Ferroptosis-related biomarkers for Alzheimer’s disease: Identification by bioinformatic analysis in hippocampus

作者：B H Wang, Chenyang Fu, Yuanyuan Wei, Bonan Xu, Rongxing Yang, Chuanxiong Li, Meihua Qiu, Yong Yin, Dongdong Qin · 发表于：Frontiers in Cellular Neuroscience · 年份：2022 · DOI：10.3389/fncel.2022.1023947 · 被引用次数：51 · 研究领域：Ferroptosis and cancer prognosis、Clusterin in disease pathology、GDF15 and Related Biomarkers

Background Globally, Alzheimer’s Disease (AD) accounts for the majority of dementia, making it a public health concern. AD treatment is limited due to the limited understanding of its pathogenesis. Recently, more and more evidence shows that ferroptosis lead to cell death in the brain, especially in the regions of the brain related to dementia. Materials and methods Three microarray datasets (GSE5281, GSE9770, GSE28146) related to AD were downloaded from Gene Expression Omnibus (GEO) datasets. Ferroptosis-related genes were extracted from FerrDb database. Data sets were separated into two groups. GSE5281 and GSE9770 were used to identify ferroptosis-related genes, and GSE28146 was used to verify results. During these processes, protein–protein interaction (PPI), the Gene Ontology (GO), and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analyses were conducted. Finally, the differentiated values of ferroptosis-related genes were determined by receiver operator characteristic (ROC) monofactor analysis to judge their potential quality as biomarkers. Results Twenty-four ferroptosis-related genes were obtained. Using STRING ( https://cn.string-db.org/ ) and Cytoscape with CytoHubba, the top 10 genes ( RB1, AGPAT3, SESN2, KLHL24, ALOX15B, CA9, GDF15, DPP4, PRDX1, UBC, FTH1, ASNS, GOT1, PGD, ATG16L1, SLC3A2, DDIT3, RPL8, VDAC2, GLS2, MTOR, HSF1, AKR1C3, NCF2 ) were identified as target genes. GO analysis revealed that response to carboxylic acid catabolic process,...