基于元图卷积网络的血浆蛋白质组学免疫状态评估
Immune status assessment based on plasma proteomics with meta graph convolutional networks.
作者信息
Zhang Min, Xu Nan, Cheng Qi, Ye Jing, Wu Shiwei, Liu Haoliang, Zhao Chengkui, Yu Lei, Feng Weixing
机构信息
College of Intelligent Systems Science and Engineering, Harbin Engineering University, Harbin, China.
Institute of Biomedical Engineering and Technology, Shanghai Engineering Research Center of Molecular Therapeutics and New Drug Development, School of Chemistry and Molecular Engineering, East China Normal University, Shanghai, China.
出版信息
BMC Genomics. 2025 Apr 10;26(1):360. doi: 10.1186/s12864-025-11537-6.
Plasma proteins, especially immune-related proteins, are vital for assessing immune health and predicting disease risks. Despite their significance, the link between these proteins and systemic immune function remains unclear. To bridge this gap, researchers developed ProMetaGCN, a model integrating meta-learning, graph convolutional networks, and protein-protein interaction (PPI) data to evaluate immune status via plasma proteomics. This framework identified 309 immune-related factors with associated biological functions and pathways. Using six machine learning methods, four algorithms (Random Forest, LightGBM, XGBoost, Lasso) were selected for immune profiling and aging analysis, revealing ADAMTS13, GDF15, and SERPINF2 as key biomarkers. Validation across two COVID-19 cohorts confirmed the model's robustness, showing immune status correlates with infection progression and recovery. Furthermore, the study proposed ImmuneAgeGap, a novel metric linking immune profiles to survival rates in non-small-cell lung cancer (NSCLC) patients. These insights advance personalized immune health strategies and disease prevention.
血浆蛋白，尤其是与免疫相关的蛋白，对于评估免疫健康和预测疾病风险至关重要。尽管它们具有重要意义，但这些蛋白与全身免疫功能之间的联系仍不清楚。为了弥补这一差距，研究人员开发了ProMetaGCN，这是一种整合了元学习、图卷积网络和蛋白质-蛋白质相互作用（PPI）数据的模型，通过血浆蛋白质组学来评估免疫状态。该框架识别出了309个具有相关生物学功能和途径的免疫相关因子。使用六种机器学习方法，选择了四种算法（随机森林、LightGBM、XGBoost、套索）进行免疫谱分析和衰老分析，揭示了ADAMTS13、GDF15和SERPINF2作为关键生物标志物。在两个COVID-19队列中的验证证实了该模型的稳健性，表明免疫状态与感染进展和恢复相关。此外，该研究提出了免疫年龄差距（ImmuneAgeGap），这是一种将免疫谱与非小细胞肺癌（NSCLC）患者生存率联系起来的新指标。这些见解推动了个性化免疫健康策略和疾病预防。
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