Integrative proteomic and machine learning strategies identify key biomarkers associated with response to JAK inhibitor in rheumatoid arthritis.
13 affiliations
- 1Department of Heavy Particles & Radiation Oncology, Taipei Veterans General Hospital, Taipei, Taiwan.
- 2Department of Biomedical Sciences, Chung Shan Medical University, Taichung, Taiwan.
- 3Department of Medical Research, Chung Shan Medical University Hospital, Taichung, Taiwan.
- 4Department of Medical Research, Taichung Veterans General Hospital, Taichung, Taiwan.
- 5Department of Biomedical Sciences and Engineering, Institute of Systems Biology and Bioinformatics, National Central University, Taoyuan, Taiwan.
- 6Division of Allergy, Immunology and Rheumatology, Department of Internal Medicine, Taichung Veterans General Hospital, Taichung, Taiwan.
- 7School of Medicine, National Yang Ming Chiao Tung University, Taipei, Taiwan.
- 8Department of Biomedical Sciences and Engineering, Institute of Systems Biology and Bioinformatics, National Central University, Taoyuan, Taiwan. [email protected].
- 9Department of Medical Research, Taichung Veterans General Hospital, Taichung, Taiwan. [email protected].
- 10Division of Allergy, Immunology and Rheumatology, Department of Internal Medicine, Taichung Veterans General Hospital, Taichung, Taiwan. [email protected].
- 11School of Medicine, National Yang Ming Chiao Tung University, Taipei, Taiwan. [email protected].
- 12Department of Post-Baccalaureate Medicine, College of Medicine, National Chung-Hsing University, Taichung, Taiwan. [email protected].
- 13Graduate Institute of Clinical Medicine, College of Medicine, National Chung Hsing University, Taichung, Taiwan. [email protected].
Abstract
Introduction
Janus kinase inhibitors (JAKi) have revolutionized the treatment of rheumatoid arthritis (RA), yet therapeutic responses vary widely across patients. Identifying proteomic biomarkers of response may enhance personalized treatment strategies and improve clinical outcomes.Materials and methods
We conducted a prospective study involving 42 RA patients treated with tofacitinib, baricitinib, or upadacitinib. Longitudinal serum proteomic profiling was performed at baseline and 6 months using the Olink® Explore 384 platform. Treatment response was defined by ACR/EULAR criteria. Biomarkers associated with good responders were identified through differential expression analysis and ensemble feature selection. Prediction models were developed using selected features, and feature importance was quantified using SHapley Additive exPlanations (SHAP).Results
Pretreatment levels of IL-12B, IL-17A, and IFN-γ were significantly elevated in JAKi non-responders. IL-17 signaling was associated with response to baricitinib and tofacitinib, while upadacitinib responders exhibited broader suppression of immune-related proteins including IL-4/IL-13 pathways. Prediction models using proteomic features outperformed those based on clinical variables alone (AUC ≥ 0.90 vs. 0.52 - 0.57), with further improvement upon integrating clinical and proteomic data (AUC = 0.95). Among all features, IL-12B emerged as the most robust and consistent predictor of treatment response, supported by both differential expression and multiple machine learning approaches.Conclusion
Serum proteomic profiling identifies elevated pretreatment IL-12B levels as a key predictor of JAKi non-response and uncovers distinct molecular responses to different JAK inhibitors. These findings support the utility of integrating proteomics with machine learning to advance personalized treatment strategies in RA.
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BibTeX
@article{chen2026integrative,
title = {{Integrative proteomic and machine learning strategies identify key biomarkers associated with response to JAK inhibitor in rheumatoid arthritis.}},
author = {Shuo-Fu Chen and Ting-Shuan Wu and Kuang-Yi Chang and Sheng-Min Lo and Yi-Ting Chen and Chung-Mao Kao and Yen-Ju Chen and Hui-Yin Chang and Yi-Ming Chen},
journal = {Arthritis research \& therapy},
year = {2026},
month = aug,
doi = {10.1186/s13075-026-03880-0},
url = {https://europepmc.org/article/MED/42958313},
note = {CC BY-NC-ND},
}
RIS
TY - JOUR
TI - Integrative proteomic and machine learning strategies identify key biomarkers associated with response to JAK inhibitor in rheumatoid arthritis.
AU - Shuo-Fu Chen
AU - Ting-Shuan Wu
AU - Kuang-Yi Chang
AU - Sheng-Min Lo
AU - Yi-Ting Chen
AU - Chung-Mao Kao
AU - Yen-Ju Chen
AU - Hui-Yin Chang
AU - Yi-Ming Chen
PY - 2026
DA - 2026/08/04
JO - Arthritis research & therapy
DO - 10.1186/s13075-026-03880-0
UR - https://europepmc.org/article/MED/42958313
AB - Introduction Janus kinase inhibitors (JAKi) have revolutionized the treatment of rheumatoid arthritis (RA), yet therapeutic responses vary widely across patients. Identifying proteomic biomarkers of response may enhance personalized treatment strategies and improve clinical outcomes.Materials and methods We conducted a prospective study involving 42 RA patients treated with tofacitinib, baricitinib, or upadacitinib. Longitudinal serum proteomic profiling was performed at baseline and 6 months using the Olink® Explore 384 platform. Treatment response was defined by ACR/EULAR criteria. Biomarkers associated with good responders were identified through differential expression analysis and ensemble feature selection. Prediction models were developed using selected features, and feature importance was quantified using SHapley Additive exPlanations (SHAP).Results Pretreatment levels of IL-12B, IL-17A, and IFN-γ were significantly elevated in JAKi non-responders. IL-17 signaling was associated with response to baricitinib and tofacitinib, while upadacitinib responders exhibited broader suppression of immune-related proteins including IL-4/IL-13 pathways. Prediction models using proteomic features outperformed those based on clinical variables alone (AUC ≥ 0.90 vs. 0.52 - 0.57), with further improvement upon integrating clinical and proteomic data (AUC = 0.95). Among all features, IL-12B emerged as the most robust and consistent predictor of treatment response, supported by both differential expression and multiple machine learning approaches.Conclusion Serum proteomic profiling identifies elevated pretreatment IL-12B levels as a key predictor of JAKi non-response and uncovers distinct molecular responses to different JAK inhibitors. These findings support the utility of integrating proteomics with machine learning to advance personalized treatment strategies in RA.
SN - 1478-6354
SN - 1478-6362
ER -