开发和验证一个综合性54个基于生物标志物的多种癌症风险识别模型在42,666个人:以人口为基础的前性研究,以指导先进的查策略
Renjia Zhao1, Huangbo Yuan1, Yanfeng Jiang1,2
1State Key Laboratory of Genetics and Development of Complex Phenotypes, ZhangjiangFudan International Innovation Center, Human Phenome Institute, Fudan University, Songhu Road 2005, Shanghai, 200433, China.
Biomarker research
|August 12, 2025
概括
使用生物标志物和生活方式因素的新型多种癌症风险预测模型识别了针对性查的高风险个体. 这种方法提高了癌症的早期检测,并支持精确的预防策略.
科学领域:
- 在瘤学瘤学.
- 生物标志物 生物标志物
- 流行病学 流行病学
- 机器学习 机器学习
背景情况:
- 早期癌症检测对于有效的查至关重要,特别是使用多组学和内镜等先进方法.
- 开发合并多种数据的实用多种癌症风险预测模型具有挑战性.
研究的目的:
- 开发和验证使用多尺度数据的多种癌症风险预测模型.
- 在一个大型的中国队列中,识别高风险个人进行向癌症查.
主要方法:
- 招募了42,666名参与者参加发现和验证队列.
- 整合了54个血液生物标志物和26个流行病学因素.
- 使用LASSO的机器学习来选择五种癌症风险模型 (肺,食道,肝脏,胃,结肠直肠) 的特征.
主要成果:
- 该模型使用了四个生物标志物,即年龄,性别和吸烟,在五年风险预测中获得了0.767的AUROC.
- 高风险人群 (17.19%) 占发病癌症的50.42%,风险增加了15.19倍.
- 与低风险组相比,高风险组的癌症检测是高风险组的5.02倍.
结论:
- 这是第一个基于人口的前性研究,使用多尺度数据来预测大型中国队列中的多种癌症风险.
- 风险分层模型改善了早期检测,并指导了先进查技术的应用.
- 这些发现支持精确的预防和优化医疗保健资源分配.
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