LGALS9, LAMP3, PRSS8 and AGRN Predict Hospitalisation Risk in COVID-19 Patients

How to cite: McLarnon, T.; McDaid, D.; Lynch, S. M.; Cooper, E.; McLaughlin, J.; McGilligan, V. E.; Watterson, S.; Shukla, P.; Zhang, S.-D.; Bucholc, M.; English, A.; Peace, A.; O'Kane, M.; Kelly, M.; Bhavsar, M.; Murray, E. K.; Gibson, D. S.; Walsh, C. P.; Bjourson, A. J.; Rai, T. S. LGALS9, LAMP3, PRSS8 and AGRN Predict Hospitalisation Risk in COVID-19 Patients. Preprints 2024, 2024081813. https://doi.org/10.20944/preprints202408.1813.v1 McLarnon, T.; McDaid, D.; Lynch, S. M.; Cooper, E.; McLaughlin, J.; McGilligan, V. E.; Watterson, S.; Shukla, P.; Zhang, S.-D.; Bucholc, M.; English, A.; Peace, A.; O'Kane, M.; Kelly, M.; Bhavsar, M.; Murray, E. K.; Gibson, D. S.; Walsh, C. P.; Bjourson, A. J.; Rai, T. S. LGALS9, LAMP3, PRSS8 and AGRN Predict Hospitalisation Risk in COVID-19 Patients. Preprints 2024, 2024081813. https://doi.org/10.20944/preprints202408.1813.v1

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MDPI and ACS Style

McLarnon, T.; McDaid, D.; Lynch, S. M.; Cooper, E.; McLaughlin, J.; McGilligan, V. E.; Watterson, S.; Shukla, P.; Zhang, S.-D.; Bucholc, M.; English, A.; Peace, A.; O'Kane, M.; Kelly, M.; Bhavsar, M.; Murray, E. K.; Gibson, D. S.; Walsh, C. P.; Bjourson, A. J.; Rai, T. S. LGALS9, LAMP3, PRSS8 and AGRN Predict Hospitalisation Risk in COVID-19 Patients. Preprints 2024, 2024081813. https://doi.org/10.20944/preprints202408.1813.v1

APA Style

McLarnon, T., McDaid, D., Lynch, S. M., Cooper, E., McLaughlin, J., McGilligan, V. E., Watterson, S., Shukla, P., Zhang, S. D., Bucholc, M., English, A., Peace, A., O'Kane, M., Kelly, M., Bhavsar, M., Murray, E. K., Gibson, D. S., Walsh, C. P., Bjourson, A. J., & Rai, T. S. (2024). LGALS9, LAMP3, PRSS8 and AGRN Predict Hospitalisation Risk in COVID-19 Patients. Preprints. https://doi.org/10.20944/preprints202408.1813.v1

Chicago/Turabian Style

McLarnon, T., Anthony J. Bjourson and Taranjit Singh Rai. 2024 "LGALS9, LAMP3, PRSS8 and AGRN Predict Hospitalisation Risk in COVID-19 Patients" Preprints. https://doi.org/10.20944/preprints202408.1813.v1

Abstract

Background: The COVID-19 pandemic, caused by the novel coronavirus SARS-CoV-2 has posed unprecedented challenges to healthcare systems worldwide. Here, we have identified proteomic and genetic signatures for improved prognosis which is vital for COVID-19 research. Methods: We investigated the proteomic and genomic profile of COVID-19 positive patients (n=400 for proteomics, n=483 for genomics), focusing on differential regulation between hospitalised and non-hospitalised COVID-19 patients. Signatures had their predictive capabilities tested using independent machine learning models such as Support Vector Machine (SVM), Random Forest (RF) and Logistic Regression (LR). Results: This study has identified 224 differentially expressed proteins in hospitalised COVID-19 patients compared to non-hospitalised COVID-19 patients, involved in various inflammatory and immunological pathways. LGALS9 (p-value < 0.001), LAMP3 (p-value < 0.001), PRSS8 (p-value < 0.001) and AGRN (p-value < 0.001), were identified as the most statistically significant proteins. Several hundred rsIDs were queried across the top 10 significant signatures, identifying three significant SNPs on the FSTL3 gene showing correlation with hospitalisation status. Conclusion: Our study has not only identified key signatures of COVID-19 patients with worsened health but has also demonstrated their predictive capabilities as potential biomarkers, which suggests a staple role in the worsened health effects caused by COVID-19

Keywords

Sars-CoV-2, COVID-19, Biomarker, LGALS9, LAMP3, PRSS8, AGRN, Support Vector Machine, Logistic Regression, Random Forest

Subject

Biology and Life Sciences, Virology

Copyright: This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

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