Fig 1.
Schematic workflow for predicting the therapeutic potential of propranolol in HCC using network pharmacology, molecular docking, and molecular dynamics simulations.
Key steps include target identification, pathway enrichment analysis, expression profiling, and in silico validation of propranolol–target interactions.
Fig 2.
Network pharmacology identification of propranolol–HCC intersecting targets and their protein–protein interaction landscape.
(A) Potential propranolol targets and HCC-related targets, (B) Venn Diagram showing intersecting potential anti-HCC targets, (C) STRING Protein – Protein Interaction (PPI) network of potential 70 anti-HCC targets of propranolol.
Fig 3.
PPI-based hub and module analysis of predicted anti-HCC targets and validation of core target expression in LIHC.
(A) PPI network of the potential anti-HCC targets, (B) PPI network of the top 30 anti-HCC targets with a degree greater than the average (9.84). In both networks (A and B), the transition from yellow to red indicates a degree shift from low to high for each node. (C) Thirty anti-HCC core targets in the hub network ranked by DC > 9.84 (DC = Degree centrality). (D) MCODE network clusters within the PPI network of the core targets. (E) Expression of the top eight anti-HCC core targets in LIHC (red and grey boxes represent tumor and normal cells, respectively) (LIHC = Liver hepatocellular carcinoma).
Fig 4.
Top 10 GO enrichment analyses of the 64 core anti-HCC targets.
Fig 5.
Top 30 most significant KEGG pathways of anti-HCC core targets of propranolol.
Table 1.
Molecular docking results of propranolol, Sorafenib and Lenvatinib against the nine anti-HCC targets.
Fig 6.
Molecular docking results for (A) JAK2, (B) ERBB2, (C) EGFR, (D) CDK2, (E) PARP1, (F) CHEK1, (G) CDK4, and (H) SRC.
Fig 7.
Molecular dynamics simulation analysis of protein-ligand complexes for JAK2, ERBB2, EGFR, and CDK2 over 100 nanoseconds.
(A) Protein backbone RMSD indicating overall structural stability, (B) Ligand RMSD depicting ligand-binding stability, (C) Radius of gyration demonstrating protein compactness throughout the simulation, (D) Per-residue protein RMSF highlighting regions of flexibility, (E) Polar surface area showing solvent-exposed polar regions, and (F) Solvent-accessible surface area reflecting protein surface exposure.
Fig 8.
Interaction analysis of protein-ligand complexes highlighting key residue interactions over simulation trajectories.
Bar plots (A-D) depict the interaction fractions of various residues categorized by interaction types for JAK2, EGFR, CDK2, and ERBB2. Panels (E-H) illustrate detailed 2D interaction maps for representative ligand-binding for JAK2, EGFR, CDK2, and ERBB2, respectively.
Table 2.
Binding free energies for the propranolol-target complexes were calculated using MM-GBSA.
Fig 9.
Anti-HCC core targets of Propranolol mapped within the PI3K–AKT signaling pathway (Pathway ID: hsa04151) from KEGG pathways.
The PI3K–AKT cascade is annotated to highlight six key targets of Propranolol implicated in hepatocellular carcinoma (HCC): EGFR (RTK), ERBB2 (RTK), JAK2, CCND1, CDK4, and CDK2. Shown in magenta, these proteins govern upstream signaling and cell cycle progression. Their collective positioning within this oncogenic network illustrates Propranolol’s potential for multi-target interference and supports its therapeutic repurpose in HCC.