Document Type : Review Article
Authors
1
Institute of Fundamental Medicine and Biology, Kazan Federal University, Kazan, Russia
2
Department of Pedagogy and Psychology , Urgench State University, Urgench, Uzbekistan
3
Department of Medicine , Mamun University, Khiva, Uzbekistan
4
Department of Medicine,Urgench Mamun University,Urgench, Uzbekistan
5
Department of Clinical Subjects,Tashkent State Medical University, Tashkent, Uzbekistan
6
Department of Psychological Sciences, Mamun University, Khiva, Uzbekistan
Abstract
Background: Esophageal cancer (EC) is a highly aggressive malignancy with increasing global prevalence, especially esophageal adenocarcinoma (EAC). Late-stage detection significantly contributes to its unfavorable outcomes, highlighting an urgent demand for non-invasive early diagnostic approaches. Metabolomics, the comprehensive study of small-molecule metabolites, provides a valuable strategy for discovering biomarker patterns that mirror the pathophysiological condition of cancer.
Objective: This review seeks to consolidate and critically assess existing research on the utility of plasma metabolites as diagnostic, prognostic, and predictive biomarkers in EC.
Methods: A systematic search of PubMed, Scopus, and Web of Science was performed for literature published between January 2000 and March 2024. Keywords such as "esophageal cancer," "metabolomics," "plasma," "serum," "biomarkers," "mass spectrometry," and "NMR" were employed. Studies were chosen based on their focus on plasma or serum metabolomic analysis in human EC patients.
Results: EC patients exhibit consistent changes in plasma metabolomic profiles compared to healthy individuals. Major disrupted pathways involve amino acid metabolism (e.g., increased branched-chain amino acids, reduced glutamine), energy metabolism (including the Warburg effect and disturbances in the TCA cycle), and lipid metabolism (alterations in phospholipid and sphingolipid concentrations). Panels comprising multiple metabolites show strong diagnostic performance, often with area under the curve (AUC) values above 0.90. Additionally, certain metabolic patterns may be useful for predicting patient outcomes and evaluating responses to neoadjuvant treatments.
Conclusion: Plasma metabolomics offers considerable potential to transform the clinical approach to EC through non-invasive methods for early diagnosis, risk assessment, and therapy evaluation. Validation through extensive, multi-center prospective studies is needed to implement these advances in clinical settings.
Keywords
