ArticleNature communications2024
Metabolomic machine learning predictor for diagnosis and prognosis of gastric cancer.
Article in Nature communications, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 163 papers, 8 of them syntheses that pooled it.
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Who cites it
163 citing papers in PubMed, 8 syntheses or guidelines pooled it.
- Unveiling the efficacy predictors and potential mechanisms ofFrontiers in immunology · 2026Pooled it
- Pooled it
- Metabolomics and metabolites in cancer diagnosis and treatment.Molecular biomedicine · 2025Pooled it
- The application of random forest-based models in prognostication of gastrointestinal tract malignancies: a systematic review.Frontiers in artificial intelligence · 2025Pooled it
- Machine learning-based mortality risk prediction models in patients with sepsis-associated acute kidney injury: a systematic review.Frontiers in medicine · 2025Pooled it
- Global research hotspots and trends in the application of artificial intelligence in gastric cancer: a bibliometric analysis from 2005 to 2024.Frontiers in oncology · 2025Pooled it
- Comparative diagnostic accuracy of different artificial intelligence models for early gastric cancer: a systematic review and meta-analysis.Frontiers in oncology · 2025Pooled it
- Current status and trends of machine learning applied in clinical research of gastric cancer from 2004 to 2023: global bibliometric and visual analysis.Frontiers in oncology · 2025Pooled it
- Metabolomic profiles impacted by brief mindfulness intervention with contributions to improved health.Scientific reports · 2025Trial
- Decoding the microbiome: artificial intelligence-targeted gut microenvironment breakthroughs in personalized cancer therapy.Gut microbes · 2026Review
- Single-cell transcriptomics and machine-learning reveal M1 macrophage-driven progression from minimal change disease to focal segmental glomerulosclerosis.Renal failure · 2026Article
- Clinical significance of intraoperative dissection time and machine learning-based prediction in laparoscopic distal gastrectomy for gastric cancer.Surgical endoscopy · 2026Article
- Sphingomyelin as a key negative mediator in atrial fibrillation via lipid profiling analysis and functional identification.Europace : European pacing, arrhythmias, and cardiac electrophysiology : journal of the working groups on cardiac pacing, arrhythmias, and cardiac cellular electrophysiology of the European Society of Cardiology · 2026Article
- Molecular classification and precision therapy in gastric cancer: Current advances and future perspectives (Review).Oncology reports · 2026Review
- Serum Bile Acid Metabolomics-Based Machine Learning Model for Non-Invasive Diagnosis of Gastric Cancer.Cancer medicine · 2026Article
- Review
- Plasma metabolomic signatures enable the diagnosis and prognosis of chronic obstructive pulmonary disease.Nature communications · 2026Article
- TMEM97 regulates cholesterol biosynthesis and mitochondrial metabolism in gastric carcinoma.iScience · 2026Article
- Metabolic biomarkers of treatment-resistant schizophrenia and cognitive correlates: insights from a machine learning.Molecular psychiatry · 2026Article
- A high-resolution circulating metabolic atlas of lung adenocarcinoma progression supports early and accurate diagnosis.Cell reports. Medicine · 2026Article
103 more citing papers are in PubMed but not listed here.
Corrections and comments
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Authors and funding
17 authors.
Funding
Abstract
Gastric cancer (GC) represents a significant burden of cancer-related mortality worldwide, underscoring an urgent need for the development of early detection strategies and precise postoperative interventions. However, the identification of non-invasive biomarkers for early diagnosis and patient risk stratification remains underexplored. Here, we conduct a targeted metabolomics analysis of 702 plasma samples from multi-center participants to elucidate the GC metabolic reprogramming. Our machine learning analysis reveals a 10-metabolite GC diagnostic model, which is validated in an external test set with a sensitivity of 0.905, outperforming conventional methods leveraging cancer protein markers (sensitivity < 0.40). Additionally, our machine learning-derived prognostic model demonstrates superior performance to traditional models utilizing clinical parameters and effectively stratifies patients into different risk groups to guide precision interventions. Collectively, our findings reveal the metabolic landscape of GC and identify two distinct biomarker panels that enable early detection and prognosis prediction respectively, thus facilitating precision medicine in GC.
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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 OpenQuestion graph.