Metabolic reprogramming by Syntenin-1 directs RA FLS and endothelial cell-mediated inflammation and angiogenesis

PTEN: an emerging target in rheumatoid arthritis?

Phosphatase and tensin homolog deleted on chromosome 10 (PTEN) is a critical tumor suppressor protein that regulates various biological processes such as cell proliferation, apoptosis, and inflammatory responses by controlling the phosphatidylinositol 3-kinase (PI3K)/protein kinase B (PI3K/AKT) signaling pathway. PTEN plays a crucial role in the pathogenesis of rheumatoid arthritis (RA). Loss of PTEN may contribute to survival, proliferation, and pro-inflammatory cytokine release of fibroblast-like synoviocytes (FLS). Also, persistent PI3K signaling increases myeloid cells' osteoclastic potential, enhancing localized bone destruction. Recent studies have shown that the expression of PTEN protein in the synovial lining of RA patients with aggressive FLS is minimal. Experimental upregulation of PTEN protein expression could reduce the damage caused by RA. Nonetheless, a complete comprehension of aberrant PTEN drives RA progression and its interactions with other crucial molecules remains elusive. This review is dedicated to promoting a thorough understanding of the signaling mechanisms of aberrant PTEN in RA and aims to furnish pertinent theoretical support for forthcoming endeavors in both basic and clinical research within this domain.

Cathepsin B serves as a potential prognostic biomarker and correlates with ferroptosis in rheumatoid arthritis

BACKGROUND

Rheumatoid arthritis (RA) is a long-term, systemic, and progressive autoimmune disorder. It has been established that ferroptosis, a type of iron-dependent lipid peroxidation cell death, is closely associated with RA. Fibroblast-like synoviocytes (FLS) are the main drivers of RA joint destruction, and they possess a high concentration of endoplasmic reticulum structure. Therefore, targeting ferroptosis and RA-FLS may be a potential treatment for RA.

METHODS

Four machine learning algorithms were utilized to detect the essential genes linked to RA, and an XGBoost model was created based on the identified genes. SHAP values were then used to visualize the factors that affect the development and progression of RA, and to analyze the importance of individual features in predicting the outcomes. Moreover, WGCNA and PPI were employed to identify the key genes related to RA, and CIBERSORT was used to analyze the correlation between the chosen genes and immune cells. Finally, the findings were validated through in vitro cell experiments, such as CCK-8 assay, lipid peroxidation assay, iron assay, GSH assay, and Western blot.

RESULTS

Bioinformatics and machine learning were employed to identify cathepsin B (CTSB) as a potential biomarker for RA. CTSB is highly expressed in RA patients and has been found to have a positive correlation with macrophages M2, neutrophils, and T cell follicular helper cells, and a negative correlation with CD8 T cells, monocytes, Tregs, and CD4 memory T cells. To investigate the effect of CTSB on RA-FLS from RA patients, the CTSB inhibitor CA-074Me was used and it was observed to reduce the proliferation and migration of RA-FLS, as indicated by the accumulation of lipid ROS and ferrous ions, and induce ferroptosis in RA-FLS.

CONCLUSIONS

This study identified CTSB, a gene associated with ferroptosis, as a potential biomarker for diagnosing and managing RA. Moreover, CA-074Me, a CTSB inhibitor, was observed to cause ferroptosis and reduce the migratory capacity of RA-FLS.