Potential Plasma Proteins (LGALS9, LAMP3, PRSS8 and AGRN) as Predictors of Hospitalisation Risk in COVID-19 Patients

• Published in: Biomolecules (Basel, Switzerland) Vol. 14; no. 9; p. 1163
• Main Authors: McLarnon, Thomas, McDaid, Darren, Lynch, Seodhna M., Cooper, Eamonn, McLaughlin, Joseph, McGilligan, Victoria E., Watterson, Steven, Shukla, Priyank, Zhang, Shu-Dong, Bucholc, Magda, English, Andrew, Peace, Aaron, O’Kane, Maurice, Kelly, Martin, Bhavsar, Manav, Murray, Elaine K., Gibson, David S., Walsh, Colum P., Bjourson, Anthony J., Rai, Taranjit Singh
• Format: Journal Article
• Language: English
• Published: Switzerland MDPI AG 01.09.2024; MDPI
• Subjects: Adult; Aged; Automation; biomarker; Biomarkers; Biomarkers - blood; Blood proteins; Blood Proteins - genetics; Blood Proteins - metabolism; Coronaviruses; COVID-19; COVID-19 - epidemiology; COVID-19 - genetics; Cytokines; Ethylenediaminetetraacetic acid; Female; Galectins - genetics; Genomes; Genomics; Health aspects; Hospitalization; Humans; Infections; LAMP3; LGALS9; Linux; Lysosomal Membrane Proteins - genetics; Machine learning; Male; Middle Aged; Patients; Plasma; Plasma proteins; Prognosis; Proteins; Proteomics; Proteomics - methods; PRSS8; Quality control; Regression analysis; SARS-CoV-2; SARS-CoV-2 - isolation & purification; Severe acute respiratory syndrome coronavirus 2; Single-nucleotide polymorphism; Statistical analysis; United Kingdom; Massachusetts; Taiwan; United Kingdom > UK; United States > US; COVID-19; SARS-CoV-2; Logistic Regression; LAMP3; LGALS9; PRSS8; Support Vector Machine; AGRN; biomarker; Random Forest
• ISSN: 2218-273X; 2218-273X
• DOI: 10.3390/biom14091163

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.