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Published on: July 21, 2018
Deciphering tumor metabolites: emerging technologies shaping clinical implications
Xiao-Hui Zhu1, Liang Huang1, Pu Tian1
1Key Laboratory of Breast Cancer in Shanghai, Department of Breast Surgery, Fudan University Shanghai Cancer Center, Shanghai 200032, PR China; Department of Oncology, Shanghai Medical College, Fudan University, Shanghai, PR China.
Abstract:
Metabolites exert pleiotropic effects that govern tumor progression. This review evaluates current platforms and technologies for metabolite detection, alongside emerging strategies for functional research. These innovations have identified novel metabolites and revealed their previously unrecognized functions in tumor biology. Furthermore, the integration of advanced algorithms has facilitated the clinical application of metabolites in diagnosis, patient stratification, and therapeutic target discovery. However, critical limitations remain, particularly related to methodological constraints and tumor metabolic heterogeneity. We, therefore, outline a forward-looking vision underscoring the need for next-generation technologies and deep learning frameworks to enhance functional insights and clinical translation.

