Odongo Regan, Demiroglu-Zergeroglu Asuman, Çakır Tunahan
Department of Bioengineering, Faculty of Engineering, Gebze Technical University, Gebze, Kocaeli, 41400, Turkey.
Department of Molecular Biology & Genetics, Faculty of Science, Gebze Technical University, Gebze, Kocaeli, 41400, Turkey.
BioData Min. 2024 Feb 21;17(1):5. doi: 10.1186/s13040-024-00357-1.
Prioritizing candidate drugs based on genome-wide expression data is an emerging approach in systems pharmacology due to its holistic perspective for preclinical drug evaluation. In the current study, a network-based approach was proposed and applied to prioritize plant polyphenols and identify potential drug combinations in breast cancer. We focused on MEK5/ERK5 signalling pathway genes, a recently identified potential drug target in cancer with roles spanning major carcinogenesis processes.
By constructing and identifying perturbed protein-protein interaction networks for luminal A breast cancer, plant polyphenols and drugs from transcriptome data, we first demonstrated their systemic effects on the MEK5/ERK5 signalling pathway. Subsequently, we applied a pathway-specific network pharmacology pipeline to prioritize plant polyphenols and potential drug combinations for use in breast cancer. Our analysis prioritized genistein among plant polyphenols. Drug combination simulations predicted several FDA-approved drugs in breast cancer with well-established pharmacology as candidates for target network synergistic combination with genistein. This study also highlights the concept of target network enhancer drugs, with drugs previously not well characterised in breast cancer being prioritized for use in the MEK5/ERK5 pathway in breast cancer.
This study proposes a computational framework for drug prioritization and combination with the MEK5/ERK5 signaling pathway in breast cancer. The method is flexible and provides the scientific community with a robust method that can be applied to other complex diseases.
基于全基因组表达数据对候选药物进行优先级排序是系统药理学中一种新兴的方法，因为它为临床前药物评估提供了整体视角。在本研究中，我们提出了一种基于网络的方法，并将其应用于对植物多酚进行优先级排序以及识别乳腺癌中的潜在药物组合。我们聚焦于MEK5/ERK5信号通路基因，这是最近在癌症中确定的一个潜在药物靶点，其作用涉及主要的致癌过程。
通过构建和识别来自转录组数据的腔面A型乳腺癌、植物多酚和药物的扰动蛋白质-蛋白质相互作用网络，我们首先证明了它们对MEK5/ERK5信号通路的系统性影响。随后，我们应用了一种特定于通路的网络药理学流程来对用于乳腺癌的植物多酚和潜在药物组合进行优先级排序。我们的分析将染料木黄酮列为植物多酚中的优先选择。药物组合模拟预测了几种在乳腺癌中已确立药理学特性的FDA批准药物，作为与染料木黄酮进行靶点网络协同组合的候选药物。本研究还强调了靶点网络增强剂药物的概念，将以前在乳腺癌中未得到充分表征的药物列为优先用于乳腺癌MEK5/ERK5通路的药物。
本研究提出了一种用于乳腺癌中基于MEK5/ERK5信号通路的药物优先级排序和组合的计算框架。该方法具有灵活性，为科学界提供了一种可应用于其他复杂疾病的强大方法。
