Identification of potential ferroptosis-associated biomarkers in rheumatoid arthritis

作者：Xu He, Juqi Zhang, Mingli Gong, Yanlun Gu, Bingqi Dong, Xiaocong Pang, Chenglong Zhang, Yimin Cui · 发表于：Frontiers in Immunology · 年份：2023 · DOI：10.3389/fimmu.2023.1197275 · 被引用次数：33 · 研究领域：Ferroptosis and cancer prognosis、GDF15 and Related Biomarkers、Macrophage Migration Inhibitory Factor

Background: Rheumatoid arthritis (RA) is a chronic autoimmune disorder characterized by inflammation and gradual joint degeneration, resulting in function disability. Recently, ferroptosis, a novel form of regulated cell death that involves iron-dependent lipid peroxidation, has been implicated in the pathogenesis of RA. However, the underlying molecular mechanisms and key genes involved in ferroptosis in RA remain largely unknown. Methods: The GSE134420 and GSE77298 datasets were downloaded and DEGs were identified using R software. The DEGs were then mapped to the dataset of 619 ferroptosis-related genes obtained from the GeneCards database. Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analyses were conducted to investigate the possible biological functions. Protein-protein interaction (PPI) networks were constructed to identify the hub genes. The relationship between hub genes and immune infiltration was estimated using the CIBERSORT algorithms. Gene Set Enrichment Analysis (GSEA) was used to explore the underlying signaling pathways of hub genes. Genome-wide association studies (GWAS) analysis was performed to confirm the pathogenic regions of the hub genes. RcisTarget and Gene-motif ranking databases were used to identify transcription factors (TFs) associated with the hub genes. The miRcode databases were utilized to construct the microRNA (miRNA)-messenger RNA (mRNA) network. Single-cell analysis was utilized to cluster cells and displa...