Lei Yin
Cheng Kong
Chunmiao Cai
Hongqi Teng
Zhengyan Chang- 1Department of Urology, Shanghai Ninth People’s Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China
- 2Department of Colorectal Surgery, Fudan University Shanghai Cancer Center, Shanghai, China
- 3Department of Medicine, Division of Gastroenterology and Hepatology, Mayo Clinic, Rochester, MN, United States
- 4Department of Experimental Radiation Oncology, University of Texas MD Anderson Cancer Center, Houston, TX, United States
- 5Department of Pathology, Shanghai 10th People’s Hospital, Tongji University, Shanghai, China
Editorial on the Research Topic
Challenges and opportunities in tumor metabolomics
Introduction
Tumor metabolomics has emerged as a pivotal discipline at the intersection of cancer biology, systems medicine, and translational oncology (Trefny et al., 2025). Beyond genetic and epigenetic alterations, malignant cells undergo profound metabolic reprogramming to sustain uncontrolled proliferation, survive hostile microenvironments, evade immune surveillance, and acquire invasive and metastatic capacities (Tan et al., 2026; Yin et al., 2023; Dai et al., 2025). Classical hallmarks such as the Warburg effect have now evolved into a far more complex landscape encompassing lipid remodeling, amino acid dependency, redox homeostasis, mitochondrial rewiring, and metabolite-driven epigenetic regulation (Hu et al., 2024; Yin et al., 2020; Yin et al., 2024). These metabolic adaptations are not merely consequences of oncogenic transformation, but active drivers of tumor evolution, therapeutic resistance, and immune escape.
Recent technological advances have fundamentally transformed the field. High-resolution mass spectrometry–based metabolomics, single-cell and spatial profiling technologies, and multi-omics integration strategies now enable the systematic dissection of metabolic heterogeneity within the tumor microenvironment (Liu et al., 2026). At the same time, artificial intelligence and machine learning approaches are increasingly applied to extract predictive patterns from complex metabolic datasets, accelerating biomarker discovery and clinical translation (McKenzie et al., 2026). Together, these innovations are shifting tumor metabolomics from descriptive profiling toward mechanistic understanding and precision medicine. However, significant challenges remain. Metabolic plasticity allows tumors to dynamically adapt to environmental stressors such as hypoxia, nutrient deprivation, immune pressure, and therapeutic intervention, complicating target identification and therapeutic durability (Karakousi et al., 2026). Moreover, the translation of metabolomic signatures into clinically robust diagnostics and therapeutic strategies requires rigorous validation, standardized methodologies, and integrative analytical frameworks (Long et al., 2020).
The Research Topic “Challenges and Opportunities in Tumor Metabolomics” brings together eleven contributions that collectively capture the conceptual, methodological, and translational dimensions of this rapidly evolving field. Zhu et al. demonstrated that the transcription factor SOX2 promotes osteosarcoma invasion and metastasis by transcriptionally activating LPCAT1, thereby reprogramming cholesterol metabolism. Their in vitro and in vivo data establish a direct link between stemness-associated transcriptional programs and lipid metabolic remodeling, highlighting cholesterol metabolism as a critical vulnerability in aggressive bone tumors. In non-small cell lung cancer, Kong et al. elucidated a glutamine metabolism–driven axis in which FGF17, activated under conditions of GLUL overexpression, sustains redox homeostasis and epithelial–mesenchymal transition through FGFR4/MEK5/ERK5/NRF2 signaling. Importantly, targeting this pathway not only suppressed invasion but also enhanced cisplatin sensitivity, illustrating how metabolic dependencies can be exploited for combinatorial therapeutic strategies. Complementing these findings, Guo et al. investigated urothelial carcinoma and revealed a strong association between HER2 positivity and elevated lactate dehydrogenase levels, linking oncogenic signaling with enhanced glycolytic activity. Their clinical data suggest that metabolic markers such as LDH may serve as accessible surrogates for aggressive, HER2-driven disease phenotypes.
A central theme emerging from this Research Topic is the role of tumor metabolism in sculpting the immune microenvironment. Dong et al. provided compelling evidence that hypoxia-induced metabolic stress in uveal melanoma promotes immune evasion through CD63-enriched exosomes. Under hypoxic conditions, tumor cells increased exosomal lactate delivery, reprogramming macrophage metabolism toward an immunosuppressive M2 phenotype and inducing CD8+ T-cell exhaustion. This study highlights extracellular vesicle–mediated metabolic communication as a previously underappreciated mechanism of immune suppression and identifies the hypoxia/CD63/exosomal lactate axis as a potential therapeutic target. At a systemic level, Zheng et al. integrated serum metabolomics with machine learning to predict responses to chemoimmunotherapy in advanced lung squamous cell carcinoma. By constructing a robust prognostic model based on eight metabolites, this work illustrates how metabolic signatures can inform patient stratification and therapeutic decision-making in the era of immunotherapy. Although not cancer-specific, the study by Yang et al. on myocardial infarction further reinforces the broader relevance of immunometabolic regulation. Their identification of inflammation- and immune-related hub genes through integrative bioinformatics and machine learning underscores methodological frameworks that are directly transferable to tumor metabolomics and immune-oncology research.
Beyond individual mechanisms, several contributions adopt a macroscopic view of tumor metabolomics research. Chen et al. conducted a comprehensive bibliometric analysis of spatial metabolomics in cancer, revealing rapid growth of the field and highlighting metabolic heterogeneity within the tumor microenvironment as a dominant research frontier. Their work emphasizes the transformative potential of spatially resolved metabolomics for understanding cell–cell metabolic interactions in situ. Similarly, Cai et al. systematically mapped global research trends in ocular tumor–associated metabolites and metabolism-related intraocular malignancies, respectively. These analyses consistently identified uveal melanoma as a central disease focus and documented a shift toward immune regulation, hypoxia, lipid metabolism, and precision oncology. Together, these studies provide valuable context for positioning mechanistic discoveries within broader scientific and clinical trajectories. In melanoma research, Bai et al. further explored global trends in resistance to BRAF and MEK inhibitors, revealing an increasing emphasis on metabolic adaptation, non-apoptotic cell death pathways, and combination therapies. These insights reinforce the concept that metabolic plasticity underlies therapeutic resistance and must be addressed to achieve durable clinical responses.
Collectively, the studies in this Research Topic highlight several key challenges and opportunities in tumor metabolomics. Methodologically, integrating metabolomics with single-cell, spatial, and computational approaches remains technically demanding but essential for capturing tumor heterogeneity. Biologically, disentangling causal metabolic drivers from adaptive consequences requires rigorous functional validation. Clinically, translating complex metabolic signatures into robust, reproducible biomarkers and therapeutic targets continues to be a major hurdle. By bridging mechanistic discovery with computational innovation and translational relevance, this Research Topic underscores tumor metabolomics as a dynamic and integrative field poised to reshape cancer diagnosis and therapy. Continued interdisciplinary collaboration and methodological innovation will be critical to fully harness the clinical potential of tumor metabolomics in the years ahead.
Author contributions
LY: Funding acquisition, Project administration, Writing – original draft. CK: Conceptualization, Data curation, Writing – review and editing. CC: Funding acquisition, Resources, Software, Supervision, Writing – review and editing. HT: Investigation, Methodology, Resources, Software, Writing – review and editing. ZC: Funding acquisition, Methodology, Project administration, Writing – original draft.
Funding
The author(s) declared that financial support was received for this work and/or its publication. This work was supported by the National Natural Science Foundation of China (No. 82002923,82203698), the Sailing Program of Shanghai Science and Technology Commission (No. 22YF1425000), the Postdoctoral Research Start Up Foundation of Shanghai Ninth People’s Hospital (No. 202501042), the Sponsored by Shanghai Rising-Star Program (No.24QA2708900).
Acknowledgements
We sincerely thank all contributing authors, reviewers, and the editorial team of Frontiers for their efforts and dedication in bringing this Research Topic to fruition.
Conflict of interest
The author(s) declared that this work was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.
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Keywords: diagnosis, metabolic reprogramming, microenvironment, therapy, tumor metabolomics
Citation: Yin L, Kong C, Cai C, Teng H and Chang Z (2026) Editorial: Challenges and opportunities in tumor metabolomics. Front. Mol. Biosci. 13:1796372. doi: 10.3389/fmolb.2026.1796372
Received: 26 January 2026; Accepted: 04 February 2026;
Published: 11 February 2026.
Edited and reviewed by:
Matteo Becatti, University of Firenze, ItalyCopyright © 2026 Yin, Kong, Cai, Teng and Chang. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
*Correspondence: Lei Yin, eWx0YWxlQDEyNi5jb20=; Zhengyan Chang, RmVsaWNpdHljeUAxNjMuY29t