The Role Of Serum Protein Biomarkers In Stroke Patients In Predicting Functional Prognosis

Objective(1)Based on TMT proteomics research technology,looking for protein biomarkers related to the prognosis of stroke;(2)Exploring whether proteins that change early after stroke can be used as a predictive model for constructing stroke functional prognosis.Methods This study selected patients who were hospitalized in the Rehabilitation Medicine Department of the First Affiliated Hospital of Fujian Medical University from September 2018 to July 2020 and were diagnosed with stroke.The stroke diagnosis meets the Chinese diagnostic criteria for stroke and has undergone head CT or magnetization resonance scan confirmed.A total of 70 patients meet the inclusion and exclusion criteria were included.All patients in the group will take blood samples within one month of the onset of illness,and perform functional evaluation of the patients within 7 days of admission and 6 months after the onset of illness.The functional status of the patients after 6 months was evaluated by the m RS scale,and the patients were divided into two groups,m RS≤2divided into good prognosis,m RS>2 divided into poor prognosis.Adopting proteomics technology to detect the blood samples of these two groups of patients separately to find the different proteins,and perform protein cluster analysis,protein principal component analysis,Gene Ontology function annotation(GO),protein Pathview analysis,protein interaction on the obtained data(PPI)After processing such as network analysis,a model for predicting the functional prognosis of stroke is established.Results 1)Comparing the clinical baseline characteristics of the two groups of patients,the age of patients with poor prognosis(61.771±10.071)was significantly higher than the age of patients with good prognosis(55.029±10.910;P=0.009);the patients with poor prognosis were admitted to the hospital by Fugl-Meyer(12 [IQR 8-3];P=0.001)was significantly lower than that of patients with good prognosis(40[ IQR 20-61]).There was no significant difference in other baseline characteristics.After adjusting for potential confounding factors,the age of the two groups(P=0.003)and the Fugl-Meyer score at the time of entry(P=0.001)were significantly related to the outcome and prognosis.2)The two groups of samples were analyzed by proteomics technology,and a total of 22 differential proteins were obtained.Among them,the expression of 15 proteins were up-regulated and 7 proteins were down-regulated.The up-regulated proteins are glutathione peroxidase 3,vitamin D binding protein,CD44 antigen,cysteine-rich secretory protein 3,prothrombin,plasminogen,apolipoprotein A-II,alpha-2-HS-glycoprotein,apolipoprotein B-100,cholinesterase,complement C2,corticosteroid-binding globulin,plastin-2,properdin,apolipoprotein F.The down-regulated proteins include coagulation factor V,apolipoprotein L1,CD5 antigen-like,immunoglobulin J chain,Ig kappa chain Ⅴ-Ⅲ region B6,Ig mu chain C region,and mannose binding protein C.3)Through GO secondary annotation analysis,GO enrichment analysis,cluster analysis,KEGG pathway enrichment results,protein interaction network and other series of analyses on differential proteins,it was found that proteins related to prognostic differences involve 26 biological processes,mainly Including innate immune response,proteolysis,receptor-mediated endocytosis,classical pathway of complement activation,complement activation,leukocyte migration,lipoprotein metabolism process,etc.;13 cell components,mainly including endoplasmic reticulum lumen,extracellular space,extracellular exosomes,etc.;11 molecular functions mainly include serine-type endopeptidase activity,lipid transporter activity,cholesterol transporter activity,phosphatidylcholine binding,etc.4)The clinical baseline characteristic model AUC was 0.789(95%CI 68.4%-89.3%),the sensitivity was65.7%,the specificity was 65.7%,the differential protein model AUC was 0.864(95%CI 78.2%-94.5%),the sensitivity was 77.1%,The specificity was 74.3%,the clinical baseline characteristic model combined with the protein model AUC was0.910(95% CI 84.1%-98%),the sensitivity was 85.7%,and the specificity was 85.7%.The comparison shows that adding differential proteins to the clinical baseline characteristic model can improve the performance of the predictive model of stroke function prognosis.Conclusion The differential proteins between the two groups are mostly related to inflammation,cholesterol metabolism,complement and coagulation cascades,indicating that these processes are involved in the pathophysiological process of stroke.Adding significantly different proteins to the clinical baseline characteristic model can improve its sensitivity and specificity for predicting the prognosis of stroke.In order to better stratify the risk of stroke patients,further verification is needed in a larger patient population.