Evidence map›Paper›PMID 40211143›Full record

ArticleBMC genomics2025

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

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in BMC genomics, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.

0numbers the graph read from it
0cells of the map it votes in
2citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

2 citing papers in PubMed.

  1. Article
  2. Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

9 authors.

Min ZhangCollege of Intelligent Systems Science and Engineering, Harbin Engineering University, Harbin, China.
Nan XuInstitute 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.
Qi ChengCollege of Intelligent Systems Science and Engineering, Harbin Engineering University, Harbin, China.
Jing YeInstitute 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.
Shiwei WuCollege of Intelligent Systems Science and Engineering, Harbin Engineering University, Harbin, China.
Haoliang LiuCollege of Intelligent Systems Science and Engineering, Harbin Engineering University, Harbin, China.
Chengkui ZhaoCollege of Intelligent Systems Science and Engineering, Harbin Engineering University, Harbin, China. zhaochengkui@hrbeu.edu.cn.
Lei YuInstitute 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. yulei@nbic.ecnu.edu.cn.
Weixing FengCollege of Intelligent Systems Science and Engineering, Harbin Engineering University, Harbin, China. fengweixing@hrbeu.edu.cn.

Funding

Natural Science Foundation of Heilongjiang Province of China LH2022F012the China National Natural Science Foundation 62172121the China National Natural Science Foundation 82073800
6 · The paper itself

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.

Indexed as

Blood ProteinsCOVID-19ProteomicsAlgorithmsBiomarkersCarcinoma, Non-Small-Cell LungHumansLung NeoplasmsMachine LearningProtein Interaction MapsSARS-CoV-2BiomarkersBlood ProteinsImmune-related proteinsImmune status scoreMachine learningMeta-learning graph convolutional networkPlasma proteomics

Identifiers

PMID40211143
PMCPMC11983875

What OpenQuestion holds

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LicenceCC BY-NC-ND
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.