Zheng Yang, Fang Miaojia, Sanan Shriya, Meng Xi-Hui, Huang Jie-Feng, Qian Yu
Zhejiang Provincial Hospital of Traditional Chinese Medicine, Hangzhou, People's Republic of China.
Institute of Forensic Science, Yuhang Public Security Department, Hangzhou, People's Republic of China.
J Inflamm Res. 2024 Dec 8;17:10681-10697. doi: 10.2147/JIR.S493889. eCollection 2024.
Osteoarthritis (OA) is a common age-related joint disease characterized by joint destruction and impaired quality of life. Angiogenesis plays a vital role in OA progression. This study aimed to identify key angiogenesis-related genes (ARGs) in OA using transcriptomic and machine learning methods.
The GSE55235 dataset (10 OA and 10 healthy synovial tissue samples) was analyzed for differentially expressed genes (DEGs), integrated with weighted gene co-expression network analysis (WGCNA), and ARGs to identify differentially expressed ARGs (DE-ARGs). Candidate genes were identified through three machine learning algorithms and evaluated using ROC curve analysis. Gene set enrichment analysis (GSEA), immune cell infiltration analysis, and therapeutic agent prediction were performed. Synovial samples from 5 OA patients and 5 matched controls were collected for RT-qPCR validation of biomarkers.
From 1552 DEGs, 11 DE-ARGs were identified, and six candidate genes were selected using machine learning. Four genes-COL3A1, OLR1, STC1, and KCNJ8-showed AUC >0.8 in both GSE55235 and GSE1919, indicating strong diagnostic value. GSEA linked biomarkers to the "lysosome" pathway, and eosinophils and Th2 cells were significantly associated with biomarkers. Potential therapeutic agents included bisphenol A, tetrachlorodibenzo-p-dioxin, and valproic acid. Clinical validation confirmed that COL3A1, OLR1, and STC1 expression levels were consistent with database findings.
The study identified COL3A1, OLR1, STC1, and KCNJ8 as key angiogenesis-related biomarkers in osteoarthritis, which could serve as potential diagnostic tools and therapeutic targets. The research underscores the importance of angiogenesis in osteoarthritis progression and suggests that targeting angiogenesis-related pathways may offer new treatment strategies.
骨关节炎（OA）是一种常见的与年龄相关的关节疾病，其特征为关节破坏和生活质量受损。血管生成在OA进展中起关键作用。本研究旨在使用转录组学和机器学习方法鉴定OA中关键的血管生成相关基因（ARGs）。
对GSE55235数据集（10个OA和10个健康滑膜组织样本）进行差异表达基因（DEGs）分析，与加权基因共表达网络分析（WGCNA）整合，并鉴定ARGs以确定差异表达的ARGs（DE-ARGs）。通过三种机器学习算法鉴定候选基因，并使用ROC曲线分析进行评估。进行基因集富集分析（GSEA）、免疫细胞浸润分析和治疗药物预测。收集5例OA患者和5例匹配对照的滑膜样本，用于生物标志物的RT-qPCR验证。
从1552个DEGs中鉴定出11个DE-ARGs，并使用机器学习选择了6个候选基因。四个基因——COL3A1、OLR1、STC1和KCNJ8——在GSE55235和GSE1919中均显示AUC>0.8，表明具有很强的诊断价值。GSEA将生物标志物与“溶酶体”途径联系起来，嗜酸性粒细胞和Th2细胞与生物标志物显著相关。潜在的治疗药物包括双酚A、四氯二苯并对二恶英和丙戊酸。临床验证证实COL3A1、OLR1和STC1的表达水平与数据库结果一致。
该研究确定COL3A1、OLR1、STC1和KCNJ8为骨关节炎中关键的血管生成相关生物标志物，可作为潜在的诊断工具和治疗靶点。该研究强调了血管生成在骨关节炎进展中的重要性，并表明靶向血管生成相关途径可能提供新的治疗策略。
