ArticleCell reports. Medicine2025
Metabolic fingerprinting enables rapid, label-free histopathology in gastric cancer diagnosis and prognostic prediction.
Article in Cell reports. Medicine, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 14 papers.
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Who cites it
14 citing papers in PubMed.
- A Dual-Mode In Situ EV-miRNA Profiling Platform for Machine-Learning-Assisted Gastric Cancer Liquid Biopsy.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2026Article
- Systems biology of Wnt signaling in glioma chemotherapy resistance: Integrating multi-omics mechanisms and emerging therapeutic vulnerabilities (Review).Oncology letters · 2026Review
- Multi-modal AI-enabled steatotic liver disease diagnostics using facial images and metabolomics.Cell reports. Medicine · 2026Article
- Heterojunction-Engineered Mass Spectrometry Platform for Deciphering Serum Metabolic Fingerprints in Diagnosis of Respiratory Diseases.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2026Article
- The evolving landscape of the Warburg effect in gastric cancer: From molecular mechanisms to targeted therapy.Clinical and translational medicine · 2026Review
- Artificial intelligence-driven integration of multi-biofluid omics and clinical phenotype enables stratification of endometrial cancer.Cell reports. Medicine · 2026Article
- Article
- Single-Cell Metabolic Imaging and Digital Scoring of Fat Tissue Remodeling by Label-Free Metabolic Microscopy.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2026Article
- Primary tissue metabolic fingerprinting for efficient diagnosis of lymph node metastasis and metabolic reprogramming mechanisms in colorectal cancer.Materials today. Bio · 2026Article
- GAN-enhanced digital twins for biomarker-guided cancer immunotherapy and adoptive cell therapy: a mini review.Frontiers in medicine · 2026Review
- Urinary metabolomics-based machine learning for diagnosis of early gastric neoplasia: a retrospective diagnostic case-control study.Frontiers in oncology · 2026Article
- Hydroxygenkwanin Identified in Rehmanniae Radix Praeparata-Corni Fructus Ameliorates Postmenopausal Osteoporosis via Modulation of the AGE-RAGE Signaling Pathway.Journal of inflammation research · 2026Article
- AI-driven genotype-phenotype modeling: a framework integrating multi-modal single-cell genomics and reverse vaccinology forFrontiers in genetics · 2026Review
- Enhancing Immunotherapy in Diffuse Large B-Cell Lymphoma: The Synergistic Potential of Metabolic Checkpoint Inhibitors and Immunomodulation.OncoTargets and therapy · 2026Review
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Authors and funding
23 authors.
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No grant is acknowledged in the PubMed record.
Abstract
Histopathological evaluation is a cornerstone of cancer identification but often involves time-consuming labeling processes (∼days per sample) and experience-dependent interpretation. Herein, we introduce a rapid (∼40 min per sample) and label-free histopathological method based on metabolic fingerprinting of tissue using nanoparticle-enhanced laser desorption/ionization mass spectrometry. Applied to gastric cancer (GC, n = 284 paired tissue), this approach distinguishes malignant from benign tissues (area under the curve [AUC] of 0.979), identifies tumor subtypes (AUC of 0.963), and assesses prognosis (p < 0.05) without specialized pathologists. External validation on 238 samples from an independent cohort confirmed its robustness. This method advances histopathological analysis, offering potential for scalable clinical use.
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