Machine learning reveals ferroptosis and therapeutic targets in rheumatoid arthritis.

A complex, multisystem, chronic autoimmune disease, rheumatoid arthritis (RA) severely impairs a patient's quality of life and finances. Though the precise chemical process is unclear, ferroptosis, a recently studied method of cell death, plays a significant role in RA. Furthermore, despite the fact that Chinese medicine, particularly the usage of Taohong Siwu Decoction (THSWD), has substantial benefits in treating RA, there are numerous shortcomings in the way RA is currently treated. In order to determine the primary acting components of THSWD and the primary acting targets that influence RA Ferroptosis based on network pharmacology, we will use machine learning to identify the major targets and molecular processes of Ferroptosis in RA. The main components and underlying molecular mechanisms of THSWD treatment for RA were elucidated by molecular docking techniques and in vitro experiments. Ferroptosis marker genes were found to be PTGS2, SLC40A1, TF, TFRC, FTH1, GPX4, HSPB1, and NFE2L2 in RA in microarray data analysis. Furthermore, we used ATF3, EGR1, and KDM6B as the ferroptosis activator genes. Ferroptosis inhibitor genes included ZFP36, NR4A1, CDKN1A, ABRD4, and RRM2. The disease process of RA may be linked to intracellular iron-ion homeostasis, iron-ion transport, and oxidative stress response, according to bioinformatic study. KEGG bioprocesses indicate that ferroptosis, the HIF-1 signaling pathway, and the p53 signaling pathway are closely linked to the development of RA. Lastly, using network pharmacology and vitro experiment, we discovered that kaempferol, a crucial part of THSWD, controlled ferroptosis to enhance RA through TFRC, ZFP36, PTGS, CDKN1A, and ATF3. The multi-component, multi-target, and multi-pathway features of THSWD for RA were demonstrated by this research investigation, which further elucidated the mechanism of THSWD for RA treatment with fresh perspectives.