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Updated: Jul 9, 2025
An Adoptive Transfer Model of Rheumatoid Arthritis in Mice
Published on: June 6, 2025
Machine learning and molecular subtype analyses provide insights into PANoptosis-associated genes in rheumatoid
1Department of Anesthesiology, Shanxi Provincial People's Hospital (Fifth Hospital) of Shanxi Medical University, Taiyuan, China.
Background:
PANoptosis represents a newly identified form of programmed cell death that plays a significant role in the autoimmune diseases. Rheumatoid arthritis (RA) is characterized by the presence of autoantibodies. Nevertheless, the specific biomarkers and molecular mechanisms responsible for the apoptotic characteristics of RA remain largely uninvestigated.
Methods:
We utilized 8 synovial tissue RA datasets. We selected genes associated with PANoptosis from the GeneCard database. By employing the limma, WGCNA, and machine learning algorithms we identified core genes. We utilized consensus clustering analysis to identify distinct PANoptosis subtypes of RA. Boruta algorithm was employed to construct a PANoptosis signature score. The sensitivity of distinct subtypes to drug treatment was verified using an independent dataset.
Results:
The SPP1 emerged as the significant gene, with its elevated expression in RA patients. We identified two PANoptosis RA subtypes. Cluster 1 showed high expression of Tregs, resting dendritic cells, and resting mast cells. Cluster 2 exhibited high expression of CD4 memory T cells and follicular helper T cells. Cluster 2 exhibited a higher degree of sensitivity towards immune checkpoint therapy. Employing the Boruta algorithm, a subtype score was devised for 37 PANoptosis genes, successfully discerning the subtypes (AUC = 0.794), wherein patients with elevated scores demonstrated enhanced responsiveness to Rituximab treatment.
Conclusion:
Our analysis revealed that SPP1 holds potential biomarker for the diagnosis of RA. Cluster 2 exhibited enhanced sensitivity to immune checkpoint therapy, higher PANoptosis scores, and improved responsiveness to drug treatment. This study offers potential implications in the realm of diagnosis and treatment.
Insights
Researchers identified SPP1 as a potential biomarker for rheumatoid arthritis (RA). Two PANoptosis subtypes were found, with one showing increased sensitivity to immune checkpoint therapy and Rituximab treatment.
Area of Science:
- Immunology
- Cell Death Research
- Rheumatology
Background:
- PANoptosis, a programmed cell death, is implicated in autoimmune diseases like rheumatoid arthritis (RA).
- Specific biomarkers and molecular mechanisms driving RA's apoptotic characteristics are largely unknown.
- RA is characterized by autoantibody presence, necessitating further investigation into its cellular underpinnings.
Purpose of the Study:
- To identify novel biomarkers for rheumatoid arthritis (RA) diagnosis.
- To elucidate the role of PANoptosis in RA pathogenesis and identify distinct RA subtypes.
- To assess the therapeutic sensitivity of identified RA subtypes.
Main Methods:
- Analysis of 8 synovial tissue RA datasets and GeneCard database for PANoptosis-associated genes.
- Application of limma, WGCNA, and machine learning algorithms to identify core genes.
- Consensus clustering for PANoptosis RA subtypes and Boruta algorithm for a PANoptosis signature score.
Main Results:
- SPP1 gene identified with elevated expression in RA patients, suggesting its potential as a biomarker.
- Two distinct PANoptosis RA subtypes identified: Cluster 1 (Tregs, dendritic cells, mast cells) and Cluster 2 (CD4 memory T cells, follicular helper T cells).
- Cluster 2 demonstrated higher sensitivity to immune checkpoint therapy and Rituximab, with a validated PANoptosis score (AUC=0.794).
Conclusions:
- SPP1 shows promise as a diagnostic biomarker for RA.
- Cluster 2, characterized by specific immune cell profiles and higher PANoptosis scores, indicates enhanced sensitivity to immune checkpoint therapy and Rituximab.
- Findings suggest potential advancements in RA diagnosis and personalized treatment strategies.
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