Machine learning and molecular subtype analyses provide insights into PANoptosis-associated genes in rheumatoid arthritis
Authors' 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.
Study context
- Population
- Synovial tissue rheumatoid arthritis (RA) datasets; PANoptosis-associated genes from the GeneCard database8 synovial tissue datasets
- Comparison
- PANoptosis subtypes of RA: Cluster 1 vs Cluster 2
- Outcome
- Core PANoptosis-associated genes and PANoptosis subtypes of RA in synovial tissue
Methodological detailsmachine learning and molecular subtype analysis, 8 synovial tissue RA datasets, limma, WGCNA and machine learning, consensus clustering, Boruta algorithm, independent validation dataset
- Machine learning and molecular subtype analysis
- 8 synovial tissue RA datasets
- Limma, WGCNA and machine learning
- Consensus clustering
- Boruta algorithm
- Independent validation dataset
Recommendations
As reported in the abstract
Two PANoptosis RA subtypes identified
2
Boruta subtype score for 37 PANoptosis genes discerning the subtypes, AUC
0.794
Original publication
Machine learning and molecular subtype analyses provide insights into PANoptosis-associated genes in rheumatoid arthritis. Arthritis Res Ther. Published 1 December 2023. doi:10.
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