ArticleFrontiers in oncology2025
Metabolomics for early-stage lung adenocarcinoma: diagnostic biomarker screening.
Article in Frontiers in oncology, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.
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Who cites it
2 citing papers in PubMed.
- Integration of blood protein-metabolic profiles via machine learning to enable the accurate early detection of non-small cell lung cancer.Respiratory research · 2026Article
- Comprehensive analysis of risk factors and metabolic profiling in preclinical atherosclerosis: a cross-sectional study.Frontiers in physiology · 2025Article
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4 authors.
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Abstract
Objective: This study aimed to identify specific metabolic markers in the blood that can diagnose early-stage lung adenocarcinoma. Methods: An untargeted metabolomics study was performed, and the participants were divided into four groups: early-stage lung adenocarcinoma group (E-LUAD; n = 21), healthy control group (HC, n = 17), non-cancerous lung disease group (NCC; n = 17), and advanced lung adenocarcinoma group (A-LUAD; n = 25). Plasma metabolite levels that differed in the E-LUAD group compared to the other three groups were identified via liquid chromatography-mass spectrometry (LC-MS). Principal component analysis (PCA) and partial least squares discriminant analysis (PLS-DA) were performed at metaX for statistical analysis. A Venn diagram was constructed to identify overlapping differential metabolites of the class comparisons. The data were randomly divided into a training set and a validation set. Based on the overlapping differential metabolites, the diagnostic model was constructed. The discrimination of the model was evaluated using the area under the curve (AUC). Results: A total of 527 metabolites were tentatively identified in positive ion mode and 286 metabolites in negative ion mode. Compared with the HC group, 121 differential metabolites were identified. Compared with the NCC group, 67 differential metabolites were identified. Compared with the A-LUAD group, 54 differential metabolites were identified. The Venn diagram showed that 29 metabolites can distinguish E-LUAD from HC and NCC and that four metabolites can distinguish E-LUAD from HC, NCC, and A-LUAD. The feature metabolites were selected to establish the diagnostic model for E-LUAD. The AUC value of the training set was 0.918, and it was 0.983 in the validation set. Conclusion: Blood metabolomics has potential diagnostic value for E-LUAD. More medical studies are needed to verify whether the metabolic markers identified in the current research can be applied in clinical practice.
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