通过多队列数据集成,构建和临床验证基于机器学习的骨髓瘤共识预后签名 (MLPS)
Liangxi Chen1, Hanling Wu1, Ranyue Ren1
1Department of Orthopedic Surgery, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, 1095 Jiefang Avenue, 430030 Wuhan, Hubei, China.
International immunopharmacology
|November 12, 2025
概括
针对骨髓瘤的新型机器学习预后签名 (MLPS) 准确预测患者的生存率,并建议个性化治疗策略,改进了对这一具有挑战性的骨癌现有模型.
科学领域:
- 在瘤学瘤学.
- 生物信息学是一种生物信息学.
- 基因组学就是基因组学.
背景情况:
- 骨髓瘤是年轻人中最常见的原发性骨癌,由于分子复杂性,其生存结果不佳.
- 目前的骨髓瘤预后模型不足以开发个性化疗法.
研究的目的:
- 开发一种基于机器学习的骨髓瘤预后签名,使用多队列的转录基因数据.
- 评估签名分层患者,预测存活率和指导治疗决策的能力.
主要方法:
- 集成的多队列转录基因数据集.
- 应用了十种不同的机器学习算法来识别预后基因.
- 进行了功能丰富和单细胞分析.
- 通过C指数和体外实验验验证了签名的性能.
主要成果:
- 开发了一种11基因的机器学习预后签名 (MLPS),可靠地将骨髓瘤患者分为高风险和低风险组 (C指数 = 0.862).
- 低风险组显示免疫激活 ("热瘤"),而高风险组显示增殖途径.
- MLPS预测了化学疗法和免疫疗法的不同反应.
- 确定LGR4是一种促进骨髓瘤细胞增殖和迁移的瘤基因.
结论:
- MLPS是骨髓瘤预后评估的一个强有力的工具.
- 在骨髓瘤中,MLPS促进了个性化的治疗决策.
- 准LGR4可能为骨髓瘤提供治疗策略.
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