Full Papers
MXRA5 is identified and validated as a key gene related to epithelial-mesenchymal transition in rheumatoid arthritis fibroblast-like synoviocytes
H. Lv1, Y. Li2, R. Wang3, Y. Gao4
- Department of Clinical Laboratory, The Second Affiliated Hospital of Shandong First Medical University, Tai an, Shandong, China.
- Department of Rehabilitation Medicine, The Second Affiliated Hospital of Shandong First Medical University, Tai an, Shandong, China.
- Department of Trauma Orthopaedics, The Second Affiliated Hospital of Shandong First Medical University, Tai an, Shandong, China. wrq2010158@163.com
- Department of Trauma Orthopaedics, The Second Affiliated Hospital of Shandong First Medical University, Tai an, Shandong, China. gygkys@163.com
CER19054
Full Papers
PMID: 41105461 [PubMed]
Received: 27/06/2025
Accepted : 11/09/2025
In Press: 16/10/2025
Abstract
OBJECTIVES:
The invasion of fibroblasts is a major cause of cartilage destruction in rheumatoid arthritis (RA). The epithelial-to-mesenchymal transition (EMT) is a key factor that enhances the proliferation, invasion and migration abilities of cells. This study aims to identify important EMT-related genes in the synovial cells of RA using single-cell analysis and various machine learning methods, followed by functional validation.
METHODS:
The RA-related single-cell dataset SDY 998 was employed to investigate the heterogeneity of EMT across different cell types using the AUCcell and AddModuleScore algorithms. By intersecting the high EMT-related genes with the differentially expressed genes (DEGs), we obtained the EMT-related DEGs for subsequent correlation analysis. We then used five machine learning algorithms: Xtreme Gradient Boosting (XGBoost), Boruta, Random Forest (RF), Least Absolute Shrinkage and Selection Operator (LASSO), and Support Vector Machine Recursive Feature Elimination (SVM-RFE) to identify the optimal feature genes in the bulk RNA datasets. To validate the accuracy of our analysis, we conducted functional experiments on MH7A cells.
RESULTS:
After analysing the scRNA-seq dataset with various algorithms, we found that fibroblast cells exhibited significantly high EMT activity. Fifty genes were identified as high EMT-related DEGs for subsequent analysis. The five machine learning algorithms revealed that MXRA5 and LRRC15 were significantly upregulated in RA fibroblast cells. Functional experiments confirmed that knockdown of MXRA5 significantly reduced the proliferation, migration, and invasion capabilities of the cells.
CONCLUSIONS:
MXRA5 was identified as a key gene related to EMT in fibroblast-like synoviocytes of RA patients. Knockdown of MXRA5 could suppress the proliferation, migration, and invasion capabilities of MH7A cells.
