从组织档案到液体活检:转移学习用于基于微RNA的肺癌诊断
Jiajia Song1,2,3, Ruiting Liu1,2,3, Yixuan Wu1,2,3
1State Key Laboratory of Chemo and Biosensing, Hunan University, Changsha 410082, P. R. China.
Analytical chemistry
|January 23, 2026
概括
一种新的转移学习方法增强了血清微RNA (miRNA) 分析,用于非侵入性肺癌诊断. 这种方法有效地将组织数据的知识转移到有限的血清样本,实现高诊断精度.
科学领域:
- 生物分子分析.
- 癌症诊断 癌症诊断 癌症诊断
- 生物信息学是一种生物信息学.
背景情况:
- 血清微RNA (miRNA) 液体活检显示出对非侵入性肺癌诊断的前景.
- 挑战包括庞大的miRNA组合和有限的临床血清样本.
- 现有的方法难以从组织到血清数据的域适应.
研究的目的:
- 开发一种有效的基于血清的miRNA分类器,用于肺癌检测.
- 为了使有效的领域适应和知识从组织转移到血清数据.
- 为了验证液体活检的转移学习策略.
主要方法:
- 开发了一种转移学习策略,并与特征空间对齐.
- 基因算法驱动的特征选择确定了一个4-miRNA面板.
- 逆转录定量PCR (RT-qPCR) 在血清样本中量化miRNA表达.
主要成果:
- 一个4-miRNA小组 (miR-139-5p,miR-10a-5p,miR-148a-3p,miR-30d-5p) 在组织分类中实现了AUC>0.98.
- 转移学习模型在临床血清样本中达到91.5%的准确度和92.2%的灵敏度.
- 该方法证明了有效的领域适应和知识转移.
结论:
- 开发的转移学习策略为高精度液体活检提供了具有成本效益的解决方案.
- 这种方法克服了小型,临床注释的血清样本集的局限性.
- 它显著推进了使用血清miRNAs的非侵入性肺癌诊断.
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