Development of a Large-Scale Serum Metabolome Relative Quantitative Method and Application for Metabolic Traits of Colon Cancer

Detection of Pancreatic Cancer via Specific Metabolite Markers: A Metabolomics Approach

ABSTRACT Background and Aims Pancreatic ductal adenocarcinoma (PDAC) remains one of the most lethal malignancies worldwide because most patients are diagnosed at advanced stages when curative treatment is no longer feasible. Metabolomics has emerged as a promising strategy for identifying biochemical alterations associated with early tumor development and may improve the early detection of PDAC. This review critically evaluates the role of metabolomics in PDAC diagnosis, focusing on metabolite biomarkers, nuclear magnetic resonance (NMR) spectroscopy, mass spectrometry‐based platforms, and machine learning assisted approaches. Methods A narrative review of the current literature was conducted to assess the diagnostic potential, analytical methodologies, and translational challenges of metabolomics in PDAC. Evidence related to metabolite biomarkers, analytical platforms, artificial intelligence applications, and clinical implementation was critically examined. Results PDAC is characterized by significant alterations in glycolysis, amino acid metabolism, lipid metabolism, and microbiome derived metabolites. Several metabolite panels have demonstrated promising diagnostic performance, particularly when combined with CA19‐9. Advanced analytical platforms, including NMR spectroscopy, liquid chromatography mass spectrometry, and gas chromatography mass spectrometry, have enabled detailed characterization of metabolic signatures and improved discrimination between PDAC and benign pancreatic diseases such as chronic pancreatitis. However, clinical translation remains limited by methodological heterogeneity, biological variability, lack of standardized analytical workflows, interlaboratory reproducibility concerns, and insufficient prospective multicenter validation. Although machine learning has enhanced biomarker discovery and pattern recognition, challenges related to overfitting, interpretability, and external validation remain unresolved. Conclusion Metabolomics is unlikely to replace existing diagnostic modalities in the near future but may substantially improve diagnostic accuracy when integrated with established biomarkers, imaging techniques, and molecular diagnostics. Future progress will depend on standardized protocols, large prospective validation studies, and clearly defined regulatory pathways. With these advances, metabolomics may become an important component of precision diagnostic strategies for PDAC.

The promise of deep urine proteomics for diagnosis of cancer, neurologic, and metabolic diseases

Introduction Urine offers a noninvasive and low-cost source of disease biomarkers, yet most proteomic studies have targeted single conditions. Using deep proteomic profiling and machine learning, we evaluated whether urinary protein signatures distinguish early-stage cancer, neurologic, and metabolic diseases from healthy controls. Methods This case–control diagnostic accuracy study analyzed urine samples from the Ukraine Association of Biobank (UAB), collected during routine medical check-ups. The study included 22 patients each with kidney, bladder, melanoma, prostate, ovarian, endometrial, and cervical cancers; 22 with multiple sclerosis (MS); 22 with metabolic dysfunction–associated steatohepatitis (MASH); and 66 healthy controls, yielding 264 samples analyzed in September 2023. Proteomic assays were performed using the Olink Explore 3072 platform, with laboratory personnel blinded to disease status. Urine proteomes were profiled to identify disease-specific protein signatures. The primary outcome was diagnostic accuracy of multiprotein urine panels for each disease compared with healthy controls, expressed as the area under the receiver operating characteristic curve (AUC), along with sensitivity and specificity calculated at a prespecified threshold. Results Expected sex‑specific differences (KLK3, MSMB higher in males; KLK8, KLK13 higher in females) supported assay validity. Three expression patterns were observed: (1) few strong, symmetric signals in melanoma and endometrial cancer; (2) asymmetry with many up‑regulated proteins in cervical, ovarian, and prostate cancers and in MS; and (3) broad up‑regulation in kidney and bladder cancers and in MASH. Multiprotein models outperformed single proteins, plateauing at five to seven. Maximum AUCs ranged from 0.88 (MS) to 0.98 0.97 (ovarian cancer), with AUCs ≥ 0.95 for seven of nine diseases. Some proteins (e.g., C9orf40, PPY) showed cross‑disease importance. Conclusions Urine proteomics identified disease‑related signals across cancer and metabolic conditions and may enable accurate, noninvasive classification using multiprotein panels, but given the exploratory design and its limitations, the reported accuracies should be regarded as upper-bound estimates requiring prospective validation.

A systematic DoE approach for optimizing urinary LC-HRMS metabolomics: enhancing reliability in bladder cancer profiling.

Untargeted metabolomics reveals differential metabolic pathways and biomarkers in the acute phase of Kawasaki disease

Kawasaki disease (KD) is one of the most common rheumatic diseases in children and manifests with multisystem clinical features. Using untargeted metabolomics, our study investigated alterations in small-molecule metabolites in plasma of children with acute KD. Our study aimed to identify differential metabolic pathways and potential biomarkers. Plasma samples were collected from 30 children diagnosed with KD and 30 age-matched healthy controls (HC) at Jinhua Maternal and Child Health Hospital between January 2025 and December 2025. Liquid chromatography-tandem mass spectrometry (LC-MS/MS) was applied to analyse plasma samples. Enriched pathways were identified using the Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analysis, and differential metabolic pathways were determined using MetaboAnalyst 5.0. Differential metabolites were screened using the nonparametric Mann–Whitney U -test and receiver operating characteristic curve area (AUC). The conservative average AUC from nested cross-validation was reported as the primary performance metric. Pearson correlation analysis was conducted to evaluate correlations between metabolites and clinical parameters. In total, 261 differential metabolites were identified between the KD and HC groups, including 87 lipids and lipid-like molecules, 69 organic heterocyclic compounds, 38 benzenoids, 34 organic acids, 14 phenylpropanoids, and 19 other compounds. Pathway analysis of these differential metabolites revealed 30 putatively enriched metabolic pathways for exploratory analysis. Of these pathways, primary bile acid biosynthesis, arginine biosynthesis, histidine metabolism, and phenylalanine-tyrosine-tryptophan biosynthesis were nominally associated with KD. Six metabolites with exploratory discriminatory performance (AUC > 0.8) were further identified: L-tyrosine, L-tryptophan, glutamine, histidine, histamine, and taurocholic acid. A combined model incorporating these metabolites achieved an apparent AUC of 0.984 in the full dataset; nested cross-validation yielded a more conservative AUC of 0.889 (95% CI 0.798–0.968), indicating promising exploratory discriminatory performance. Untargeted metabolomics enables identification of metabolically perturbed pathways during the acute phase of KD. L-tyrosine, L-tryptophan, glutamine, histidine, histamine, and taurocholic acid may serve as candidate biomarkers for acute phase of KD.

Serum metabolic characteristics of interstitial lung disease: a potential link between endocrine aging and gut-derived metabolites

Untargeted metabolomics has revealed significant systemic metabolic dysregulation in ILD and the biomarkers and “metabolic-immune-endocrine” interaction patterns identified offer potential leads for early diagnosis and targeted treatment, which require validation in larger cohorts.

Serum 1H NMR-Based Untargeted Metabolomics for Noninvasive Early Detection of Breast Cancer

Breast cancer (BC) is the most common malignancy among women, and late-stage presentation remains common in Asia, highlighting the need for affordable and minimally invasive adjuncts to imaging. This study evaluated whether fasting serum 1H NMR metabolomics could distinguish Malaysian women with newly diagnosed treatment-naïve BC from healthy controls and identify perturbed metabolic pathways. After spectral quality control exclusion, 101 participants were analyzed (BC, n = 44; control, n = 57). PCA and oOPLS-DA showed group separation, and 14 metabolites were deferred significantly after false discovery rate correction. Endogenous metabolites evaluated in BC included 3-hydroxybutyrate, citrate, glutamine, malonate, phenylalanine, dimethyl sulfone, myo-inositol, and L-carnitine, and the levels of formate, tyrosine, histamine, and p-hydroxyphenylacetate were reduced. Acetylsalicylate and salicylate were classified as exogenous aspirin-related markers and were excluded from the endogenous biomarker panel. Pathway analysis indicated disruptions in the tricarboxylic acid (TCA) cycle, glyoxylate/dicarboxylate metabolism, butanoate metabolism, and amino acid pathways. Metabolite-based classifiers achieved 0.901 accuracy on an internal held-out test partition (AUC 0.942) and 0.902 ± 0.061 accuracy in repeated stratified 5-fold cross-validation. These internally validated findings define a BC-associated serum metabolomic signature in a Southeast Asian cohort and warrant independent external validation.