在机器学习模型中,细胞因子签名优于免疫子集,用于预测中性粒细胞移植中的急性移植对宿主疾病
Mohini Mendiratta1, Praful Pandey1, Shobhit Pandey2
1Department of Medical Oncology, Dr. B. R. Ambedkar Institute Rotary Cancer Hospital, All India Institute of Medical Sciences, New Delhi, Delhi, India, aiims.edu.
Journal of immunology research
|March 11, 2026
概括
机器学习模型使用细胞因子概况准确地预测异构干细胞移植后的急性移植对宿主疾病 (aGvHD). 细胞因子是早期aGvHD检测和管理策略的优越预测剂.
科学领域:
- 免疫学 免疫学 免疫学
- 移植医学 移植医学
- 计算生物学 计算生物学
背景情况:
- 急性移植对宿主疾病 (aGvHD) 是所有原性造血干细胞移植 (Allo-HSCT) 后的一个显著的免疫并发症.
- 复杂的免疫细胞因子相互作用驱动aGvHD的发病.
- 早期预测aGvHD对于患者管理至关重要.
研究的目的:
- 使用机器学习 (ML) 为aGvHD开发早期预测模型.
- 确定关键的免疫和细胞因子生物标志物,以在移植时预测aGvHD.
- 为了比较免疫子集与细胞因子概况的预测性能.
主要方法:
- 预计招募70名接受Allo-HSCT的患者.
- 通过流细胞计和细胞因子通过ELISA对周围血液免疫子集的分析.
- 在48个特征 (34个免疫,14个细胞因子) 上训练ML模型 (SVC,决策树,随机森林).
主要成果:
- aGvHD患者表现出改变的免疫特征 (降低CD4+/CD8+比率,降低Tregs) 和增加的促炎细胞因子.
- 机器学习模型取得了出色的预测性能.
- 单独或与免疫数据相结合的细胞因子概况产生了完美的准确性 (1.00),超过了免疫子集特定模型.
结论:
- 与免疫子集相比,细胞因子档案是aGvHD的优越预测因素.
- 将细胞因子配置文件集成到ML模型中可以提高aGvHD风险预测.
- 这些发现支持用于早期aGvHD检测和管理的生物标志物导向策略.
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