Systematic Analysis and Biomarker Study for Alzheimer’s Disease

Abstract

Revealing the relationship between dysfunctional genes in blood and brain tissues from patients with Alzheimer’s Disease (AD) will help us to understand the pathology of this disease. In this study, we conducted the first such large systematic analysis to identify differentially expressed genes (DEGs) in blood samples from 245 AD cases, 143 mild cognitive impairment (MCI) cases, and 182 healthy control subjects, and then compare these with DEGs in brain samples. We evaluated our findings using two independent AD blood datasets and performed a gene-based genome-wide association study to identify potential novel risk genes. We identified 789 and 998 DEGs common to both blood and brain of AD and MCI subjects respectively, over 77% of which had the same regulation directions across tissues and disease status, including the known ABCA7, and the novel TYK2 and TCIRG1. A machine learning classification model containing NDUFA1, MRPL51, and RPL36AL, implicating mitochondrial and ribosomal function, was discovered which discriminated between AD patients and controls with 85.9% of area under the curve and 78.1% accuracy (sensitivity = 77.6%, specificity = 78.9%). Moreover, our findings strongly suggest that mitochondrial dysfunction, NF-κB signalling and iNOS signalling are important dysregulated pathways in AD pathogenesis.

Prediction of Alzheimer’s disease using blood gene expression data

“…Considering both internal and external validations, this study showed that all DEGs and the DEGs with CFG could accurately identify AD. Among the previous studies listed in Table 1, a study by Li et al 20 classified AD patients most accurately in the ANM dataset. However, in their study 20 , it seems that both ANM1 and ANM2 datasets were used for selecting features (six genes).…”

“…Among the previous studies listed in Table 1, a study by Li et al 20 classified AD patients most accurately in the ANM dataset. However, in their study 20 , it seems that both ANM1 and ANM2 datasets were used for selecting features (six genes). After curating the six AD-related genes, they used them as input features for the SVM model, yielding AUCs of 0.86 and 0.873 when testing ANM1 and ANM2, respectively 20 .…”

“…The L1-LR needs λ, the tuning hyperparameter that controls the degree of the penalty, which is typically set as a value that gives the best performance via CV. However, the previous study showed that most weights were penalized to 0 after selecting λ by CV 20 . Therefore, we preliminarily applied 100 sequencing λ values ranging from 10 −3 to 10 −5 .…”

“…In Table 1, we list previously published blood expression-based studies for the identification of patients with AD, especially focusing on machine learning (ML)-based studies [15][16][17][18][19][20] . Detailed information about related works is presented in Supplementary File.…”

Deletion of the Capn1 Gene Results in Alterations in Signaling Pathways Related to Alzheimer’s Disease, Protein Quality Control and Synaptic Plasticity in Mouse Brain

“…While the majority of DEGs were down-regulated (270), a significant number (84) were upregulated (Supplementary Tables S4, S5). Among these several belong to the ribosome pathway, which is interesting since alterations of these genes might be involved in Alzheimer's disease (Li et al, 2018). CSEA also indicated that some of the DEGs were enriched in specific brain regions and/or cell types.…”