Integrated Analysis, Machine Learning, Molecular Docking and Dynamics of CDK1 Inhibitors in Epithelial Ovarian...

Area of Science:

• Oncology
• Molecular Biology
• Computational Biology

Background:

• Epithelial ovarian cancer (EOC) is a leading cause of gynecologic cancer mortality.
• Late diagnosis and treatment resistance contribute to poor patient outcomes in EOC.
• Identifying novel therapeutic targets is crucial for improving EOC treatment strategies.

Purpose of the Study:

• To validate Cyclin-Dependent Kinase 1 (CDK1) as a high-confidence therapeutic target in EOC.
• To evaluate the dual-target inhibitory potential of Naringin against CDK1 and its regulator WEE1.
• To accelerate oncology drug discovery through computational-experimental integration.

Main Methods:

• Integrative pipeline combining transcriptomic profiling, protein-protein interaction network analysis, and machine learning.
• Identification of key oncogenic regulators in EOC.
• Molecular docking and dynamics simulations to assess compound binding efficacy.

Main Results:

• CDK1 was identified as a central hub gene in EOC, strongly associated with poor prognosis and signaling convergence.
• CDK1 overexpression correlated with adverse survival outcomes and involvement in critical oncogenic pathways.
• Naringin demonstrated high-affinity binding to CDK1 and WEE1, with stable complex formation and low predicted toxicity.

Conclusions:

• CDK1 is a validated therapeutic target for epithelial ovarian cancer.
• Naringin exhibits potential as a dual inhibitor of CDK1 and WEE1 for EOC treatment.
• Computational-experimental approaches effectively accelerate the identification of novel cancer drug candidates.