融合基因调控网络与深度学习，提升癌症转移预测精准度
Genotype-Phenotype Integration through Machine Learning and Personalized Gene Regulatory Networks for Cancer Metastasis Prediction
- 结合多模型与患者特异性基因调控网络进行预测
- XGBoost AUROC达0.7051，GNN捕捉非线性调控关系
- 适合精准肿瘤学中转移风险评估的研究者使用
转移是癌症致死的主要原因，但现有预测模型多依赖浅层架构，忽视患者特异性调控机制。本文整合经典机器学习与深度学习方法，预测多种癌症的转移潜力。基于癌症细胞系百科全书的基因表达数据，结合DoRothEA提供的转录因子-靶标先验信息，聚焦9个与转移相关的调控因子。通过Kruskal-Wallis检验筛选差异表达基因后，使用ElasticNet、随机森林和XGBoost进行模型对比。进一步利用PANDA与LIONESS构建个性化基因调控网络，并通过图注意力神经网络（GATv2）学习拓扑与表达特征表示。尽管XGBoost取得最高AUROC（0.7051），GNN仍成功捕捉患者层面的非线性调控依赖关系。结果表明，传统机器学习与图神经网络结合可实现可扩展且可解释的转移风险预测框架。
原文摘要 · Abstract (English)
Metastasis is the leading cause of cancer-related mortality, yet most predictive models rely on shallow architectures and neglect patient-specific regulatory mechanisms. Here, we integrate classical machine learning and deep learning to predict metastatic potential across multiple cancer types. Gene expression profiles from the Cancer Cell Line Encyclopedia were combined with a transcription factor-target prior from DoRothEA, focusing on nine metastasis-associated regulators. After selecting differential genes using the Kruskal-Wallis test, ElasticNet, Random Forest, and XGBoost models were trained for benchmarking. Personalized gene regulatory networks were then constructed using PANDA and LIONESS and analyzed through a graph attention neural network (GATv2) to learn topological and expression-based representations. While XGBoost achieved the highest AUROC (0.7051), the GNN captured non-linear regulatory dependencies at the patient level. These results demonstrate that combining traditional machine learning with graph-based deep learning enables a scalable and interpretable framework for metastasis risk prediction in precision oncology.
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