Khan Gyas, Hussain Md Sadique, Ahmad Sarfaraz, Alam Nawazish, Ali Md Sajid, Alam Prawez
Department of Pharmacology and Toxicology, College of Pharmacy, Jazan University, 45142, Jazan, Saudi Arabia.
Uttaranchal Institute of Pharmaceutical Sciences, Uttaranchal University, Prem Nagar, Dehradun, Uttarakhand, 248007, India.
Naunyn Schmiedebergs Arch Pharmacol. 2025 May 2. doi: 10.1007/s00210-025-04234-4.
Triple-negative breast cancer (TNBC) is an aggressive and heterogeneous variant of breast cancer distinguished by a lack of targeted therapies, posing significant challenges in diagnosis and treatment. Metabolomics, the comprehensive study of small compounds in biological systems, has been identified as an instrument for revealing the metabolic underpinnings of TNBC. This review highlights recent advancements in metabolomic approaches, such as mass spectrometry and nuclear magnetic resonance, which have identified metabolic vulnerabilities, resistance mechanisms, and potential therapeutic targets. Key findings include alterations in fatty acid, amino acid, and glutathione metabolism, along with hypoxia-driven metabolic reprogramming that contributes to disease progression. The combination of metabolomics with multi-omics techniques, supported by advanced computational methods such as machine learning, offers a pathway to overcome challenges in data standardization and biological complexity. Emerging strategies, including the use of artificial intelligence and multidimensional omics approaches, are paving the way for personalized medicine by enabling the discovery of novel biomarkers and targeted therapies. Despite these advances, significant hurdles remain, including the need for robust data standardization, validation of findings in diverse patient cohorts, and seamless integration with clinical workflows. By addressing these challenges, metabolomics has the potential to revolutionize TNBC management, offering tools for early detection, precision therapy, and improved patient outcomes. This review underscores the importance of interdisciplinary collaboration to translate metabolomic insights into actionable clinical applications.
三阴性乳腺癌(TNBC)是一种侵袭性且异质性的乳腺癌亚型,其特点是缺乏靶向治疗方法,这给诊断和治疗带来了重大挑战。代谢组学是对生物系统中的小分子化合物进行的全面研究,已被视为揭示TNBC代谢基础的一种手段。本综述重点介绍了代谢组学方法(如质谱和核磁共振)的最新进展,这些方法已确定了代谢弱点、耐药机制和潜在的治疗靶点。主要发现包括脂肪酸、氨基酸和谷胱甘肽代谢的改变,以及缺氧驱动的代谢重编程,这促进了疾病进展。代谢组学与多组学技术相结合,并得到机器学习等先进计算方法的支持,为克服数据标准化和生物复杂性方面的挑战提供了一条途径。包括使用人工智能和多维组学方法在内的新兴策略,通过发现新的生物标志物和靶向治疗方法,为个性化医疗铺平了道路。尽管取得了这些进展,但仍存在重大障碍,包括需要强大的数据标准化、在不同患者队列中验证研究结果以及与临床工作流程无缝整合。通过应对这些挑战,代谢组学有潜力彻底改变TNBC的管理方式,为早期检测、精准治疗和改善患者预后提供工具。本综述强调了跨学科合作将代谢组学见解转化为可操作的临床应用的重要性。