Exploring potential biomarkers and lead molecules in gastric cancer by network biology, drug repurposing and virtual screening strategies

Gastric cancer poses a significant global health challenge, necessitating innovative approaches for biomarker discovery and therapeutic intervention. This study employs a multifaceted strategy integrating network biology, drug repurposing, and virtual screening to elucidate and expand the molecular landscape of gastric cancer. We identified and prioritized key genes implicated in gastric cancer by utilizing data from diverse databases and text-mining techniques. Network analysis underscored intricate gene interactions, emphasizing potential therapeutic targets such as CTNNB1, BCL2, TP53, etc, and highlighted ACTB among the top hub genes crucial in disease progression. Drug repurposing on 626 FDA-approved drugs for digestive system-related cancers revealed Norgestimate and Nimesulide as likely top candidates for gastric cancer, validated by molecular docking and dynamics simulations. Further, combinatorial synthesis of scaffold libraries derived from known chemotypes generated 56,160 virtual compounds, of which 76 new compounds were prioritized based on promising binding affinities and interactions at critical residues. Hotspot residue analysis identified GLU 214 and others as essential for ligand binding stability, enhancing compound efficacy and specificity. These findings support the therapeutic potential of targeting beta-actin protein in gastric cancer treatment, suggesting a future for further experimental validation and clinical translation. In conclusion, this study highlights the potential of repurposable drugs and virtual screening which can be used in combination with existing anti-gastric cancer drugs for gastric cancer therapy, emphasizing the role of computational methodologies in drug discovery.

This graphical abstract summarizes the multifaceted strategy employed to explore novel biomarkers and therapeutic targets for gastric cancer. Key genes implicated in gastric cancer were identified and prioritized through network biology. Network analysis highlighted crucial genes such as CTNNB1, BCL2, TP53, ACTB, etc. The repurposing of 626 FDA-approved drugs revealed top candidates, which were validated through molecular docking and dynamics simulations. Virtual screening generated 56,160 virtual compounds, with 76 new compounds prioritized based on binding affinities and interactions. These findings emphasize the potential of targeting beta-actin protein in gastric cancer therapy, paving the way for further experimental validation and clinical translation toward precision medicine.