Immune status assessment based on plasma proteomics with meta graph convolutional networks
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 in...
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| Format: | Article |
| Language: | English |
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BMC
2025-04-01
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| Series: | BMC Genomics |
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| Online Access: | https://doi.org/10.1186/s12864-025-11537-6 |
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| author | Min Zhang Nan Xu Qi Cheng Jing Ye Shiwei Wu Haoliang Liu Chengkui Zhao Lei Yu Weixing Feng |
| author_facet | Min Zhang Nan Xu Qi Cheng Jing Ye Shiwei Wu Haoliang Liu Chengkui Zhao Lei Yu Weixing Feng |
| author_sort | Min Zhang |
| collection | DOAJ |
| description | 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. |
| format | Article |
| id | doaj-art-b35be888fed24a0bbc0a5f11d819bf52 |
| institution | DOAJ |
| issn | 1471-2164 |
| language | English |
| publishDate | 2025-04-01 |
| publisher | BMC |
| record_format | Article |
| series | BMC Genomics |
| spelling | doaj-art-b35be888fed24a0bbc0a5f11d819bf522025-08-20T03:10:05ZengBMCBMC Genomics1471-21642025-04-0126111710.1186/s12864-025-11537-6Immune status assessment based on plasma proteomics with meta graph convolutional networksMin Zhang0Nan Xu1Qi Cheng2Jing Ye3Shiwei Wu4Haoliang Liu5Chengkui Zhao6Lei Yu7Weixing Feng8College of Intelligent Systems Science and Engineering, Harbin Engineering UniversityInstitute 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 UniversityCollege of Intelligent Systems Science and Engineering, Harbin Engineering UniversityInstitute 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 UniversityCollege of Intelligent Systems Science and Engineering, Harbin Engineering UniversityCollege of Intelligent Systems Science and Engineering, Harbin Engineering UniversityCollege of Intelligent Systems Science and Engineering, Harbin Engineering UniversityInstitute 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 UniversityCollege of Intelligent Systems Science and Engineering, Harbin Engineering UniversityAbstract 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.https://doi.org/10.1186/s12864-025-11537-6Meta-learning graph convolutional networkPlasma proteomicsImmune-related proteinsMachine learningImmune status score |
| spellingShingle | Min Zhang Nan Xu Qi Cheng Jing Ye Shiwei Wu Haoliang Liu Chengkui Zhao Lei Yu Weixing Feng Immune status assessment based on plasma proteomics with meta graph convolutional networks BMC Genomics Meta-learning graph convolutional network Plasma proteomics Immune-related proteins Machine learning Immune status score |
| title | Immune status assessment based on plasma proteomics with meta graph convolutional networks |
| title_full | Immune status assessment based on plasma proteomics with meta graph convolutional networks |
| title_fullStr | Immune status assessment based on plasma proteomics with meta graph convolutional networks |
| title_full_unstemmed | Immune status assessment based on plasma proteomics with meta graph convolutional networks |
| title_short | Immune status assessment based on plasma proteomics with meta graph convolutional networks |
| title_sort | immune status assessment based on plasma proteomics with meta graph convolutional networks |
| topic | Meta-learning graph convolutional network Plasma proteomics Immune-related proteins Machine learning Immune status score |
| url | https://doi.org/10.1186/s12864-025-11537-6 |
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