骨关节炎(OA)是一种使人衰弱的关节病症,其特征在于关节软骨的进行性退化。虽然离子通道在OA发病机制中的作用日益得到认可,诊断标记和靶向治疗仍然有限。
■在这项研究中,我们分析了GSE48556数据集,以鉴定OA和正常对照中差异表达的离子通道相关基因(DEGs).我们采用了机器学习算法,最小绝对收缩和选择运算符(LASSO),和支持向量机递归特征消除(SVM-RFE)来选择潜在的诊断标记。然后进行基因集富集分析(GSEA)和基因集变异分析(GSVA),以探索潜在的诊断标记物参与生物学途径。最后,加权基因共表达网络分析(WGCNA)用于鉴定与OA相关的关键基因。
■我们总共确定了47个DEG,大多数参与瞬时受体电位(TRP)途径。七个基因(CHRNA4,GABRE,HTR3B,KCNG2,KCNJ2,LRRC8C,和TRPM5)被鉴定为区分OA与健康样品的最佳特征基因。我们进行了聚类分析,确定了两种不同的OA亚型,C1和C2,具有差别基因表达和免疫细胞浸润谱。然后我们确定了三个关键基因(PPP1R3D,ZNF101和LOC651309)与OA相关。我们使用这些基因构建了一个预测模型,并使用GSE46750数据集进行了验证,表现出合理的准确性和特异性。
■我们的发现为离子通道相关基因在OA发病机制中的作用提供了新的见解,并为OA的治疗提供了潜在的诊断标志物和治疗靶标。
UNASSIGNED: Osteoarthritis (OA) is a debilitating joint disorder characterized by the progressive degeneration of articular cartilage. Although the role of ion channels in OA pathogenesis is increasingly recognized, diagnostic markers and targeted therapies remain limited.
UNASSIGNED: In this study, we analyzed the GSE48556 dataset to identify differentially expressed ion channel-related genes (DEGs) in OA and normal controls. We employed machine learning algorithms, least absolute shrinkage and selection operator(LASSO), and support vector machine recursive feature elimination(SVM-RFE) to select potential diagnostic markers. Then the gene set enrichment analysis (GSEA) and gene set variation analysis (GSVA) were performed to explore the potential diagnostic markers\' involvement in biological pathways. Finally, weighted gene co-expression network analysis (WGCNA) was used to identify key genes associated with OA.
UNASSIGNED: We identified a total of 47 DEGs, with the majority involved in transient receptor potential (TRP) pathways. Seven genes (CHRNA4, GABRE, HTR3B, KCNG2, KCNJ2, LRRC8C, and TRPM5) were identified as the best characteristic genes for distinguishing OA from healthy samples. We performed clustering analysis and identified two distinct subtypes of OA, C1, and C2, with differential gene expression and immune cell infiltration profiles. Then we identified three key genes (PPP1R3D, ZNF101, and LOC651309) associated with OA. We constructed a prediction model using these genes and validated it using the GSE46750 dataset, demonstrating reasonable accuracy and specificity.
UNASSIGNED: Our findings provide novel insights into the role of ion channel-related genes in OA pathogenesis and offer potential diagnostic markers and therapeutic targets for the treatment of OA.
As society ages, the incidence of knee osteoarthritis continues to rise, bringing with it a series of social impacts and medical pressure. Despite the increasing recognition of the role of ion channels in the pathogenesis of OA, diagnostic markers and targeted therapies remain limited.This study investigated the role of TRP as possible diagnostic tools for OA.Seven TRP-related genes were identified as the best traits to distinguish OA from healthy samples, and then we constructed and validated risk scores for three key genes (PPP1R3D, ZNF101, and LOC651309) relevant to OA ion channel gene modules.Our findings provide novel insights into the role of ion channel-related genes in OA pathogenesis and offer a reference for further clinical diagnosis.