Clinical Medical College, Nanchang University, Nanchang, Jiangxi, China.
Front Immunol. 2023 Aug 31;14:1230027. doi: 10.3389/fimmu.2023.1230027. eCollection 2023.
Frozen shoulder (FS) and Dupuytren's disease (DD) are two closely related diseases, but the mechanism of their interaction is unknown. Our study sought to elucidate the molecular mechanism of these two diseases through shared gene and protein interactions.
GSE75152 and GSE140731 data were downloaded from the Gene Expression Omnibus (GEO) database, and shared genes between FS and DD were selected by using R packages. Then, we used Cytoscape software and the STRING database to produce a protein-protein interaction (PPI) network. Important interaction networks and hub genes were selected through MCODE and cytoHubba algorithms. To explore the potential mechanisms of the development of the two diseases, the hub genes were further enriched by GO and KEGG analyses. We predicted the transcription factors (TFs) of hub genes with Transcriptional Regulatory Relationships Unraveled by Sentence-based Text mining (TRRUST). Moreover, we identified candidate genes for FS with DD with cytoHubba and machine learning algorithms. Finally, we analyzed the role of immunocyte infiltration in FS and constructed the relationship between candidate genes and immunocytes in FS.
We identified a total of 321 shared genes. The results of GO and KEGG enrichment of shared genes showed that extracellular matrix and collagen fibril tissue play a certain role in the occurrence and development of disease. According to the importance of genes, we constructed the key PPI network of shared genes and the top 15 hub genes for FS with DD. Then, we predicted that five TFs are related to the hub genes and are highly expressed in the FS group. Machine learning results show that the candidate genes POSTN and COL11A1 may be key for FS with DD. Finally, immune cell infiltration revealed the disorder of immunocytes in FS patients, and expression of candidate genes can affect immunocyte infiltration.
We identified a PPI network, 15 hub genes, and two immune-related candidate genes (POSTN and COL11A1) using bioinformatics analysis and machine learning algorithms. These genes have the potential to serve as diagnostic genes for FS in DD patients. Furthermore, our study reveals disorder of immunocytes in FS.
冻结肩（FS）和掌腱膜挛缩症（DD）是两种密切相关的疾病，但它们的相互作用机制尚不清楚。本研究旨在通过共享基因和蛋白质相互作用来阐明这两种疾病的分子机制。
从基因表达综合数据库（GEO）下载 GSE75152 和 GSE140731 数据，使用 R 包选择 FS 和 DD 之间的共享基因。然后，我们使用 Cytoscape 软件和 STRING 数据库构建蛋白质-蛋白质相互作用（PPI）网络。通过 MCODE 和 cytoHubba 算法选择重要的互作网络和枢纽基因。为了探索两种疾病发展的潜在机制，我们进一步通过 GO 和 KEGG 分析对枢纽基因进行富集。我们使用基于句子的文本挖掘转录调控关系揭示（TRRUST）预测枢纽基因的转录因子（TFs）。此外，我们使用 cytoHubba 和机器学习算法确定 FS 与 DD 相关的候选基因。最后，我们分析了免疫细胞浸润在 FS 中的作用，并构建了 FS 中候选基因与免疫细胞的关系。
我们共鉴定出 321 个共享基因。GO 和 KEGG 富集分析结果表明，细胞外基质和胶原纤维组织在疾病的发生和发展中起着一定的作用。根据基因的重要性，我们构建了共享基因的关键 PPI 网络和 FS 与 DD 的前 15 个枢纽基因。然后，我们预测了与枢纽基因相关的 5 个 TFs，这些 TFs在 FS 组中高表达。机器学习结果表明，候选基因 POSTN 和 COL11A1 可能是 FS 与 DD 的关键基因。最后，免疫细胞浸润揭示了 FS 患者免疫细胞的紊乱，候选基因的表达可以影响免疫细胞的浸润。
我们通过生物信息学分析和机器学习算法，确定了一个 PPI 网络、15 个枢纽基因和两个与免疫相关的候选基因（POSTN 和 COL11A1）。这些基因可能成为 DD 患者 FS 的诊断基因。此外，我们的研究揭示了 FS 中免疫细胞的紊乱。
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