Unveiling key molecular mechanisms and therapeutic targets of lentinan for asthma: A novel computational-experimental approach

Abstract

Lentinan, a polysaccharide derived from Lentinula edodes, has garnered attention for its anti-inflammatory and immunomodulatory properties, yet its therapeutic mechanisms in asthma remain inadequately characterized. Our study employs a novel integrative pipeline combining network pharmacology, machine learning, Mendelian randomization (MR), molecular docking, and in vitro experimental validation to unravel lentinan's molecular mechanisms and therapeutic potential in asthma. Initial bioinformatics analyses identified seven hub genes through protein-protein interaction (PPI) network construction, which were further refined to four candidate targets using LASSO regression, random forest (RF), and support vector machine-recursive feature elimination (SVM-RFE). Subsequent MR analysis prioritized STAT3 and TP53 as key therapeutic targets in asthma. Molecular docking and dynamics simulations revealed strong binding affinities of lentinan to STAT3 and TP53, corroborating their functional relevance. Experimental validation using LPS-induced RAW264.7 macrophage cells demonstrated that lentinan significantly downregulated phosphorylation levels of STAT3 and TP53, inhibited macrophage activation, and suppressed TNF-α, IL-6, IL-1β secretion. Additionally, lentinan modulated the PI3K/Akt signaling pathway, further substantiating its anti-inflammatory effects. This comprehensive computational-experimental framework highlights lentinan's therapeutic potential in asthma by targeting critical inflammatory pathways, warranting further investigation into its clinical applications.