基于机器学习的转录组签名用于预测T1结直肠癌治疗切除后的瘤复发:一项回顾性多中心队列研究 (Tw1CE试验)
Takayuki Noma1,2, Karmele Saez de Gordoa3, María Daca-Alvarez4
1Department of Molecular Diagnostics and Experimental Therapeutics, Beckman Research Institute of City of Hope, Biomedical Research Center, Monrovia, CA, USA.
International journal of surgery (London, England)
|January 28, 2026
概括
一个新的转录组小组准确地预测了内镜切除后早期结直肠癌 (T1 CRC) 的复发. 这种生物标志物有助于对T1CRC患者的个性化随访和治疗决策.
科学领域:
- 在瘤学瘤学.
- 基因组学就是基因组学.
- 生物标志物发现发现
背景情况:
- T1结直肠癌 (T1CRC) 治疗依赖于内镜切除,但预测复发是具有挑战性的.
- 缺乏验证的生物标志物阻碍了T1CRC患者的风险调整后续和辅助治疗决策.
研究的目的:
- 开发和验证一个转录组分类器,用于预测T1CRC内镜切除后瘤复发.
- 评估分类器在指导风险分层和治疗决策方面的临床实用性.
主要方法:
- 一个多中心的回顾性队列研究,涉及138名T1CRC患者.
- 来自FFPE内镜样本的5个mRNA和2个miRNA的RT-qPCR量化.
- 开发基于XGBoost的转录组组,并在独立队列中进行验证.
主要成果:
- 转录组组显示出高预测性能 (AUROC 91.7%的培训,88.2%的测试).
- 由专家组确定的高风险患者的无复发存活率 (RFS) 和整体存活率 (OS) 显著降低.
- 结合淋巴侵袭改善了预测准确度 (AUROC 94.6%),证明了卓越的临床效用.
结论:
- 一个经过验证的基于机器学习的转录组分类器可以准确地预测T1 CRC从内镜样本中复发的情况.
- 这种生物标志物小组可以促进风险适应的监测,并为治疗后的辅助治疗决策提供信息.
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