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通过加权基因共表达网络分析和孟德尔随机化探索冻结肩的致病基因。

Exploring Pathogenic Genes in Frozen Shoulder through weighted gene co-expression network analysis and Mendelian Randomization.

机构信息

Division of Joint Surgery and Sports Medicine, Department of Orthopedic Surgery, Zhongnan Hospital of Wuhan University, Wuhan 430071, China.

出版信息

Int J Med Sci. 2024 Oct 21;21(14):2745-2758. doi: 10.7150/ijms.98505. eCollection 2024.

Abstract

Frozen shoulder (FS) is characterized by the thickening and fibrosis of the joint capsule, leading to joint contracture and a reduction in joint volume. The precise etiology responsible for these pathological changes remains elusive. Therefore, the primary aim of this study was to explore the potential involvement of pathogenic genes in FS and analyze their underlying roles in the disease progression. Differential expression analysis and weighted gene co-expression network analysis (WGCNA) were employed to investigate co-expressed genes potentially associated with FS. Gene Ontology and Kyoto Encyclopedia of Genes and Genomes analyses were conducted to elucidate the potential roles of these co-expressed genes. Subsequently, Mendelian randomization (MR) analysis was performed using expression quantitative trait loci datasets for the co-expressed genes, combined with summary statistics from the genome-wide association study of FS, aiming to identify key genes causally associated with FS. The identified key genes were further validated through reverse transcription-quantitative PCR (RT-qPCR). Additionally, a nomogram model and receiver operating characteristic (ROC) curves were established to assess the diagnostic value of the hub genes. Furthermore, the infiltration of immune cells was evaluated using the CIBERSORT algorithm and the relationship between key genes and immune-infiltrating cells was analyzed. 295 overlapping co-expressed genes were identified by intersecting the differentially expressed genes with the hub genes obtained from associated modules identified through WGCNA. Utilizing MR analysis, four key genes, namely ADAMTS1, NR4A2, PARD6G and SMKR1, were found to exhibit positive causal relationships with FS, which were subsequently validated through RT-qPCR analysis. Moreover, the diagnostic value of these four key genes was demonstrated through the development of a nomogram model and the construction of ROC curves. Notably, a causal relationship between ADAMTS1 and immune cell infiltration in FS was observed. Our study suggested genetic predisposition to higher expression levels of ADAMTS1, NR4A2, PARD6G and SMKR1, was associated with an increased risk of FS. Further investigations elucidating the functional roles of these genes will enhance our understanding of the pathogenesis of FS and may facilitate the development of targeted treatment strategies.

摘要

冻结肩 (FS) 的特征是关节囊增厚和纤维化,导致关节挛缩和关节容积减少。导致这些病理变化的确切病因尚不清楚。因此,本研究的主要目的是探讨致病基因在 FS 中的潜在作用,并分析它们在疾病进展中的潜在作用。采用差异表达分析和加权基因共表达网络分析 (WGCNA) 研究与 FS 相关的潜在共表达基因。进行基因本体论和京都基因与基因组百科全书分析,以阐明这些共表达基因的潜在作用。随后,使用共表达基因的表达数量性状基因座数据集和 FS 的全基因组关联研究的汇总统计数据进行孟德尔随机分析,旨在确定与 FS 因果相关的关键基因。通过逆转录定量 PCR (RT-qPCR) 进一步验证鉴定的关键基因。此外,建立了列线图模型和接收者操作特征 (ROC) 曲线,以评估枢纽基因的诊断价值。进一步使用 CIBERSORT 算法评估免疫细胞的浸润,并分析关键基因与免疫浸润细胞之间的关系。通过交集差异表达基因与 WGCNA 获得的相关模块中的枢纽基因,鉴定出 295 个重叠的共表达基因。利用 MR 分析,发现 ADAMTS1、NR4A2、PARD6G 和 SMKR1 这四个关键基因与 FS 呈正因果关系,随后通过 RT-qPCR 分析进行验证。此外,通过建立列线图模型和构建 ROC 曲线,证明了这四个关键基因的诊断价值。值得注意的是,观察到 ADAMTS1 与 FS 中免疫细胞浸润之间存在因果关系。本研究表明,ADAMTS1、NR4A2、PARD6G 和 SMKR1 等基因表达水平升高的遗传易感性与 FS 风险增加相关。进一步研究这些基因的功能作用将增强我们对 FS 发病机制的理解,并可能有助于开发靶向治疗策略。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/4f2d/11539380/fdd92a03dd21/ijmsv21p2745g001.jpg

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