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ORIGINAL RESEARCH article

Front. Cell. Infect. Microbiol.
Sec. Molecular Viral Pathogenesis
Volume 14 - 2024 | doi: 10.3389/fcimb.2024.1397297
This article is part of the Research Topic Exploring SARS-CoV-2 Inflammatory Responses and Potential Targets for Treatment View all articles

Predicting survival in patients with SARS-CoV-2 based on cytokines and soluble immune checkpoint regulators

Provisionally accepted
Nuri Lee Nuri Lee 1Kibum Jeon Kibum Jeon 2Min-Jeong Park Min-Jeong Park 1Wonkeun Song Wonkeun Song 1Seri Jeong Seri Jeong 1*
  • 1 Kangnam Sacred Heart Hospital, Seoul, Republic of Korea
  • 2 Hangang Sacred Heart Hospital, Hallym University, Seoul, Republic of Korea

The final, formatted version of the article will be published soon.

    Background: Coronavirus disease 2019 (COVID-19) has been widespread for over four years and has progressed to an endemic stage. Accordingly, the evaluation of host immunity in infected patients and the development of markers for prognostic prediction in the early stages have been emphasized. Soluble immune checkpoints (sICs), which regulate T cell activity, have been reported as promising biomarkers of viral infections.Methods: In this study, quantitative values of 17 sICs and 16 cytokines (CKs) were measured using the Luminex multiplex assay. A total of 148 serum samples from 100 patients with COVID-19 were collected and the levels were compared between survivors vs. non-survivors and pneumonic vs. nonpneumonic conditions groups. The impact of these markers on overall survival were analyzed using a machine learning algorithm. Results: sICs, including sCD27, sCD40, herpes virus entry mediator (sHVEM), T-cell immunoglobulin and mucin-domain containing-3 (sTIM-3), and Toll-like receptor 2 (sTLR-2) and CKs, including chemokine CC motif ligand 2 (CCL2), interleukin-6 (IL-6), IL-8, IL-10, IL-13, granulocyte-macrophage colony-stimulating factor (GM-CSF), and tumor necrosis factor-α (TNF-α), were statistically significantly increased in the non-survivors compared to those of in the survivors. IL-6 showed the highest area under the receiver-operating curve (0.844, 95% CI = 0.751-0.913) to discriminate non-survival, with a sensitivity of 78.9% and specificity of 82.4%. In Kaplan-Meier analysis, patients with procalcitonin over 0.25 ng/mL, C-reactive protein (CRP) over 41.0 mg/dL, neutrophil-to-lymphocyte ratio over 18.97, sCD27 over 3828.8 pg/mL, sCD40 over 1283.6 pg/mL, and IL-6 over 21.6 pg/mL showed poor survival (log-rank test). In the decision tree analysis, IL-6, sTIM-3, and sCD40 levels had a strong impact on survival. Moreover, IL-6, CD40, and CRP levels were important to predict the probability of 90-d mortality using the SHapley Additive exPlanations method.Conclusion: sICs and CKs, especially IL-6, sCD27, sCD40, and sTIM-3 are expected to be useful in predicting patient outcomes when used in combination with existing markers.

    Keywords: Immune checkpoint, Cytokines, SARS-CoV-2, machine learning, prognosis

    Received: 07 Mar 2024; Accepted: 31 Oct 2024.

    Copyright: © 2024 Lee, Jeon, Park, Song and Jeong. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

    * Correspondence: Seri Jeong, Kangnam Sacred Heart Hospital, Seoul, Republic of Korea

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