ArticleScientific reports2022
Metabolite biomarker discovery for human gastric cancer using dried blood spot mass spectrometry metabolomic approach.
Article in Scientific reports, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 17 papers, 3 of them syntheses that pooled it.
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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.
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Who cites it
17 citing papers in PubMed, 3 syntheses or guidelines pooled it.
- Multi-omics and high-spatial-resolution omics: deciphering complexity in neurological disorders.GigaScience · 2025Pooled it
- Metabolomic profiling of upper GI malignancies in blood and tissue: a systematic review and meta-analysis.Journal of cancer research and clinical oncology · 2024Pooled it
- A Systematic Literature Review on the Use of Dried Biofluid Microsampling in Patients With Kidney Disease.Journal of clinical laboratory analysis · 2024Pooled it
- Serum acylcarnitine to amide ratio as a predictive biomarker for the progression of pulmonary disease caused byERJ open research · 2026Article
- Dried blood spot sample extraction for metabolomics and proteomics profiling for clinical trials: a descriptive exploratory study.Scientific reports · 2026Article
- Acylcarnitines in Cancer Metabolism: Mechanistic Insights and Stratification Potential.Cancers · 2026Review
- Integrative analysis of gut microbiota, plasma metabolome, and gene expression identifies causal mediators in Graves' disease pathogenesis.Frontiers in microbiology · 2026Article
- WHFDL: an explainable method based on World Hyper-heuristic and Fuzzy Deep Learning approaches for gastric cancer detection using metabolomics data.BioData mining · 2025Article
- Altered metabolic profiles in colon and rectal cancer.Scientific reports · 2025Article
- The prowess of metabolomics in cancer research: current trends, challenges and future perspectives.Molecular and cellular biochemistry · 2025Review
- Serum metabolic profiling analysis of chronic gastritis and gastric cancer by untargeted metabolomics.Annals of medicine and surgery (2012) · 2025Article
- Advancements in Mass Spectrometry-Based Targeted Metabolomics and Lipidomics: Implications for Clinical Research.Molecules (Basel, Switzerland) · 2024Review
- Targeted and untargeted metabolomics and lipidomics in dried blood microsampling: Recent applications and perspectives.Analytical science advances · 2024Review
- Automated sample preparation for electrospray ionization mass spectrometry based on CLOCK-controlled autonomous centrifugal microfluidics.Biomedical microdevices · 2024Article
- Metabolomic machine learning predictor for diagnosis and prognosis of gastric cancer.Nature communications · 2024Article
- Anti-Rheumatoid Arthritis Pharmacodynamic Substances Screening ofInternational journal of molecular sciences · 2023Article
- To metabolomics and beyond: a technological portfolio to investigate cancer metabolism.Signal transduction and targeted therapy · 2023Review
Corrections and comments
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Authors and funding
4 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
As one of the most common malignancies, gastric cancer (GC) is the third leading cause of cancer-related deaths in China. GC is asymptomatic in early stages, and the majority of GC mortality is due to delayed symptoms. It is an urgent task to find reliable biomarkers for the identification of GC in order to improve outcomes. A combination of dried blood spot sampling and direct infusion mass spectrometry (MS) technology was used to measure blood metabolic profiles for 166 patients with GC and 183 healthy individuals, and 93 metabolites including amino acids, carnitine/acylcarnitines and their derivatives, and related ratios were quantified. Multiple algorithms were used to characterize the changes of metabolic profiles in patients with GC compared to healthy individuals. A biomarker panel was identified in training set, and assessed by tenfold cross-validation and external test data set. After systematic selection of 93 metabolites, a biomarker panel consisting of Ala, Arg, Gly, Orn, Tyr/Cit, Val/Phe, C4-OH, C5/C3, C10:2 shows the potential to distinguish patients with GC from healthy individuals in tenfold cross-validation model (sensitivity: 0.8750, specificity: 0.9006) and test set (sensitivity: 0.9545, specificity: 0.8636). This metabolomic analysis makes contribution to the identification of disease-associated biomarkers and to the development of new diagnostic tools for patients with GC.
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