Saha Sagarika, Bapat Sanket, Vijayasarathi Durairaj, Vyas Renu
MIT ADTU School of Bioengineering Sciences & Research, MIT Art, Design and Technology University, Pune, Maharashtra, India.
Mol Divers. 2025 Jun;29(3):2341-2366. doi: 10.1007/s11030-024-10995-6. Epub 2024 Sep 30.
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.
胃癌对全球健康构成重大挑战，因此需要创新方法来发现生物标志物和进行治疗干预。本研究采用了一种多方面的策略，整合网络生物学、药物再利用和虚拟筛选，以阐明和扩展胃癌的分子格局。我们利用来自不同数据库的数据和文本挖掘技术，识别并确定了与胃癌相关的关键基因的优先级。网络分析强调了复杂的基因相互作用，突出了潜在的治疗靶点，如CTNNB1、BCL2、TP53等，并在疾病进展中至关重要的顶级中心基因中突出了ACTB。对626种FDA批准的用于消化系统相关癌症的药物进行药物再利用研究，发现诺孕酯和尼美舒利可能是胃癌的顶级候选药物，并通过分子对接和动力学模拟得到验证。此外，从已知化学类型衍生的支架库的组合合成产生了56,160种虚拟化合物，其中76种新化合物根据其在关键残基处有前景的结合亲和力和相互作用被确定为优先选择。热点残基分析确定GLU 214等对配体结合稳定性至关重要，从而提高了化合物的疗效和特异性。这些发现支持了靶向β-肌动蛋白蛋白在胃癌治疗中的治疗潜力，为进一步的实验验证和临床转化指明了方向。总之，本研究突出了可再利用药物和虚拟筛选的潜力，它们可与现有的抗胃癌药物联合用于胃癌治疗，强调了计算方法在药物发现中的作用。
