ArticleCancer medicine2024
Novel metabolic biomarker for early detection and diagnosis to the patients with gastric cardia adenocarcinoma.
Article in Cancer medicine, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers, 1 of them a synthesis that pooled it.
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Who cites it
8 citing papers in PubMed, 1 synthesis or guideline pooled it, 7 citations in OpenAlex.
- Bibliometric analysis of tumor marker application in gastric cancer diagnosis from 2019 to 2024.Frontiers in medicine · 2025Pooled it
- Research progress of machine learning applications in gastric cancer diagnosis and therapy.Clinical & translational oncology : official publication of the Federation of Spanish Oncology Societies and of the National Cancer Institute of Mexico · 2026Review
- Prognostic model for early gastric cardia cancer after endoscopic submucosal dissection.BMC surgery · 2025Article
- Beyond Biomarkers: Machine Learning-Driven Multiomics for Personalized Medicine in Gastric Cancer.Journal of personalized medicine · 2025Review
- Use of fecal microbiome to understand the impact of housing conditions on metabolic stress responses in farmed saltwater crocodiles (Frontiers in veterinary science · 2025Article
- Liquid Biopsy in the Clinical Management of Cancers.International journal of molecular sciences · 2024Review
- Novel metabolic biomarker for early detection and diagnosis to the patients with gastric cardia adenocarcinoma.Cancer medicine · 2024Article
- From multi-omics to predictive biomarker: AI in tumor microenvironment.Frontiers in immunology · 2024Review
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Authors and funding
18 authors at 4 institutions in 1 country.
Funding
Abstract
backgroundGastric cardia adenocarcinoma (GCA) is classified as Siewert type II adenocarcinoma at the esophagogastric junction in Western countries. The majority of GCA patients do not exhibit early warning symptoms, leading to over 90% of diagnoses at an advanced stage, resulting in a grim prognosis, with less than a 20% 5-year survival rate.
methodMetabolic features of 276 GCA and 588 healthy controls were characterized through a widely-targeted metabolomics by UPLC-MS/MS analysis. This study encompasses a joint pathway analysis utilizing identified metabolites, survival analysis in both early and advanced stages, as well as high and negative and low expression of HER2 immunohistochemistry staining. Machine learning techniques and Cox regression models were employed to construct a diagnostic panel.
resultsA total of 25 differential metabolites were consistently identified in both discovery and validation sets based on criteria of p < 0.05, (VIP) ≥ 1, and FC ≥ 2 or FC ≤ 0.5. Early-stage GCA patients exhibited a more favorable prognosis compared to those in advanced stages. HER2 overexpression was associated with a more positive outcome compared to the negative and low expression groups. Metabolite panel demonstrated a robust diagnostic performance with AUC of 0.869 in discovery set and 0.900 in validation set.
conclusionsA total of 25 common and stable differential metabolites may hold promise as liquid non-invasive indicators for GCA diagnosis. HER2 may function as a tumor suppressor gene in GCA, as its overexpression is associated with improved survival. The downregulation of bile acid metabolism in GCA may offer valuable theoretical insights and innovative approaches for precision-targeted treatments in GCA patients.
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