在骨关节炎中通过整合性多组学和机器学习分析来表征编程细胞死亡特征
Qinchao Sun1, Ye Zhong1, Gaoxiang Huang1
1Department of Orthopedics, Hangzhou Fuyang First People's Hospital, Hangzhou, China.
PeerJ
|October 15, 2025
概括
编程细胞死亡 (PCD) 基因在骨关节炎 (OA) 中被改变,S100A9,PMAIP1和EDA2R作为危险因素,FASN作为保护因素. PCD显示了对OA的诊断潜力和治疗影响.
科学领域:
- 生物化学 生物化学
- 遗传学 遗传学是一种遗传学.
- 免疫学 免疫学 免疫学
背景情况:
- 编程细胞死亡 (PCD) 对组织平衡至关重要,但其在骨关节炎 (OA) 中的作用尚不清楚.
- 信号分子影响新陈代谢和疾病,但它们在OA病变发生中的具体参与需要阐明.
研究的目的:
- 系统地评估PCD相关基因在OA中的预测价值,遗传改变和治疗影响.
- 研究OA中PCD基因相关的诊断疗效和免疫透.
主要方法:
- 进行了包括转录组和单细胞转录组数据在内的多组学分析.
- 使用权重基因共同表达网络分析和机器学习模型来识别关键PCD基因.
- 使用缺氧ATDC5冠状细胞的体外OA模型被用于研究基因表达和功能.
主要成果:
- 在OA中,S100A9,PMAIP1和EDA2R受到上调,表明它们是风险因素,而FASN受到下调,表明具有保护作用.
- 经过PCD检测,可以证明OA的可靠诊断准确性.
- 在S100A9,PMAIP1,EDA2R的降低调节和FASN的过度表达改善了体外低氧诱导的细胞损伤和炎症.
结论:
- PCD基因在关节炎的发病和进展中起着重要作用.
- 人工智能驱动的PCD基因分析为个性化OA脆弱性评估和治疗策略提供了潜力.
- 四个枢纽PCD基因的异常表达为OA和免疫媒介疾病提供了新的研究途径.
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