周围血液的TCR剧目改善了在多种癌症类型中使用癌症预测器的早期检测
Yinglei Tang1, Xinyi Liao2, Bo Liao1,3
1School of Mathematics and Statistics, Hainan Normal University, Haikou, China.
Frontiers in oncology
|September 12, 2025
概括
监测T细胞受体 (TCR) 谱系变化可以帮助早期发现癌症. 一个新的深度学习框架,DeepCaTCR,准确地识别与癌症相关的TCR,并通过一种新的基于差异的评分策略改进早期癌症查.
科学领域:
- 免疫学 免疫学 免疫学
- 生物信息学是一种生物信息学.
- 计算生物学 计算生物学
背景情况:
- 免疫系统在癌症的早期阶段准瘤抗原.
- T细胞受体 (TCR) 识别了由MHC.呈现的瘤抗原.
- 监测TCR谱的变化显示了癌症检测的前景.
研究的目的:
- 开发一个深度学习框架 (DeepCaTCR) 以提高癌症相关T细胞受体的预测.
- 通过分析瘤免疫微环境来改善早期癌症诊断.
主要方法:
- 使用一个深度学习框架 (DeepCaTCR),结合1D CNN,BiLSTM和自我注意力.
- 从不同长度的氨基酸碎片中提取的特征.
- 引入了基于差异的曲目评分策略.
主要成果:
- DeepCaTCR实现了0.863的AUC和0.669的F1得分,超过了现有的模型.
- 成功地将瘤透的淋巴细胞与健康的外周血液区分开来 (AUC > 0.95).
- 获得了高灵敏度 (62.5%) 和特异性 (>98%) 在周围血液中早期癌症检测.
- 基于差异的得分增加了AUC到0.967的泛癌早期查.
结论:
- DeepCaTCR是一种用于分析瘤免疫微环境的新工具.
- 基于差异的评分策略为早期癌症诊断提供了重要的翻译潜力.
- 这种方法通过TCR谱系分析提高了早期癌症检测的有效性.
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