Deciphering tumor metabolites: emerging technologies shaping clinical implications.

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.

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Journal
PubMed
Published
2026-05-12
DOI
https://doi.org/10.1016/j.trecan.2026.04.004
Primary Topic
Metabolomics and Mass Spectrometry Studies
Type
article
Field-Weighted Citation Impact
0.00
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article

Deciphering tumor metabolites: emerging technologies shaping clinical implications.

Liang Huang, Pu Tian, Xi Zhu, Z M Shao et al.
PubMed
Metabolomics and Mass Spectrometry Studies
article

Deciphering tumor metabolites: emerging technologies shaping clinical implications.

Liang Huang, Pu Tian, Xi Zhu, Z M Shao, Yi Xiao
article en

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.

PubMed
Fudan University Shanghai Cancer Center (CN)
Industry, innovation and infrastructure
Openalex Percentile: Top 7%
Metabolomics and Mass Spectrometry Studies
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Deciphering tumor metabolites: emerging technologies shaping clinical implications. — Liang Huang, Pu Tian, et al. · PubMed (2026) | TGRS Research Map | TGRS