ArticleGut2023
Efficient plasma metabolic fingerprinting as a novel tool for diagnosis and prognosis of gastric cancer: a large-scale, multicentre study.
Article in Gut, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 44 papers, 1 of them a synthesis that pooled it.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
44 citing papers in PubMed, 1 synthesis or guideline pooled it, 69 citations in OpenAlex.
- Metabolomics and metabolites in cancer diagnosis and treatment.Molecular biomedicine · 2025Pooled it
- Metabolic Signatures for Liver Cancer Diagnosis and Mechanistic Insights: A Large-Scale, Multicenter Study.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2026Article
- Metabolomic and lifestyle profiles refine BMI-metabolic phenotypes in older adults.Cell reports. Medicine · 2026Observational
- A machine learning‑enhanced serum metabolomics model for non‑invasive detection of gastric cancer.Metabolomics : Official journal of the Metabolomic Society · 2026Article
- Targeted metabolomic profiling of gastric cancer biopsies.The Indian journal of medical research · 2026Article
- Extracellular vesicle and particle biomarkers in cancer: a machine learning blueprint for liquid biopsy.Journal of nanobiotechnology · 2026Review
- A methodological framework for establishing regional disease-specific health research priorities (the RDHRP Framework).Health research policy and systems · 2026Article
- Multi-phase hybrid metabolomics framework identifies clinically applicable plasma signatures for early detection of gastric cancer.Nature communications · 2026Article
- Lysophosphatidylcholine acyltransferase 1 promotes head and neck squamous cell carcinoma progression by enhancing COX17-dependent oxidative phosphorylation.Cell death discovery · 2026Article
- Artificial intelligence models: transforming early diagnosis and precise treatment of gastrointestinal cancers.Molecular cancer · 2026Review
- Autoencoders decode polyunsaturated fatty acid metabolism with strong genetic architecture in cancer risk.EBioMedicine · 2026Article
- Urinary metabolomics-based machine learning for diagnosis of early gastric neoplasia: a retrospective diagnostic case-control study.Frontiers in oncology · 2026Article
- Exploratory Serum Metabolomics Identifies Metabolic Subgroups Across the Gastric Dysplasia-Early Cancer Spectrum.Journal of Cancer · 2026Article
- Metabolic signatures for gastric cancer diagnosis and mechanistic insights: a multicenter study.EMBO molecular medicine · 2025Article
- Identification of an E2Fs-based gene signature for predicting prognosis and therapeutic response in colorectal cancer.Discover oncology · 2025Article
- A prognostic model for gastric cancer constructed by multiple machine learning algorithms.Journal of molecular histology · 2025Article
- Multimodal artificial intelligence technology in the precision diagnosis and treatment of gastroenterology and hepatology: Innovative applications and challenges.World journal of gastroenterology · 2025Review
- AI-based large-scale screening of gastric cancer from noncontrast CT imaging.Nature medicine · 2025Observational
- Serum metabolic patterns reveal the diagnostic and prognostic role of alanine abnormality in ocular adnexal lymphoma.Proceedings of the National Academy of Sciences of the United States of America · 2025Article
- Low-dimensional nanomaterials for electrochemical biosensing of gastric cancer biomarkers in whole blood: Current trends and future Prospects.Materials today. Bio · 2025Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
29 authors at 7 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
objectiveMetabolic biomarkers are expected to decode the phenotype of gastric cancer (GC) and lead to high-performance blood tests towards GC diagnosis and prognosis. We attempted to develop diagnostic and prognostic models for GC based on plasma metabolic information.
designWe conducted a large-scale, multicentre study comprising 1944 participants from 7 centres in retrospective cohort and 264 participants in prospective cohort. Discovery and verification phases of diagnostic and prognostic models were conducted in retrospective cohort through machine learning and Cox regression of plasma metabolic fingerprints (PMFs) obtained by nanoparticle-enhanced laser desorption/ionisation-mass spectrometry (NPELDI-MS). Furthermore, the developed diagnostic model was validated in prospective cohort by both NPELDI-MS and ultra-performance liquid chromatography-MS (UPLC-MS).
resultsWe demonstrated the high throughput, desirable reproducibility and limited centre-specific effects of PMFs obtained through NPELDI-MS. In retrospective cohort, we achieved diagnostic performance with areas under curves (AUCs) of 0.862-0.988 in the discovery (n=1157 from 5 centres) and independent external verification dataset (n=787 from another 2 centres), through 5 different machine learning of PMFs, including neural network, ridge regression, lasso regression, support vector machine and random forest. Further, a metabolic panel consisting of 21 metabolites was constructed and identified for GC diagnosis with AUCs of 0.921-0.971 and 0.907-0.940 in the discovery and verification dataset, respectively. In the prospective study (n=264 from lead centre), both NPELDI-MS and UPLC-MS were applied to detect and validate the metabolic panel, and the diagnostic AUCs were 0.855-0.918 and 0.856-0.916, respectively. Moreover, we constructed a prognosis scoring system for GC in retrospective cohort, which can effectively predict the survival of GC patients.
conclusionWe developed and validated diagnostic and prognostic models for GC, which also contribute to advanced metabolic analysis towards diseases, including but not limited to GC.
Indexed as
Identifiers
What Socratic holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the Socratic graph.