Identification and verification of genes associated with hypoxia microenvironment in Alzheimer’s disease

Abstract

As the incidence of Alzheimer's disease (AD) increases year by year, more people begin to study this disease. In recent years, many studies on reactive oxygen species (ROS), neuroinflammation, autophagy, and other fields have confirmed that hypoxia is closely related to AD. However, no researchers have used bioinformatics methods to study the relationship between AD and hypoxia. Therefore, our study aimed to screen the role of hypoxia-related genes in AD and clarify their diagnostic significance. A total of 7681 differentially expressed genes (DEGs) were identified in GSE33000 by differential expression analysis and cluster analysis. Weighted gene co-expression network analysis (WGCNA) was used to detect 9 modules and 205 hub genes with high correlation coefficients. Next, machine learning algorithms were applied to 205 hub genes and four key genes were selected. Through the verification of external dataset and quantitative real-time PCR (qRT-PCR), the AD diagnostic model was established by ANTXR2, BDNF and NFKBIA. The bioinformatics analysis results suggest that hypoxia-related genes may increase the risk of AD. However, more in-depth studies are still needed to investigate their association, this article would guide the insights and directions for further research.

Applying a Recurrent Neural Network-Based Deep Learning Model for Gene Expression Data Classification

“…Gene expression data processing in molecular biology uses both experimental and computational methods to analyze gene activity. The widely used qRT-PCR method quantifies specific gene expression levels and is excellent for validating high-throughput results [3]. Microarrays, which analyze thousands of genes simultaneously, have long been standard but are gradually being surpassed by RNA sequencing (RNA-Seq) due to its higher resolution and capability to detect novel transcripts.…”