Plasma Proteomics Identify Biomarkers and Pathogenesis of COVID-19
Open accessAbstract
The coronavirus disease 2019 (COVID-19) pandemic is a global public health crisis. However, little is known about the pathogenesis and biomarkers of COVID-19. Here, we profiled host responses to COVID-19 by performing plasma proteomics of a cohort of COVID-19 patients, including non-survivors and survivors recovered from mild or severe symptoms, and uncovered numerous COVID-19-associated alterations of plasma proteins. We developed a machine-learning-based pipeline to identify 11 proteins as biomarkers and a set of biomarker combinations, which were validated by an independent cohort and accurately distinguished and predicted COVID-19 outcomes. Some of the biomarkers were further validated by enzyme-linked immunosorbent assay (ELISA) using a larger cohort. These markedly altered proteins, including the biomarkers, mediate pathophysiological pathways, such as immune or inflammatory responses, platelet degranulation and coagulation, and metabolism, that likely contribute to the pathogenesis. Our findings provide valuable knowledge about COVID-19 biomarkers and shed light on the pathogenesis and potential therapeutic targets of COVID-19.
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Severe COVID-19 is associated with elevated acute phase proteins including SAA1, SAA2, SAA4, SAP/APCS, and SERPINA3.
“the serum amyloids A-1 (SAA1), A-2 (SAA2), and A-4 (SAA4), the serum amyloid P-component (SAP/APCS), and alpha-1-antichymotrypsin (SERPINA3) were found to be elevated in severe COVID-19”
From COVID-19 and the human innate immune system (2021) · cites this paper as Shu et al., 2020
A machine-learning model using eleven plasma proteins was built to predict disease outcome.
“a machine learning-based classifier of eleven plasma proteins, including orosomucoid-1/alpha-1-acid glycoprotein-1 (ORM1), orosomucoid-1/alpha-1-acid glycoprotein-2 (ORM2), fetuin-B (FETUB), and cholesteryl ester transfer protein (CETP), was generated to determine and predict disease outcome (Shu et al., 2020).”
From COVID-19 and the human innate immune system (2021) · cites this paper as Shu et al., 2020
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Self-citation
- Authors
- Shu, T., Ning, W., Wu, D., Xu, J., Han, Q., Huang, M., Zou, X., Yang, Q., Yuan, Y., Bie, Y., Pan, S., Mu, J., Han, Y., Yang, X., Zhou, H., Li, R., Ren, Y., Chen, X., Yao, S., Qiu, Y., Zhang, D.-Y., Xue, Y., Shang, Y., & Zhou, X.
- Affiliations
- Chinese Academy of Sciences (CN)
- Huazhong University of Science and Technology (CN)
- Wuhan Union Hospital (CN)
- Jinyintan Hospital (CN)
- Corresponding author
- Qiu, Yang
- Zhang, Ding-Yu
- Xue, Yu
- Shang, You
- Zhou, Xi
- ORCID
- Venue
- Immunity
- Publisher
- Elsevier BV
- Published
- 2020-11
- Volume
- 53
- Issue
- 5
- Pages
- 1108-1122.e5
- ISSN
- 1074-7613 (ISSN-L)
- PMC
- PMC7574896
- OpenAlex
- W3094046234
- Cited by
- 337
- References
- 66
- Citations by year
- 2026: 18 · 2025: 31 · 2024: 76 · 2023: 61 · 2022: 75 · 2021: 69 · 2020: 5
- FWCI
- 6.88
- Type
- journal article
- Language
- en
- Open access
- bronze
- doi.org
- Licence
- Locations
- Immunity (publishedVersion)
- PubMed Central (publishedVersion)
- PubMed (publishedVersion)
- Funders
- National Natural Science Foundation of China
- National Science and Technology Major Project
- Chinese Academy of Sciences
- Topics
- Health Sciences › Medicine › Infectious Diseases › COVID-19 Clinical Research Studies
- Health Sciences › Medicine › Infectious Diseases › SARS-CoV-2 and COVID-19 Research
- Physical Sciences › Chemistry › Spectroscopy › Advanced Proteomics Techniques and Applications
- Keywords
- COVID-19 pandemic, SARS-CoV-2, plasma proteomics, biomarkers, host response, pathogenesis, machine learning, outcome prediction, immune response, inflammation, platelet secretion, coagulation, metabolic changes, therapeutic target
- MeSH terms
- Machine Learning, Betacoronavirus, COVID-19, SARS-CoV-2, Adult, Aged, Aged, 80 and over, Blood Proteins, Female, Humans, Male, Middle Aged, Plasma, Pneumonia, Viral, Reproducibility of Results, Biomarkers, Coronavirus Infections, Proteomics, Pandemics
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