ArticleScientific reports2025
Screening necroptosis genes influencing osteoarthritis development based on machine learning.
Article in Scientific reports, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
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Who cites it
1 citing paper in PubMed.
- Focus on necroptosis: its role in the pathogenesis and therapeutic potential of osteoarthritis.Frontiers in immunology · 2026Review
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Authors and funding
8 authors.
Funding
Abstract
Machine learning can be applied to identify key genes associated with osteoarthritis (OA). This study aimed to explore the differential expression of necroptosis-related genes (NRGs) during the progression of OA, identify key gene modules strongly linked to the onset of OA, and assess the role of CASP1 and its correlation with immune cell infiltration in OA. Gene expression profile data were obtained for OA and normal tissues: GSE55235 (10 OA and 10 normal synovial tissues) and GSE46750 (12 OA and 12 normal synovial tissues). Differential expression analysis identified 44 NRGs. Weighted gene co-expression network analysis revealed that the turquoise module, including 2037 genes, showed a strong correlation with OA. Gene Ontology and Kyoto Encyclopedia of Genes and Genomes enrichment analyses showed that these genes were predominantly involved in regulating the JNK cascade, cellular response to oxidative stress, and Toll-like receptor signalling pathways. The support vector machine model exhibited the highest predictive performance (area under the curve of 0.883). Additionally, CASP1 expression in OA tissues was considerably elevated compared to normal tissues and was strongly associated with immune cell infiltration. These findings deepen our understanding of the pathophysiological foundation of OA and identify possible molecular targets for creating innovative OA therapies.
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