Uncovering active ingredients and mechanisms of Pholiota adiposa in the treatment of Alzheimer's disease based on network pharmacology and bioinformatics.

Pholiota adiposa is recognized for its health benefits, particularly in Alzheimer's disease (AD), but its molecular mechanism remains elusive. Our study employs network pharmacology and machine learning to uncover its therapeutic potential. We constructed a network of AD-relevant target proteins using databases like TCMSP, CTD, and GeneCards, and performed gene enrichment and functional analysis with DAVID, GO, and KEGG via Hiplot. Targets were identified through Cytoscape's degree analysis and the Aging Atlas database for aging-related genes. Clinical targets were confirmed with GEO, and molecular docking was executed using AutoDock Vina. LASSO regression and random forest algorithms were applied for target model selection, and molecular dynamics simulations were run with Gromacs2022.3. Our findings suggest Pholiota adiposa modulates key proteins involved in AD, including STAT3, PRKCA, NF-κB1, and CDK1, potentially inhibiting protein phosphorylation and influencing neuronal membrane synthesis and RNA polymerase II activity. KEGG analysis revealed its impact on cancer pathways, indicating a multifaceted role. High-degree targets like STAT3 and NF-κB1 were identified as effective, with TERT showing a significant role in aging. Machine learning confirmed the diagnostic importance of STAT3 and NFKB1 in AD. Molecular docking highlighted the affinity of Pholiota adiposa for these targets, with carnosol, carnosic acid, and clovane diol as key components. Carnosol, in particular, induced a conformational change in STAT3, enhancing its efficacy. Pholiota adiposa shows promise as a therapeutic agent in AD treatment by modulating various pathways and signaling mechanisms, as demonstrated through network pharmacology and machine learning analyses. This study underscores its potential in managing neurodegenerative diseases.