Machine learning-based identification of CXCL13 and CXCL10 as key biomarkers in rheumatoid arthritis
6 affiliations
- 1Department of Biotechnology, Kalasalingam Academy of Research and Education, Virudhunagar, Tamil Nadu, India
- 2Department of Computer Science and Engineering, School of Computing, SRM Institute of Science and Technology, Tiruchirappalli, Tamil Nadu, India
- 3Department of Electrical/Electronics and Computer Engineering, Afe Babalola University, Ado-Ekiti, Nigeria
- 4Saveetha School of Engineering, Saveetha Institute of Medical and Technical Sciences, Chennai, India
- 5Irfan Suat Gunsel Operational Research Institute, Near East University, Nicosia/TRNC, Türkiye
- 6Department of Pharmacy, Arab International University, Damascus, Syria
Abstract
Background: Rheumatoid arthritis (RA) is one of the autoimmune diseases that involve joint damage and synovial membrane inflammation. There have been many attempts to examine the utility of biomarkers in the diagnosis of RA, but there is no clinically accepted biomarker for the diagnosis of RA due to its heterogeneity. Thus, in this study, we tried to find RA biomarkers using machine learning and bioinformatics approaches.
Methods: Three corresponding gene expression datasets (GSE55457, GSE12021, and GSE77298) were downloaded from the Gene Expression Omnibus database. Batch effect correction was carried out using the ComBat function of the sva package in R. Differentially expressed genes (DEGs) compared to controls were identified using limma. To identify the biological functions and pathways of DEGs, Gene Ontology, KEGG, and gene set enrichment analysis (GSEA) were conducted. The protein–protein interaction (PPI) network was constructed in Cytoscape using the STRING database. Genes identified through PPI analysis and further filtered by LASSO and SVM-RFE regressions were considered as diagnostic biomarkers. Additional analysis of hub genes was conducted using the GSE89408 dataset. CIBERSORTx was used to evaluate the immune cell infiltration in RA.
Results: A total of 440 DEGs were selected, including 197 downregulated and 243 upregulated genes. The DEGs were involved in biological processes and pathways associated with the immune system and were suggested by functional enrichment analysis. GSEA enriched in cytokine–cytokine receptor interaction, hematopoietic cell lineage, primary immunodeficiency, and RA pathways. The hub genes CXCL13 and CXCL10 were chosen with the help of a machine learning algorithm. An external independent validation of the hub genes was conducted. In addition, ROC analysis showed a good diagnostic value of the two genes. The analysis of immune cell infiltration showed CXCL13 was associated with multiple immune-cell populations, including plasma cells, T-cell subsets, and M1 macrophages, whereas CXCL10 showed significant associations with M1 macrophages, plasma cells, and selected T-cell subsets. Correlation analysis showed a high correlation between chemokines CXCL13 and CXCL10 and many infiltrated immune cells.
Conclusion: The present study indicates the potential usefulness of CXCL13 and CXCL10 genes as diagnostic biomarkers in RA. Synovial inflammation in RA is associated with the coordinated action of many genes involved in the recruitment of various immune cells to the inflammatory site. Although the term chemokines were used to designate them, these genes are the genes encoding molecules associated with the migration of particular immune cell types, and CXCR5 and CXCR3 may be considered the potential targets for future research.
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BibTeX
@article{subash2026machine,
title = {{Machine learning-based identification of CXCL13 and CXCL10 as key biomarkers in rheumatoid arthritis}},
author = {Subash M and Sheik Asraf S and Santhanamari M and Nagaraj P and Ayodeji Olalekan Salau and Abdullahi Garba Usman and Mouaz Yousfan},
journal = {Frontiers in Bioinformatics},
year = {2026},
month = oct,
doi = {10.3389/fbinf.2026.1925964},
url = {https://doi.org/10.3389/fbinf.2026.1925964},
note = {CC BY 4.0},
}
RIS
TY - JOUR
TI - Machine learning-based identification of CXCL13 and CXCL10 as key biomarkers in rheumatoid arthritis
AU - Subash M
AU - Sheik Asraf S
AU - Santhanamari M
AU - Nagaraj P
AU - Ayodeji Olalekan Salau
AU - Abdullahi Garba Usman
AU - Mouaz Yousfan
PY - 2026
DA - 2026/10/12
JO - Frontiers in Bioinformatics
DO - 10.3389/fbinf.2026.1925964
UR - https://doi.org/10.3389/fbinf.2026.1925964
AB - Background: Rheumatoid arthritis (RA) is one of the autoimmune diseases that involve joint damage and synovial membrane inflammation. There have been many attempts to examine the utility of biomarkers in the diagnosis of RA, but there is no clinically accepted biomarker for the diagnosis of RA due to its heterogeneity. Thus, in this study, we tried to find RA biomarkers using machine learning and bioinformatics approaches. Methods: Three corresponding gene expression datasets (GSE55457, GSE12021, and GSE77298) were downloaded from the Gene Expression Omnibus database. Batch effect correction was carried out using the ComBat function of the sva package in R. Differentially expressed genes (DEGs) compared to controls were identified using limma. To identify the biological functions and pathways of DEGs, Gene Ontology, KEGG, and gene set enrichment analysis (GSEA) were conducted. The protein–protein interaction (PPI) network was constructed in Cytoscape using the STRING database. Genes identified through PPI analysis and further filtered by LASSO and SVM-RFE regressions were considered as diagnostic biomarkers. Additional analysis of hub genes was conducted using the GSE89408 dataset. CIBERSORTx was used to evaluate the immune cell infiltration in RA. Results: A total of 440 DEGs were selected, including 197 downregulated and 243 upregulated genes. The DEGs were involved in biological processes and pathways associated with the immune system and were suggested by functional enrichment analysis. GSEA enriched in cytokine–cytokine receptor interaction, hematopoietic cell lineage, primary immunodeficiency, and RA pathways. The hub genes CXCL13 and CXCL10 were chosen with the help of a machine learning algorithm. An external independent validation of the hub genes was conducted. In addition, ROC analysis showed a good diagnostic value of the two genes. The analysis of immune cell infiltration showed CXCL13 was associated with multiple immune-cell populations, including plasma cells, T-cell subsets, and M1 macrophages, whereas CXCL10 showed significant associations with M1 macrophages, plasma cells, and selected T-cell subsets. Correlation analysis showed a high correlation between chemokines CXCL13 and CXCL10 and many infiltrated immune cells. Conclusion: The present study indicates the potential usefulness of CXCL13 and CXCL10 genes as diagnostic biomarkers in RA. Synovial inflammation in RA is associated with the coordinated action of many genes involved in the recruitment of various immune cells to the inflammatory site. Although the term chemokines were used to designate them, these genes are the genes encoding molecules associated with the migration of particular immune cell types, and CXCR5 and CXCR3 may be considered the potential targets for future research.
SN - 2673-7647
ER -