TY - JOUR
T1 - Potential Plasma Proteins (LGALS9, LAMP3, PRSS8 and AGRN) as Predictors of Hospitalisation Risk in COVID-19 Patients
AU - McLarnon, Thomas
AU - McDaid, Darren
AU - Lynch, Seodhna
AU - Cooper, Eamonn
AU - McLaughlin, Joseph
AU - McGilligan, Victoria E.
AU - Watterson, Steven
AU - Shukla, Priyank
AU - Zhang, Shu-Dong
AU - Bucholc, Magda
AU - English, Andrew
AU - Peace, Aaron
AU - O’Kane, Maurice
AU - Kelly, Martin
AU - Bhavsar, Manav
AU - Murray, Elaine
AU - Gibson, David
AU - Walsh, Colum P.
AU - Bjourson, Anthony John
AU - Rai, Taranjit Singh
PY - 2024/9/17
Y1 - 2024/9/17
N2 - 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 involved in various inflammatory and immunological pathways in hospitalised COVID-19 patients compared to non-hospitalised COVID-19 patients. 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 a correlation with hospitalisation status. Conclusions: 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.
AB - 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 involved in various inflammatory and immunological pathways in hospitalised COVID-19 patients compared to non-hospitalised COVID-19 patients. 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 a correlation with hospitalisation status. Conclusions: 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.
UR - http://dx.doi.org/10.3390/biom14091163
U2 - 10.3390/biom14091163
DO - 10.3390/biom14091163
M3 - Article
C2 - 39334929
SN - 2218-273X
VL - 14
JO - Biomolecules
JF - Biomolecules
IS - 9
M1 - 1163
ER -