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Updated: Jul 2, 2025
Author Spotlight: Advancing the Analysis of Plasma Extracellular Vesicle Proteome for Cardiovascular Biomarker Studies
Published on: January 31, 2025
Proteome analysis develops novel plasma proteins classifier in predicting the mortality of COVID-19
Yifei Zeng1, Yufan Li2, Wanying Zhang1
1Department of Infectious Diseases, Second Affiliated Hospital of Zhejiang University School of Medicine, Hangzhou, China.
Insights
Researchers identified key plasma proteins to predict COVID-19 mortality. This novel protein classifier aids in early identification of high-risk patients, improving COVID-19 outcome prediction.
Area of Science:
- Biochemistry
- Proteomics
- Machine Learning
Background:
- COVID-19 remains a global health concern with unclear plasma protein dynamics.
- Understanding protein changes is crucial for predicting disease outcomes.
Purpose of the Study:
- To identify plasma protein changes associated with COVID-19 mortality.
- To develop a machine learning model for predicting 28-day mortality in COVID-19 patients.
Main Methods:
- Proteomic analysis of plasma samples from COVID-19 patients.
- Machine learning algorithms to build a predictive protein classifier.
- Validation of the classifier using an independent patient cohort.
Main Results:
- A classifier comprising C-reactive protein, extracellular matrix protein 1, IGFBP complex acid labile subunit, HECW1, and PC-STASE was determined.
- The model achieved AUCs of 0.88 (discovery) and 0.80 (validation) for predicting 28-day mortality.
- The developed classifier outperformed existing scoring systems like 4C mortality and CURB65.
Conclusions:
- A novel protein classifier can accurately predict early 28-day mortality in COVID-19 patients.
- This classifier offers a promising tool for identifying high-risk individuals.
- The findings provide new diagnostic avenues for managing COVID-19 patients.
Abstract:
COVID-19 has been a global concern for 3 years, however, consecutive plasma protein changes in the disease course are currently unclear. Setting the mortality within 28 days of admission as the main clinical outcome, plasma samples were collected from patients in discovery and independent validation groups at different time points during the disease course. The whole patients were divided into death and survival groups according to their clinical outcomes. Proteomics and pathway/network analyses were used to find the differentially expressed proteins and pathways. Then, we used machine learning to develop a protein classifier which can predict the clinical outcomes of the patients with COVID-19 and help identify the high-risk patients. Finally, a classifier including C-reactive protein, extracellular matrix protein 1, insulin-like growth factor-binding protein complex acid labile subunit, E3 ubiquitin-protein ligase HECW1 and phosphatidylcholine-sterol acyltransferase was determined. The prediction value of the model was verified with an independent patient cohort. This novel model can realize early prediction of 28-day mortality of patients with COVID-19, with the area under curve 0.88 in discovery group and 0.80 in validation group, superior to 4C mortality and E-CURB65 scores. In total, this work revealed a potential protein classifier which can assist in predicting the outcomes of COVID-19 patients and providing new diagnostic directions.
