Immune status assessment based on plasma proteomics with meta graph convolutional networks
Min Zhang,
Nan Xu,
Qi Cheng,
Jing Ye,
Shiwei Wu,
Haoliang Liu,
Chengkui Zhao,
Lei Yu,
Weixing Feng
Affiliations
Min Zhang
College of Intelligent Systems Science and Engineering, Harbin Engineering University
Nan Xu
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
Qi Cheng
College of Intelligent Systems Science and Engineering, Harbin Engineering University
Jing Ye
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
Shiwei Wu
College of Intelligent Systems Science and Engineering, Harbin Engineering University
Haoliang Liu
College of Intelligent Systems Science and Engineering, Harbin Engineering University
Chengkui Zhao
College of Intelligent Systems Science and Engineering, Harbin Engineering University
Lei Yu
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
Weixing Feng
College of Intelligent Systems Science and Engineering, Harbin Engineering University
Abstract 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.
