Identification and Immune Landscape Analysis of Fatty Acid Metabolism-related Genes Associated with Rheumatoid Arthritis
Zhongyang Zhou, Rong Luo, Lan Shao

TL;DR
This study identifies three key genes involved in fatty acid metabolism that are linked to rheumatoid arthritis and its inflammatory processes.
Contribution
The study introduces a novel approach combining immune infiltration analysis and machine learning to identify fatty acid metabolism-related hub genes in rheumatoid arthritis.
Findings
Three hub genes (PDK1, XBP1, and ACACB) were identified with diagnostic potential in rheumatoid arthritis.
These genes are associated with immune cell infiltration and show time-dependent expression patterns during disease progression.
TNFα and IL-6 treatments mimic the gene expression patterns observed in rheumatoid arthritis synovium.
Abstract
Background: Dysfunction of fatty acid metabolism plays a critical role in the pathogenesis of Rheumatoid Arthritis (RA). This study aimed to screen for Hub genes involved in fatty acid metabolism that contribute to the inflammatory state of RA synovium. Methods: Four mRNA microarray datasets for RA were integrated into an expression matrix as a test dataset. One RNA-seq and five microarray datasets were preprocessed as validation datasets. Immune cell infiltration combined with Weighted Gene Co-expression Network Analysis (WGCNA) were used to feature infiltrated cells and their correlation with candidate genes in RA. Five machine learning algorithms were applied to Hub genes screening. Temporal, immuno-efficacy, drug target prediction, molecular docking, ceRNA, and transcription factors networks analyses were conducted to elucidate the association of the Hub genes with RA.…
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Taxonomy
TopicsRheumatoid Arthritis Research and Therapies · Bioinformatics and Genomic Networks · Spondyloarthritis Studies and Treatments
