Abstract
Alzheimer’s disease (AD), a nervous system disease, lacks effective therapies at present. RNA expression is the basic way to regulate life activities, and identifying related characteristics in AD patients may aid the exploration of AD pathogenesis and treatment. This study developed a classifier that could accurately classify AD patients and healthy people, and then obtained 3 core genes that may be related to the pathogenesis of AD. To this end, RNA expression data of the middle temporal gyrus of AD patients were firstly downloaded from GEO database, and the data were then normalized using limma package following a supplementation of missing data by k-Nearest Neighbor (KNN) algorithm. Afterwards, the top 500 genes of the most feature importance were obtained through Max-Relevance and Min-Redundancy (mRMR) analysis, and based on these genes, a series of AD classifiers were constructed through Support Vector Machine (SVM), Random Forest (RF), and KNN algorithms. Then, the KNN classifier with the highest Matthews correlation coefficient (MCC) value composed of 14 genes in incremental feature selection (IFS) analysis was identified as the best AD classifier. As analyzed, the 14 genes played a pivotal role in determination of AD and may be core genes associated with the pathogenesis of AD. Finally, protein-protein interaction (PPI) network and Random Walk with Restart (RWR) analysis were applied to obtain core gene-associated genes, and key pathways related to AD were further analyzed. Overall, this study contributed to a deeper understanding of AD pathogenesis and provided theoretical guidance for related research and experiments.
Highlights
Alzheimer’s disease (AD) is a progressive neurodegenerative disorder that is almost incurable
Expressed genes screened from the downloaded gene expression data in Gene Expression Omnibus (GEO) and feature genes in the k-Nearest Neighbor (KNN) classifier were intersected to obtain 3 core genes, including heat shock protein family B member 3 (HSPB3), adipocyte enhancer binding protein 1 (AEBP1), RNA U1 Small Nuclear 4 (RNU1G2) (Figure 4A)
HSPB3 was conspicuously down-regulated, while AEBP1 and RNU1G2 were notably up-regulated in the AD group. These results demonstrated that the 3 core genes were closely associated with the pathogenesis of AD, and were mainly related to cell functions involved in immunity and cell transportation
Summary
Alzheimer’s disease (AD) is a progressive neurodegenerative disorder that is almost incurable. According to the World Alzheimer Report 2018, there were approximately 50 million patients worldwide who suffered from AD, and AD became a major cause of death among old people (Patterson, 2018). A recent genetic study unearthed that Aβ deposition frequently occurs in people with ApoE4 (Genin et al, 2011). People with ApoE4 gene have high plasma cholesterol, which in turn stimulates the deposition of Aβ and tau proteins in the brain, thereby leading to AD (Greenberg et al, 2020). It is reported that the pathogenesis of AD is associated with heredity and gene expression like TREM2, PLCG2, ABI3
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