Journal of orthopaedic surgery and research · 2025
Cucurbitacin E inhibits M1 macrophage polarization and attenuates rheumatoid arthritis: a multi-omics analysis and experimental validation.
СтатьяВалидационное исследованиеНазвание и аннотация: Перевод в очереди
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Xiong Haolan, Cao Shenglu, Yang Lin, Hu Xiaofang, Wang Gang, Wang Fuming
Аннотация
BACKGROUND: Rheumatoid arthritis (RA) is a chronic autoimmune disease marked by joint inflammation and destruction. Current treatments often have side effects and resistance. Cucurbitacin E (CuE), a natural compound with anti-inflammatory properties, shows therapeutic potential but its role in RA is unclear. This study explores CuE's mechanisms in RA, focusing on M1 macrophage polarization.
METHODS: We used a multi-omics approach, integrating transcriptomics, single-cell sequencing, and network pharmacology. Key steps included immune infiltration analysis, WGCNA, machine learning-based biomarker discovery, and flow cytometry validation. Pseudotime trajectory and cell communication analyses were also employed.
RESULTS: Immune infiltration analysis revealed increased M1 macrophage infiltration in RA patients. WGCNA identified gene modules related to macrophage polarization. Machine learning identified five key biomarkers (CCR2, NFKB1, NT5E, PIK3R1, TYRO3). A diagnostic model based on these biomarkers achieved high accuracy (AUC = 0.94). Pseudotime and cell communication analyses suggested CuE may regulate M1 polarization and signaling networks. Flow cytometry showed high CuE concentrations inhibited M1 macrophage polarization.
CONCLUSION: CuE inhibits M1 macrophage polarization and related pathways, offering a promising RA treatment strategy. The identified biomarkers may serve as diagnostic and therapeutic targets. Future research should validate CuE's clinical effects.
METHODS: We used a multi-omics approach, integrating transcriptomics, single-cell sequencing, and network pharmacology. Key steps included immune infiltration analysis, WGCNA, machine learning-based biomarker discovery, and flow cytometry validation. Pseudotime trajectory and cell communication analyses were also employed.
RESULTS: Immune infiltration analysis revealed increased M1 macrophage infiltration in RA patients. WGCNA identified gene modules related to macrophage polarization. Machine learning identified five key biomarkers (CCR2, NFKB1, NT5E, PIK3R1, TYRO3). A diagnostic model based on these biomarkers achieved high accuracy (AUC = 0.94). Pseudotime and cell communication analyses suggested CuE may regulate M1 polarization and signaling networks. Flow cytometry showed high CuE concentrations inhibited M1 macrophage polarization.
CONCLUSION: CuE inhibits M1 macrophage polarization and related pathways, offering a promising RA treatment strategy. The identified biomarkers may serve as diagnostic and therapeutic targets. Future research should validate CuE's clinical effects.