Background and purposesRecent developments in high-throughput proteomic approach have shown the potential to discover biomarkers for diagnosing acute stroke and to elucidate the pathomechanisms specific to different stroke subtypes. We aimed to determine blood-based protein biomarkers to diagnose total stroke (IS+ICH) from healthy controls, ischemic stroke (IS) from healthy controls, and intracerebral hemorrhage (ICH) from healthy control subjects within 24 h using a discovery-based SWATH-MS proteomic approach.MethodsIn this discovery phase study, serum samples were collected within 24 h from acute stroke (IS & ICH) patients and healthy controls and were subjected to SWATH-MS-based untargeted proteomics. For protein identification, a high-pH fractionated peptide library for human serum proteins (obtained from SCIEX) comprising of 465 proteins was used. Significantly differentially expressed (SDE) proteins were selected using the following criteria: >1.5-fold change for upregulated, < 0.67 for downregulated, p-value < 0.05, and confirmed/tentative selection using Boruta random forest. Protein–protein interaction network analysis and the functional enrichment analysis were conducted using STRING 11 online tool, g:Profiler tool and Cytoscape 3.9.0 software. The statistical analyses were conducted in R version 3.6.2.ResultsOur study included 40 stroke cases (20 IS, 20 ICH) within 24 h and 40 age-, sex-, hypertension-, and diabetes-matched healthy controls. We quantified 375 proteins between the stroke cases and control groups through SWATH-MS analysis. We observed 31 SDE proteins between total stroke and controls, 16 SDE proteins between IS and controls, and 41 SDE proteins between ICH and controls within 24 h. Four proteins [ceruloplasmin, alpha-1-antitrypsin (SERPINA1), von Willebrand factor (vWF), and coagulation factor XIII B chain (F13B)] commonly differentiated total stroke, IS, and ICH from healthy control subjects. The most common significant pathways in stroke cases involved complement and coagulation cascades, platelet degranulation, immune-related processes, acute phase response, lipid-related processes, and pathways related to extracellular space and matrix.ConclusionOur discovery phase study identified potential protein biomarker candidates for the diagnosis of acute stroke and highlighted significant pathways associated with different stroke subtypes. These potential biomarker candidates warrant further validation in future studies with a large cohort of stroke patients to investigate their diagnostic performance.

Background and Objectives: Rapid diagnosis of stroke and its subtypes is critical in early stages. We aimed to discover and validate blood-based protein biomarkers to differentiate ischemic stroke (IS) from intracerebral hemorrhage (ICH) within 24 hours using high-throughput proteomics. Methods: We collected serum samples within 24 hours from acute stroke (IS & ICH) and mimics patients. In the discovery phase, SWATH-MS proteomics identified differentially expressed proteins (fold change: 1.5, p<0.05, and confirmed/tentative selection using Boruta random forest) between IS and ICH which were validated using Multiple Reaction Monitoring (MRM) proteomics in the validation phase. Protein-protein interactions and pathway analysis were conducted using STRING version 11 and Cytoscape 3.9.0. Cut-off points were determined using Youden Index. Prediction models were developed using backward stepwise multivariable logistic regression analysis. Hanley-McNeil test, Integrated discrimination improvement index, and likelihood ratio test determined the improved discrimination ability of biomarkers added to clinical models. Results: Discovery phase included 20 IS and 20 ICH while validation phase included 150 IS, 150 ICH, and 6 stroke mimics. We quantified 365 proteins in the discovery phase. Between IS and ICH, we identified 20 differentially expressed proteins. In the validation phase, combined prediction model including three biomarkers: GFAP (OR 0.04; 95%CI 0.02-0.11), MMP9 (OR 0.09; 95%CI 0.03-0.28), APO-C1 (OR 5.76; 95%CI 2.66-12.47) and clinical variables independently differentiated IS from ICH (accuracy: 92%, sensitivity: 96%, specificity: 69%). Addition of biomarkers to clinical variables improved the discrimination capacity by 26% (p<0.001). Subgroup analysis within 6 hours identified that GFAP and MMP9 differentiated IS from ICH with a sensitivity> 90%. Conclusions: Our study identified that GFAP, MMP, and APO-C1 biomarkers independently differentiated IS from ICH within 24 hours and significantly improved the discrimination ability to predict IS. Temporal profiling of these biomarkers in the acute phase of stroke is urgently warranted.

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