ArticleCombinatorial chemistry & high throughput screening2026
Network Pharmacology and Computational Study to Identify Active Components and Potential Targets of
Article in Combinatorial chemistry & high throughput screening, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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4 authors.
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Abstract
introduction
methodsActive compounds and disease targets of P. sibiricum were retrieved from the TCMSP and CTD databases. A PROTEIN-PROTEIN INTERACTION (PPI) network was constructed using the STRING database, and functional enrichment was performed with the clusterProfiler package. A compound-target-pathway network was developed in Cytoscape. Immune infiltration was assessed via CIBERSORT and ESTIMATE algorithms, while ligand-target binding was evaluated by molecular docking and 100-ns molecular dynamics (MD) simulations. In vitro experiments were performed to explore the expression and functions of the key genes.
resultsWe screened 9 active components, 87 putative targets, and 240 HCC-related genes. 20 overlapping targets were used to construct a PPI network. Network analysis identified baicalein and 4 core targets DISCUSSION: P. sibiricum, particularly through baicalein targeting FOS/MMP9/AKT1/ TP53/PTGS2, inhibited HCC development by modulating EMT/angiogenesis pathways and immune milieu. However, these findings required further verification.
conclusionBaicalein was identified as an active compound targeting 5 crucial genes to suppress HCC progression, uncovering a new anti-HCC mechanism of P. sibiricum.
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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the Socratic graph.