PathNetDRP:一种新的生物标志物发现框架,使用途径和蛋白质-蛋白质相互作用网络来预测免疫检查点抑制剂反应
Dohee Lee1, Jaegyoon Ahn2, Jonghwan Choi3
1Department of Computer Science and Engineering, Incheon National University, 119 Academy-ro, Yeonsu-gu, Incheon, 22012, Republic of Korea.
BMC bioinformatics
|May 5, 2025
概括
通过识别免疫检查点抑制剂 (ICI) 反应的关键生物标志物,PathNetDRP增强了癌症免疫疗法. 这种基于网络的方法提高了预测的准确性,并揭示了临床应用的关键免疫路径.
科学领域:
- 计算生物学是一种计算生物学.
- 免疫治疗是一种免疫疗法.
- 发现生物标志物的发现.
背景情况:
- 预测免疫检查点抑制剂 (ICI) 反应在癌症免疫治疗中至关重要,但具有挑战性.
- 目前的方法,如基因表达分析,可能无法捕捉复杂的免疫反应机制.
- 现有的网络模型缺乏用于评估通路内的基因贡献的定量框架.
研究的目的:
- 开发PathNetDRP,这是一个用于识别功能相关生物标志物的新框架,用于ICI响应预测.
- 整合生物通路,蛋白质-蛋白质相互作用网络和机器学习.
- 克服现有的生物标志物发现方法的局限性.
主要方法:
- 利用PageRank算法对ICI相关基因进行优先排序.
- 将优先级的基因映射到生物通路中.
- 计算PathNetGene得分以量化基因对免疫反应的贡献.
- 集成网络和路径信息与机器学习.
主要成果:
- PathNetDRP表现出强大的预测性能,将交叉验证AUC从0.780增加到0.940.
- 该框架通过结合生物背景来改善生物标志物选择.
- 确定了与免疫相关的关键途径,为ICI反应调节提供了洞察力.
结论:
- 通过PathNetDRP识别的生物标志物显示出强大的临床实用性预测性能.
- 丰富分析提供了更深入地了解免疫通路在ICI反应中的作用.
- 未来的工作将整合额外的功能,如瘤突变负担,以改进模型.
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