Cancer metabolic reprogramming plays a critical role in tumor progression and therapeutic resistance, underscoring the need for advanced analytical strategies. Metabolomics, leveraging mass spectrometry and nuclear magnetic resonance (NMR) spectroscopy, offers a comprehensive and functional readout of tumor biochemistry. By enabling both targeted metabolite quantification and untargeted profiling, metabolomics captures the dynamic metabolic alterations associated with cancer. The integration of metabolomics with machine learning (ML) approaches further enhances the interpretation of these complex, high-dimensional datasets, providing powerful insights into cancer biology from biomarker discovery to therapeutic targeting. This review systematically examines the transformative role of ML in cancer metabolomics. We discuss how various ML methodologies—including supervised algorithms (e.g., Support Vector Machine, Random Forest), unsupervised techniques (e.g., Principal Component Analysis, t-SNE), and deep learning frameworks—are advancing cancer research. Specifically, we highlight three major applications of ML–metabolomics integration: (1) cancer subtyping, exemplified by the use of Similarity Network Fusion (SNF) and LASSO regression to classify triple-negative breast cancer into subtypes with distinct survival outcomes; (2) biomarker discovery, where Random Forest and Partial Least Squares Discriminant Analysis (PLS-DA) models have achieved >90% accuracy in detecting breast and colorectal cancers through biofluid metabolomics; and (3) prognostic modeling, demonstrated by the identification of race-specific metabolic signatures in breast cancer and the prediction of clinical outcomes in lung and ovarian cancers. Beyond these areas, we explore applications across prostate, thyroid, and pancreatic cancers, where ML-driven metabolomics is contributing to earlier detection, improved risk stratification, and personalized treatment planning. We also address critical challenges, including issues of data quality (e.g., batch effects, missing values), model interpretability, and barriers to clinical translation. Emerging solutions, such as explainable artificial intelligence (XAI) approaches and standardized multi-omics integration pipelines, are discussed as pathways to overcome these hurdles. By synthesizing recent advances, this review illustrates how ML-enhanced metabolomics bridges the gap between fundamental cancer metabolism research and clinical application, offering new avenues for precision oncology through improved diagnosis, prognosis, and tailored therapeutic strategies.

Urine is considered an outstanding biological fluid for biomarker discovery, reflecting both systemic and urogenital physiology. However, analyzing the N-glycome in urine in detail has been challenging due to the low abundance of glycans attached to glycoproteins compared to free oligosaccharides. Therefore, this study aims to thoroughly analyze urinary N-glycome using LC-MS/MS. The N-glycans were released using hydrazine and labeled with 2-aminopyridine (PA), followed by anion-exchange fractionation before LC-MS/MS analysis. A total of 109 N-glycans were identified and quantified, of which 58 were identified and quantified repeatedly in at least 80% of samples and accounted for approximately 85% of the total urinary glycome signal. Interestingly, a comparison between urine and serum N-glycome revealed that approximately 50% of the urinary glycome could originate from the kidney and urinary tract, where they were exclusively identified in urine, while the remaining 50% were common in both. Additionally, a correlation was found between age/sex and the relative abundances of urinary N-glycome, with more age-related changes observed in women than men. The results of this study provide a reference for human urine N-glycome profiling and structural annotations.