Exploration of the Mechanism of Xinjiang Rosa laxa Retz.Fruit Extract on IgA Nephropathy by Bioinformatics and Machine Learning Methods

This study aimed to elucidate the core therapeutic targets and mechanisms of action of rosehip extract (FRLE) in IgA nephropathy (IgAN) by integrating bioinformatics, machine learning, and computational simulation techniques to simultaneously analyze the therapeutic effects of FRLE on IgAN. Through screening differentially expressed genes in IgAN patients and identifying potential target proteins of the core components of FRLE, 118 overlapping targets were ultimately determined as the gene set involved in the synergistic interaction between the two.. Through triple machine learning validation, FGR and LDHB were identified as core target proteins, with ellagic acid, rutin, and hyperoside as the core active components. In IgAN patients, LDHB was significantly downregulated, while FGR was significantly upregulated. Both demonstrated excellent diagnostic efficacy and were closely associated with the renal immune microenvironment. Molecular docking and molecular dynamics simulations confirmed that the core components could stably bind to FGR and LDHB. In summary, FRLE targets and modulates FGR and LDHB through its core components, intervening in inflammatory pathways and immune microenvironment imbalance, thereby providing a theoretical basis for subsequent experiments and clinical translation.