Identifying Protein Biomarkers in Blood for Alzheimer's Disease - Zhao, Tianyi; Hu, Yang; Zang, Tianyi; Wang, Yadong (Frontiers in Cell and Developmental Biology, 2020)
Background: At present, the main diagnostic methods for Alzheimer's disease (AD) are positron emission tomography (PET) scanning of the brain and analysis of cerebrospinal fluid (CSF) sample, but these methods are expensive and harmful to patients. Recently, more researchers focus on diagnosing AD by detecting biomarkers in blood, which is a cheaper and harmless way. Therefore, identifying AD-related proteins in blood can help treatment and diagnosis. Methods: We proposed a hypothesis that similar diseases share similar proteins. Diseases with similar symptoms are caused by abnormalities of similar proteins. Assuming that the similarities between AD and other diseases obey the normal distribution, we constructed an object function to map the weight of proteins to diseases’ similarities. We designed an iterative method and combined Elastic Network (EN) with Minimum angle regression (MAR) to find the optimal solution. Finally, we used case studies and Summary data Mendelian Random (SMR) to verify our method. Results: We selected 39 diseases which are highly re-lated to AD. They correspond 1481 kinds of proteins. 184 proteins are reported to be related to AD in Uniprot and the number would be 284 with our method. The AUC of our method by cross-validation is 0.9251 which is much higher than previous methods. Conclusion: In this paper, we presented a novel method for prioritizing AD-related proteins. 7 proteins have tissue specificity in blood among these 284 proteins, which could be used to diagnose AD in future. Case studies and SMR have been used to prove the relationship between these 7 proteins and AD.