Integrating transcriptomic analysis to explore biomarkers and therapeutic targets for rheumatoid arthritis: a case-control and systems biology approach.
- 1School of Bio Sciences and Technology, Vellore Institute of Technology, Vellore, Tamil Nadu, India.
- 2Padmavathy Rheumatic Care Centre, Vellore, Tamil Nadu, India.
Abstract
Background
Rheumatoid Arthritis (RA) is a highly variable and unpredictable autoimmune disease, relying solely on physical symptoms often leads to delayed treatment and irreversible joint destruction. Therefore, identifying biomarkers in RA is necessary to bridge the gap between a patient's unique biological profile and successful clinical management. This study aims to identify distinct molecular signatures in RA patients by integrating systems biology and case-control methodologies.Methods
Transcriptomic datasets from various sources specifically synovial tissues, macrophages, blood, T-cells, natural killer T-cells, natural killer cells, neutrophils, and monocytes were analysed. Differentially Expressed Genes and functional enrichment pathways were identified, followed by Weighted Gene Co-expression Network Analysis to pinpoint hub genes. Machine learning models were used to extract consensus biomarkers. Two of the predicted biomarkers IL2RG) and Interferon-Gamma (IFNG) were further validated using an Enzyme-Linked Immunosorbent Assay (ELISA) in RA patient serum samples.Results
The analysis revealed that synovial tissues and synovial macrophages exhibited pronounced transcriptomic expression changes. Six consensus biomarkers such as IFNG, PTEN, IL2RG, CDKN2D, VEGFA, and CDKN1A were computationally prioritized in synovial macrophages. Among these, IL2RG and IFNG were selected for preliminary experimental validation by ELISA. IL2RG concentrations were significantly higher in RA patients (1.34 ± 0.11 ng/mL) than in healthy controls (0.83 ± 0.07 ng/mL), while IFNG levels showed only a marginal increase (1.91 ± 0.06 pg/mL) compared to controls (1.69 ± 0.06 pg/mL).Conclusion
By integrating multi-tissue transcriptomic profiling with machine learning, this study mapped critical molecular changes in RA and prioritized six consensus biomarkers within synovial macrophages. Experimental validation confirmed that IL2RG and IFNG are significantly elevated in RA patients compared to healthy controls. These findings offer a promising, minimally invasive molecular markers for diagnosis and personalized clinical management in RA.
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BibTeX
@article{aswini2026integrating,
title = {{Integrating transcriptomic analysis to explore biomarkers and therapeutic targets for rheumatoid arthritis: a case-control and systems biology approach.}},
author = {S Aswini and S Mohamed Muzammil and N Raja and S Asha Devi},
journal = {Frontiers in bioinformatics},
year = {2026},
month = oct,
doi = {10.3389/fbinf.2026.1891543},
url = {https://europepmc.org/article/MED/42898585},
note = {CC BY},
}
RIS
TY - JOUR
TI - Integrating transcriptomic analysis to explore biomarkers and therapeutic targets for rheumatoid arthritis: a case-control and systems biology approach.
AU - S Aswini
AU - S Mohamed Muzammil
AU - N Raja
AU - S Asha Devi
PY - 2026
DA - 2026/10/05
JO - Frontiers in bioinformatics
DO - 10.3389/fbinf.2026.1891543
UR - https://europepmc.org/article/MED/42898585
AB - Background Rheumatoid Arthritis (RA) is a highly variable and unpredictable autoimmune disease, relying solely on physical symptoms often leads to delayed treatment and irreversible joint destruction. Therefore, identifying biomarkers in RA is necessary to bridge the gap between a patient's unique biological profile and successful clinical management. This study aims to identify distinct molecular signatures in RA patients by integrating systems biology and case-control methodologies.Methods Transcriptomic datasets from various sources specifically synovial tissues, macrophages, blood, T-cells, natural killer T-cells, natural killer cells, neutrophils, and monocytes were analysed. Differentially Expressed Genes and functional enrichment pathways were identified, followed by Weighted Gene Co-expression Network Analysis to pinpoint hub genes. Machine learning models were used to extract consensus biomarkers. Two of the predicted biomarkers IL2RG) and Interferon-Gamma (IFNG) were further validated using an Enzyme-Linked Immunosorbent Assay (ELISA) in RA patient serum samples.Results The analysis revealed that synovial tissues and synovial macrophages exhibited pronounced transcriptomic expression changes. Six consensus biomarkers such as IFNG, PTEN, IL2RG, CDKN2D, VEGFA, and CDKN1A were computationally prioritized in synovial macrophages. Among these, IL2RG and IFNG were selected for preliminary experimental validation by ELISA. IL2RG concentrations were significantly higher in RA patients (1.34 ± 0.11 ng/mL) than in healthy controls (0.83 ± 0.07 ng/mL), while IFNG levels showed only a marginal increase (1.91 ± 0.06 pg/mL) compared to controls (1.69 ± 0.06 pg/mL).Conclusion By integrating multi-tissue transcriptomic profiling with machine learning, this study mapped critical molecular changes in RA and prioritized six consensus biomarkers within synovial macrophages. Experimental validation confirmed that IL2RG and IFNG are significantly elevated in RA patients compared to healthy controls. These findings offer a promising, minimally invasive molecular markers for diagnosis and personalized clinical management in RA.
SN - 2673-7647
ER -