LG: Methodology, Investigation, Data curation, Writing—original draft. XW: Investigation, Data curation. SL: Conceptualization, Investigation. CX: Formal analysis, Visualization. KY: Validation, Supervision, Writing—review & editing. All authors read and approved the submitted version.
Conflicts of interest
The authors declare that they have no conflicts of interest.
Ethical approval
Not applicable.
Consent to participate
Not applicable.
Consent to publication
Not applicable.
Availability of data and materials
The gastric cancer metabolomics dataset used in this study is derived from a previously published multi‑center study by Chen et al. (DOI: 10.1038/s41467-024-46043-y), which conducted targeted metabolomics analysis on plasma samples from 702 participants across three independent cohorts.
The breast cancer metabolomics dataset is publicly available from the Metabolomics Workbench repository, a public repository for metabolomics data and metadata supported by the NIH Common Fund’s Metabolomics Program. The dataset can be accessed under Study ID: ST000355 (URL: https://www.metabolomicsworkbench.org/data/DRCCMetadata.php?Mode=Study&StudyID=ST000355). This GC‑MS‑based study includes 211 plasma samples from 135 breast cancer patients and 76 non‑cancer controls.
All data were used in accordance with the repository’s terms of use and the original study’s data sharing policies. No new data were generated during this study.
Funding
This work was supported by the National Key R&D Program of China [2024YFC3607500] and the Key Research and Development Project in Henan Province [No.241111114200]. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.
Open Exploration maintains a neutral stance on jurisdictional claims in published institutional affiliations and maps. All opinions expressed in this article are the personal views of the author(s) and do not represent the stance of the editorial team or the publisher.
References
Muthubharathi BC, Gowripriya T, Balamurugan K. Metabolomics: small molecules that matter more.Mol Omics. 2021;17:210–29. [DOI] [PubMed]
Yang Q, Cai Y, Guan Y, Wang Z, Guo S, Qiu S, et al. Metabolic phenotypes: Molecular bridges between health homeostasis and disease imbalance.Comput Struct Biotechnol J. 2025;27:4710–9. [DOI] [PubMed] [PMC]
Bray F, Laversanne M, Sung H, Ferlay J, Siegel RL, Soerjomataram I, et al. Global cancer statistics 2022: GLOBOCAN estimates of incidence and mortality worldwide for 36 cancers in 185 countries.CA: Cancer J Clin. 2024;74:229–63. [DOI] [PubMed]
Han B, Zheng R, Zeng H, Wang S, Sun K, Chen R, et al. Cancer incidence and mortality in China, 2022.J Natl Cancer Cent. 2024;4:47–53. [DOI] [PubMed] [PMC]
Fuller H, Zhu Y, Nicholas J, Chatelaine HA, Drzymalla EM, Sarvestani AK, et al. Metabolomic epidemiology offers insights into disease aetiology.Nat Metab. 2023;5:1656–72. [DOI] [PubMed] [PMC]
Al-Sulaiti H, Almaliti J, Naman CB, Al Thani AA, Yassine HM. Metabolomics Approaches for the Diagnosis, Treatment, and Better Disease Management of Viral Infections.Metabolites. 2023;13:948. [DOI] [PubMed] [PMC]
Sillé F, Hartung T. Metabolomics in Preclinical Drug Safety Assessment: Current Status and Future Trends.Metabolites. 2024;14:98. [DOI] [PubMed] [PMC]
Gao X, Yan M, Zhang C, Wu G, Shang J, Zhang C, et al. MDNN-DTA: a multimodal deep neural network for drug-target affinity prediction.Front Genet. 2025;16:1527300. [DOI] [PubMed] [PMC]
Hussain S, Xi X, Ullah I, Inam SA, Naz F, Shaheed K, et al. A Discriminative Level Set Method with Deep Supervision for Breast Tumor Segmentation.Comput Biol Med. 2022;149:105995. [DOI] [PubMed]
Inam SA, Iqbal D, Hashim H, Khuhro MA. An empirical approach towards detection of tuberculosis using deep convolutional neural network.Int J Data Min Model Manag. 2024;16:101–12. [DOI]
Galal A, Talal M, Moustafa A. Applications of machine learning in metabolomics: Disease modeling and classification.Front Genet. 2022;13:1017340. [DOI] [PubMed] [PMC]
Chi J, Shu J, Li M, Mudappathi R, Jin Y, Lewis F, et al. Artificial intelligence in metabolomics: a current review.TrAC Trends Anal Chem. 2024;178:117852. [DOI] [PubMed] [PMC]
Sen P, Lamichhane S, Mathema VB, McGlinchey A, Dickens AM, Khoomrung S, et al. Deep learning meets metabolomics: a methodological perspective.Brief Bioinform. 2020;22:1531–42. [DOI] [PubMed]
Elguoshy A, Zedan H, Saito S. Machine Learning-Driven Insights in Cancer Metabolomics: From Subtyping to Biomarker Discovery and Prognostic Modeling.Metabolites. 2025;15:514. [DOI] [PubMed] [PMC]
Souto-Carneiro M, Tóth L, Behnisch R, Urbach K, Klika KD, Carvalho RA, et al. Differences in the serum metabolome and lipidome identify potential biomarkers for seronegative rheumatoid arthritis versus psoriatic arthritis.Ann Rheum Dis. 2020;79:499–506. [DOI] [PubMed] [PMC]
Rahujo A, Atif D, Inam SA, Khan AA, Ullah S. A survey on the applications of transfer learning to enhance the performance of large language models in healthcare systems.Discov Artif Intell. 2025;5:90. [DOI]
Elmarakeby HA, Hwang J, Arafeh R, Crowdis J, Gang S, Liu D, et al. Biologically informed deep neural network for prostate cancer discovery.Nature. 2021;598:348–52. [DOI] [PubMed] [PMC]
Hartman E, Scott AM, Karlsson C, Mohanty T, Vaara ST, Linder A, et al. Interpreting biologically informed neural networks for enhanced proteomic biomarker discovery and pathway analysis.Nat Commun. 2023;14:5359. [DOI] [PubMed] [PMC]
Kaynar G, Cakmakci D, Bund C, Todeschi J, Namer IJ, Cicek AE. PiDeeL: metabolic pathway-informed deep learning model for survival analysis and pathological classification of gliomas.Bioinformatics. 2023;39:e39. [DOI] [PubMed] [PMC]
Chen Y, Wang B, Zhao Y, Shao X, Wang M, Ma F, et al. Metabolomic machine learning predictor for diagnosis and prognosis of gastric cancer.Nat Commun. 2024;15:1657. [DOI] [PubMed] [PMC]
Milacic M, Beavers D, Conley P, Gong C, Gillespie M, Griss J, et al. The Reactome Pathway Knowledgebase 2024.Nucleic Acids Res. 2023;52:D672–8. [DOI] [PubMed] [PMC]
Djoumbou Feunang Y, Eisner R, Knox C, Chepelev L, Hastings J, Owen G, et al. ClassyFire: automated chemical classification with a comprehensive, computable taxonomy.J Cheminformatics. 2016;8:61. [DOI] [PubMed] [PMC]
Lundberg SM, Lee SI. A unified approach to interpreting model predictions. In: Proceedings of the 31st International Conference on Neural Information Processing Systems; 2017 Dec 4–9; Long Beach, California, USA. Curran Associates Inc.; 2017. pp. 4768–77.
Zhao Y, Li JS, Guo MZ, Feng BS, Zhang JP. Inhibitory effect of S-adenosylmethionine on the growth of human gastric cancer cells in vivo and in vitro.Chin J Cancer. 2010;29:752–60. [DOI] [PubMed]
Gao P, Zuo C, Yuan W, Cai J, Chai X, Gong R, et al. Spatiotemporal multi-omics analysis uncovers NAD-dependent immunosuppressive niche triggering early gastric cancer.Signal Transduct Target Ther. 2025;10:313. [DOI] [PubMed] [PMC]
Yaku K, Okabe K, Hikosaka K, Nakagawa T. NAD Metabolism in Cancer Therapeutics.Front Oncol. 2018;8:622. [DOI] [PubMed] [PMC]
Mu X, Zhao T, Xu C, Shi W, Geng B, Shen J, et al. Oncometabolite succinate promotes angiogenesis by upregulating VEGF expression through GPR91-mediated STAT3 and ERK activation.Oncotarget. 2017;8:13174–85. [DOI] [PubMed] [PMC]
Tsuchiya A, Nishizaki T. Anticancer effect of adenosine on gastric cancerviadiverse signaling pathways.World J Gastroenterol. 2015;21:10931–5. [DOI] [PubMed] [PMC]
Saitoh M, Nagai K, Nakagawa K, Yamamura T, Yamamoto S, Nishizaki T. Adenosine induces apoptosis in the human gastric cancer cells via an intrinsic pathway relevant to activation of AMP-activated protein kinase.Biochem Pharmacol. 2004;67:2005–11. [DOI] [PubMed]
Lu Y, Zhang X, Zhang H, Lan J, Huang G, Varin E, et al. Citrate induces apoptotic cell death: a promising way to treat gastric carcinoma?Anticancer Res. 2011;31:797–805. [PubMed]
Yuan LW, Yamashita H, Seto Y. Glucose metabolism in gastric cancer: The cutting-edge.World J Gastroenterol. 2016;22:2046–59. [DOI] [PubMed] [PMC]
Wu J, Liu N, Chen J, Tao Q, Li Q, Li J, et al. The Tricarboxylic Acid Cycle Metabolites for Cancer: Friend or Enemy.Research. 2024;7:0351. [DOI] [PubMed] [PMC]
Ahuja S, Zaheer S. Molecular Mediators of Metabolic Reprogramming in Cancer: Mechanisms, Regulatory Networks, and Therapeutic Strategies.Immunology. 2025;177:1–43. [DOI] [PubMed]
Liu K, Liu Y, Zhang S, Li Z, Qu W, Li P, et al. Epigenetic regulation of RNA methylations in gastric cancer.Oncol Rev. 2025;19:1601511. [DOI] [PubMed] [PMC]
Mentch SJ, Locasale JW. One‐carbon metabolism and epigenetics: understanding the specificity.Ann N Y Acad Sci. 2015;1363:91–8. [DOI] [PubMed] [PMC]
Cao X, Guo Y, Guo Z, Liu Y, Dou Y, Xue L. One-carbon metabolism in cancer: moonlighting functions of metabolic enzymes and anti-tumor therapy.Cancer Metastasis Rev. 2025;44:91. [DOI] [PubMed] [PMC]
Zhang M, Saad C, Le L, Halfter K, Bauer B, Mansmann UR, et al. Computational modeling of methionine cycle-based metabolism and DNA methylation and the implications for anti-cancer drug response prediction.Oncotarget. 2018;9:22546–58. [DOI] [PubMed] [PMC]
Borin TF, Angara K, Rashid MH, Achyut BR, Arbab AS. Arachidonic Acid Metabolite as a Novel Therapeutic Target in Breast Cancer Metastasis.Int J Mol Sci. 2017;18:2661. [DOI] [PubMed] [PMC]
Ghanbari F, Fortier AM, Park M, Philip A. Cholesterol-Induced Metabolic Reprogramming in Breast Cancer Cells Is Mediated via the ERRα Pathway.Cancers. 2021;13:2605. [DOI] [PubMed] [PMC]
Wang H, Sun J, Jiang Z, Tong Z, Wang C. Glutamate promotes triple-negative breast cancer development through IRE1α/XBP1-mediated macrophage polarization: mechanism insights and therapy.Discov Oncol. 2025;16:1009. [DOI] [PubMed] [PMC]
Knott SRV, Wagenblast E, Khan S, Kim SY, Soto M, Wagner M, et al. Asparagine bioavailability governs metastasis in a model of breast cancer.Nature. 2018;554:378–81. [DOI] [PubMed] [PMC]
Jiang Y, Cao Y, Wang Y, Li W, Liu X, Lv Y, et al. Cysteine transporter SLC3A1 promotes breast cancer tumorigenesis.Theranostics. 2017;7:1036–46. [DOI] [PubMed] [PMC]
Lu L, Xue L, Jiang S, He G, Deng X. The pseudouridine synthase PUS7 is associated with stemness and represents a potential therapeutic target in triple-negative breast cancer cells.Sci Rep. 2026;16:2411. [DOI] [PubMed] [PMC]
Xie G, Zhou B, Zhao A, Qiu Y, Zhao X, Garmire L, et al. Lowered circulating aspartate is a metabolic feature of human breast cancer.Oncotarget. 2015;6:33369–81. [DOI] [PubMed] [PMC]