Identification and experimental validation of ferroptosis-related gene SLC2A3 is involved in rheumatoid arthritis.

BACKGROUND

Ferroptosis, an iron-dependent manner of lipid peroxidative cell death, has recently been reported to be strongly associated with rheumatoid arthritis (RA). Targeted ferroptosis may be a potential treatment for RA.

METHODS

We combined bioinformatics analysis and machine learning algorithm to screen the characteristic gene of RA. Moreover, we used gene set enrichment analysis (GSEA) to investigate the biological function of feature gene and CIBERSORT algorithm to analyze the correlation between selected hub gene and immune cells. The CellMiner database was used to predict potential drugs for RA. Finally, it was further verified by in vitro cell experiment.

RESULTS

SLC2A3 was identified as an important potential biomarker based on bioinformatics methods and machine learning algorithms. SLC2A3 encodes the predominantly neuronal glucose transporter 3 (GLUT3). GSEA showed that SLC2A3 high-expression group was correlated with metabolic pathways. Immune cell infiltration analysis showed that SLC2A3 was positively correlated with activated mast cell expression. RSL3 is an activator of ferroptosis that binds to and inactivates GPX4, mediating ferroptosis regulated by GPX4. In our experiment, we treated synovial fibroblast-like cells of RA (RA-FLS) with RSL3 (Ferroptosis inducers) and found that RSL3 can downregulate SLC2A3 expression and induce ferroptosis in RA-FLS.

CONCLUSIONS

Our study identifies and validates ferroptosis-related gene SLC2A3 as a potential biomarker for the diagnosis and treatment of RA. It was also found that RSL3 can induce ferroptosis in RA-FLS via lead to the downregulation of SLC2A3.