Alzheimer Hastalığında serum proteomik analizleri ile yeni biyobelirteçlerin araştırılması

Abstract

Current diagnostic criteria set for Alzheimer’s Disease (AD) is not sufficient for accurate prediction of the pathology. The use of CSF and PET biomarkers increase the accuracy of diagnoses but they are limited for widespread use due to their invasive nature or their lack of availability. Detection of disease associated changes in peripheral blood may help to improve the diagnosis and better understand the pathophysiology of AD. The aim of this project is to identify serum proteomic profile in Alzheimer's Disease patients and to identify key proteins with a biomarker potential. Participants diagnosed with AD and healthy controls were included in the study. Detailed demographic and clinical information of the participants was collected. To detect small changes in protein concentrations, 14 most abundant proteins from sera were separated and discarded, and LC-MS/MS analysis was applied to the remaining sera. Protein identification and concentrations were determined using Progenesis QIP. PCA was applied to reach out the number of components explaining 95% variability. Subsequently, random forest analysis was used to elucidate the variables distinguishing AD and Control. Proteins having the highest discriminatory value after multiple iterations were selected and characterized through bioinformatic analysis. A total of 194 participants were included in the study (87 AD, 107 Control). Through proteomic analyses, 590 proteins were identified. The ideal number of components in PCA analysis was found to be 8. Due to the significant discriminatory effect of cognitive score in RF, two models one with the cognitive scores and the other without, was created. 1000 iterations were performed for each condition with different seeds, and the top 10 proteins for each iteration were determined. Protein lists were extracted for both conditions, and bioinformatic analyses of the lists were performed. Proteins were enriched in various functions such as the immune system, metabolic pathways, and cellular stress response. 12 proteins entered the iteration lists for both conditions. These proteins were further characterized and their relationship with demographic and clinical characteristics was evaluated. As a result of the study, proteins that may be important for the pathophysiology of AD were identified. The diagnostic performance of these proteins needs to be tested in cohorts characterized with biomarkers.