ArticleJournal of inflammation research2025
Integrated Multi-Level Investigation of Friend Leukemia Integration 1 Transcription Factor as a Novel Immune-Inflammatory Biomarker in Rheumatoid Arthritis: Bridging Bioinformatics, Clinical Cohorts, and Mechanistic Validation.
Article in Journal of inflammation research, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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1 citing paper in PubMed.
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Authors and funding
7 authors.
Funding
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Abstract
Introduction: Friend Leukemia Integration 1 Transcription Factor (FLI1) has attracted attention due to its involvement in rheumatoid arthritis (RA). Nevertheless, the precise mechanism through which FLI1 contributes to RA remains elusive. We investigated the potential role of FLI1 in RA through integrated bioinformatics, clinical experiments, and cellular experiments. Methods: Based on the GSE1919 and GSE12021 datasets, the Kyoto Encyclopedia of Genes and Genomes (KEGG) and Gene Ontology (GO) analyses identified Results: FLI1 was upregulated in RA. FLI1 exhibited positive correlations with CRP, ESR, CCP, RF, IL-6, IL-10, IL-8, and TNF-α. The combined detection of FLI1 with CRP, ESR, CCP, and RF demonstrated the highest efficacy in evaluating RA disease activity. The target genes most strongly associated with FLI1 were AGA, DCK, LRRC15, MAN2A1, and TES, all of which exhibited positive correlations with FLI1. The suppression of FLI1 expression led to a decreased expression of AGA, DCK, LRRC15, MAN2A1, and TES. Furthermore, the inhibition of FLI1 reduced the viability of RA synovial cells and promoted their apoptosis. Discussion: FLI 1 is upregulated in RA and can promote inflammation, increase RA synovial cell viability, or inhibit synovial cell apoptosis. This finding suggests that FLI 1 and its target genes may serve as novel therapeutic targets in RA. The present findings integrate bioinformatics and experimental approaches to advance our current understanding of RA and open new avenues for targeted therapies.
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