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通过机器学习,识别了与细胞核衰老相关的基因,作为椎间盘退化的潜在生物标志物

Min Xiang¹, Qingping Peng², Feifei Dai³

¹Department of Orthopedics, Affiliated Hospital of Guangdong Medical University, Zhanjiang, 524000, China.

European journal of medical research

|September 30, 2025

概括

椎间盘退化 (IVDD) 是一个日益严重的健康问题. 这项研究确定了TAF13作为与IVDD进展相关的关键基因和潜在的诊断生物标志物.

科学领域:

生物医学研究的研究.
基因组学就是基因组学.
分子生物学分子生物学

背景情况:

椎间盘退化 (IVDD) 是一个全球性的健康问题,其机制尚不清楚.
细胞衰老越来越多地与IVDD的发病有关.
确定与IVDD相关的关键基因和途径对于开发有效的治疗方法至关重要.

研究的目的:

使用机器学习识别与IVDD相关的关键衰老相关基因 (SRG).
查IVDD的潜在诊断生物标志物.
在正常和IVDD状态下探索已识别的基因的功能途径.

主要方法:

来自基因表达综合数据库的基因表达概况 (GSE70362) 的分析.
利用权重基因共同表达网络分析 (WGCNA) 和机器学习 (随机森林,SVM-RFE) 来识别枢纽基因.
使用KEGG和GO数据库进行功能丰富分析.
使用定量聚合酶链反应 (qPCR) 和西斑 (WB) 验证关键基因/蛋白质.

主要成果:

在正常和IVDD核肉质样本之间确定了470个差异表达基因 (DEG).
功能丰富显示了与转录调节,Wnt信号传递和线粒体过程的关联.
TAF13被确定为一个与衰老相关的枢纽差异表达基因 (SRDEG),具有IVDD的显著诊断潜力.
在体内和临床样本验证证实了TAF13表达趋势.

结论:

细胞核的衰老可能会导致IVDD的进展.
TAF13显示出作为IVDD的新型诊断生物标志物的潜力.
对TAF13作用的进一步研究可能会导致IVDD的新治疗策略.

关键词:

生物标志物生物标志物细胞衰老细胞衰老椎间盘退化椎间盘退化机器学习机器学习它的核心是脉的.在TAF13中,TAF13是什么?

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