Transcriptomic analyses and machine-learning methods reveal dysregulated key genes and potential pathogenesis in human osteoarthritic cartilage.

IF 4.7 2区 医学 Q2 CELL & TISSUE ENGINEERING
Di Zhao, Ling-Feng Zeng, Gui-Hong Liang, Ming-Hui Luo, Jian-Ke Pan, Yao-Xing Dou, Fang-Zheng Lin, He-Tao Huang, Wei-Yi Yang, Jun Liu
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引用次数: 0

Abstract

Aims: This study aimed to explore the biological and clinical importance of dysregulated key genes in osteoarthritis (OA) patients at the cartilage level to find potential biomarkers and targets for diagnosing and treating OA.

Methods: Six sets of gene expression profiles were obtained from the Gene Expression Omnibus database. Differential expression analysis, weighted gene coexpression network analysis (WGCNA), and multiple machine-learning algorithms were used to screen crucial genes in osteoarthritic cartilage, and genome enrichment and functional annotation analyses were used to decipher the related categories of gene function. Single-sample gene set enrichment analysis was performed to analyze immune cell infiltration. Correlation analysis was used to explore the relationship among the hub genes and immune cells, as well as markers related to articular cartilage degradation and bone mineralization.

Results: A total of 46 genes were obtained from the intersection of significantly upregulated genes in osteoarthritic cartilage and the key module genes screened by WGCNA. Functional annotation analysis revealed that these genes were closely related to pathological responses associated with OA, such as inflammation and immunity. Four key dysregulated genes (cartilage acidic protein 1 (CRTAC1), iodothyronine deiodinase 2 (DIO2), angiopoietin-related protein 2 (ANGPTL2), and MAGE family member D1 (MAGED1)) were identified after using machine-learning algorithms. These genes had high diagnostic value in both the training cohort and external validation cohort (receiver operating characteristic > 0.8). The upregulated expression of these hub genes in osteoarthritic cartilage signified higher levels of immune infiltration as well as the expression of metalloproteinases and mineralization markers, suggesting harmful biological alterations and indicating that these hub genes play an important role in the pathogenesis of OA. A competing endogenous RNA network was constructed to reveal the underlying post-transcriptional regulatory mechanisms.

Conclusion: The current study explores and validates a dysregulated key gene set in osteoarthritic cartilage that is capable of accurately diagnosing OA and characterizing the biological alterations in osteoarthritic cartilage; this may become a promising indicator in clinical decision-making. This study indicates that dysregulated key genes play an important role in the development and progression of OA, and may be potential therapeutic targets.

转录组分析和机器学习方法揭示了人类骨关节炎软骨中失调的关键基因和潜在的发病机制。
目的:本研究旨在探讨骨关节炎(OA)患者软骨水平关键基因失调的生物学和临床重要性,以寻找诊断和治疗OA的潜在生物标志物和靶点:方法:从基因表达总库(Gene Expression Omnibus)数据库中获取六组基因表达谱。采用差异表达分析、加权基因共表达网络分析(WGCNA)和多种机器学习算法筛选骨关节炎软骨中的关键基因,并通过基因组富集和功能注释分析解读基因功能的相关类别。单样本基因组富集分析用于分析免疫细胞浸润。相关性分析用于探讨枢纽基因与免疫细胞以及与关节软骨降解和骨矿化相关的标记物之间的关系:结果:骨关节炎软骨中明显上调的基因与WGCNA筛选出的关键模块基因的交叉点共得到46个基因。功能注释分析表明,这些基因与OA相关的病理反应(如炎症和免疫)密切相关。利用机器学习算法确定了四个关键失调基因(软骨酸性蛋白 1 (CRTAC1)、碘甲状腺素脱碘酶 2 (DIO2)、血管生成素相关蛋白 2 (ANGPTL2) 和 MAGE 家族成员 D1 (MAGED1))。这些基因在训练队列和外部验证队列中都具有很高的诊断价值(接收者操作特征大于 0.8)。这些中枢基因在骨关节炎软骨中的表达上调意味着更高水平的免疫浸润以及金属蛋白酶和矿化标志物的表达,提示了有害的生物学改变,并表明这些中枢基因在 OA 的发病机制中起着重要作用。研究还构建了一个竞争性内源性RNA网络,以揭示潜在的转录后调控机制:目前的研究探索并验证了骨关节炎软骨中调控失调的关键基因集,该基因集能够准确诊断 OA 并描述骨关节炎软骨的生物学改变;这可能成为临床决策中的一个有前途的指标。这项研究表明,失调的关键基因在 OA 的发生和发展过程中起着重要作用,可能成为潜在的治疗靶点。
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来源期刊
Bone & Joint Research
Bone & Joint Research CELL & TISSUE ENGINEERING-ORTHOPEDICS
CiteScore
7.40
自引率
23.90%
发文量
156
审稿时长
12 weeks
期刊介绍: The gold open access journal for the musculoskeletal sciences. Included in PubMed and available in PubMed Central.
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