Bioinformatics, machine learning, and functional validation reveal CCNA2 as a key target of Ammopiptanthus nanus against rheumatoid arthritis.

Rheumatoid arthritis (RA) is an autoimmune disease typically characterized by joint pain and dysfunction. Ammopiptanthus nanus (M. Pop.) Cheng f. (A. nanus), a traditional Kirgiz medicinal plant in Xinjiang, China, is widely used for treating rheumatic diseases. This study aimed to identify key protein targets in RA and discover potential anti-RA agents from A. nanus. To uncover key therapeutic targets, we analyzed gene expression datasets from RA synovial tissues using weighted gene co-expression network analysis. By integrating network pharmacology with machine learning approaches, we highlighted the central role of CCNA2, a key target elevated in RA patients, and elucidated its role in immune dysregulation in RA through immune infiltration profiling and single-cell transcriptomic analysis. Functional validation was subsequently conducted in TNF-α-stimulated MH7A cells. Following siRNA-mediated silencing of CCNA2, cell proliferation, migration, and invasion were significantly inhibited. Furthermore, silencing induced G2/M cell-cycle arrest and promoted apoptosis. To quantify compound-target interactions, molecular docking and molecular dynamics simulations were applied. These simulations revealed that abyssinone II and dihydrowighteone, two bioactive compounds from A. nanus, formed stable complexes with CCNA2. In summary, through a comprehensive methodology spanning bioinformatics, functional experiments, and computational simulations, this study identifies CCNA2 as a pivotal target in RA pathogenesis and demonstrates that abyssinone II and dihydrowighteone exhibit therapeutic potential by targeting synovial fibroblast activation via CCNA2 inhibition.