多种编程细胞死亡模式和骨关节炎的诊断价值,通过整合多组学数据分析
Mingjie Wei1, Xiangwen Shi1, Wenbao Tang1
1Department of Orthopedic Surgery, 920th Hospital of Joint Logistics Support Force of PLA, Kunming, China.
Current stem cell research & therapy
|January 23, 2026
概括
编程细胞死亡 (PCD) 基因在骨关节炎 (OA) 发病过程中至关重要. 这项研究确定了关键的PCD相关基因,并开发了OA的诊断模型,提供了潜在的临床生物标志物.
科学领域:
- 基因组学和分子生物学
- 免疫学 免疫学 免疫学
- 生物标志物发现发现
背景情况:
- 骨关节炎 (OA) 导致慢性疼痛和残疾,严重影响生活质量.
- 编程细胞死亡 (PCD) 参与了OA的发病,但其模式和作用尚未完全理解.
- 需要对OA中PCD模式进行全面分析,以阐明其潜在的诊断和治疗价值.
研究的目的:
- 通过使用多omics数据,全面分析骨关节炎 (OA) 中编程细胞死亡 (PCD) 模式.
- 确定与OA中PCD相关的差异表达基因 (DEGs).
- 根据与枢纽PCD相关的基因开发OA的诊断模型.
主要方法:
- 使用来自GEO数据库的批量转录数据进行OA分析.
- 在13个PCD模式中进行了差异表达分析和无监督聚类,以定义OA分子亚型.
- 采用机器学习算法来识别与枢纽PCD相关的DEG并构建诊断模型;使用scRNA-seq和动物模型验证了发现.
主要成果:
- 确定了61种PCD相关的DEG和两种不同的OA分子亚型,其中一种富含免疫通路.
- 开发了一个非常准确的OA诊断模型 (AUC = 0.993),基于十个已识别的枢纽PCD相关DEG.
- 在OA胆固醇细胞中证实PCD得分升高,在OA细胞和老鼠模型中验证了枢纽基因表达.
结论:
- 这项多学科研究提供了对OA中PCD相关基因的诊断和分类潜力的初步见解.
- 确定了OA诊断和临床应用的潜在生物标志物.
- 强调PCD在OA病变发生过程中的重要作用,并建议未来研究的途径.
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