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Updated: Jan 13, 2026

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人体外围血液中的长非编码RNA作为预测年龄的生物标志物

Anqi Chen¹, Yujia Xuan¹, Fan Yang²

¹Institute of Forensic Science, Fudan University, Shanghai 200032, China.

Forensic science international. Genetics

|January 9, 2026

概括

长非编码RNAs (lncRNAs) 显示出法医年龄估计的潜力. 使用lncRNA表达的机器学习模型在个体中实现了准确的年龄预测,为法医科学提供了新的生物标志物.

科学领域:

基因组学就是基因组学.
法医科学法医科学法医科学
分子生物学分子生物学

背景情况:

在法医调查中,时间学年龄至关重要.
长非编码RNAs (lncRNAs) 越来越多地被认为是它们在衰老过程中的作用.
对于预测年龄的lncRNAs的实用性,特别是在法医环境中,在很大程度上是未被探索的.

研究的目的:

研究 lncRNAs作为预测年龄的生物标志物的潜力.
开发和验证用于法医年龄估计的机器学习模型,使用lncRNA表达数据.

主要方法:

分析了来自汉族中国人的外周血液样本的RNA测序数据.
不同表达分析确定了与年龄相关的lncRNAs (DElncRNAs).
六个机器学习算法,包括XGBoost和LightGBM,用于构建年龄预测模型.

主要成果:

在年轻人和老年人之间观察到显著的lncRNA表达变化.
基因本体学和KEGG通路分析表明,这些lncRNA与线粒体功能,端粒维护和神经退行性疾病通路有关.
在测试队列中,XGBoost模型表现出最佳性能,平均绝对误差 (MAE) 为8.04年.
外部验证证实了XGBoost和LightGBM模型的稳定性,MAE在7.99至8.21年之间.

结论:

lncRNA表达特征可以有效地用于准确的年龄预测.
这项研究提供了新的lncRNA生物标志物和法医年龄估计的方法见解.
这些发现突显了基于 lncRNA 的方法在法医科学中的可行性和潜力.

关键词:

年龄预测年龄预测这是一个LncRNARNA.机器学习机器学习周围血液周围血液在RNA-seqqq.

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