Unveiling key drivers of hepatocellular carcinoma: a synergistic approach with network pharmacology, machine...

Area of Science:

• Bioinformatics
• Computational Biology
• Pharmacology

Background:

• Hepatocellular carcinoma (HCC) is a leading cause of cancer mortality globally.
• Understanding the molecular mechanisms driving HCC is crucial for developing effective treatments.

Purpose of the Study:

• To identify key genes and pathways implicated in HCC pathogenesis using network pharmacology.
• To discover potential drug candidates targeting HCC through machine learning-based virtual screening.
• To validate drug-target interactions using molecular dynamics simulations.

Main Methods:

• Network pharmacology (NP) for identifying key HCC genes (AKT1, GSK3β) and pathways (PI3K-AKT, WNT signalling).
• Machine learning (ML) for virtual screening of potential inhibitors against AKT1 and GSK3β.
• Molecular docking and molecular dynamics (MD) simulations for assessing protein-ligand interactions and stability.

Main Results:

• NP identified AKT1 and GSK3β as critical enzymes in HCC, with PI3K-AKT and WNT signalling pathways being pivotal.
• ML screening identified specific inhibitors (e.g., CHEMBL2177361, CHEMBL3652546) for AKT1 and GSK3β.
• MD simulations confirmed stable interactions between identified inhibitors and target proteins, with significant binding free energies.

Conclusions:

• This study provides novel bioinformatics-driven insights into HCC pathogenesis.
• Identified drug candidates show promise as potential therapeutics for hepatocellular carcinoma.
• The integrated approach of NP, ML, and MD simulations offers a robust strategy for drug discovery in HCC.