ArticleMetabolomics : Official journal of the Metabolomic Society2024
Multiple-matrix metabolomics analysis for the distinct detection of colorectal cancer and adenoma.
Article in Metabolomics : Official journal of the Metabolomic Society, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.
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Who cites it
3 citing papers in PubMed, 3 citations in OpenAlex.
- Noninvasive diagnosis of colorectal adenoma: The emerging potential of blood-based biomarkers.World journal of gastroenterology · 2026Review
- Macro- and metabolome-based characterization between gut microbiota and metabolites in patients with colorectal adenomas.Frontiers in microbiology · 2025Article
- Faecal Metabolome Profiles in Individuals Diagnosed with Hyperplastic Polyps and Conventional Adenomas.International journal of molecular sciences · 2024Article
Corrections and comments
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Authors and funding
8 authors at 1 institution in 1 country.
Funding
Abstract
objectivesAlthough colorectal cancer (CRC) is the leading cause of cancer-related morbidity and mortality, current diagnostic tests for early-stage CRC and colorectal adenoma (CRA) are suboptimal. Therefore, there is an urgent need to explore less invasive screening procedures for CRC and CRA diagnosis.
methodsUntargeted gas chromatography-mass spectrometry (GC-MS) metabolic profiling approach was applied to identify candidate metabolites. We performed metabolomics profiling on plasma samples from 412 subjects including 200 CRC patients, 160 CRA patients and 52 normal controls (NC). Among these patients, 45 CRC patients, 152 CRA patients and 50 normal controls had their fecal samples tested simultaneously.
resultsDifferential metabolites were screened in the adenoma-carcinoma sequence. Three diagnostic models were further developed to identify cancer group, cancer stage, and cancer microsatellite status using those significant metabolites. The three-metabolite-only classifiers used to distinguish the cancer group always keeps the area under the receiver operating characteristic curve (AUC) greater than 0.7. The AUC performance of the classifiers applied to discriminate CRC stage is generally greater than 0.8, and the classifiers used to distinguish microsatellite status of CRC is greater than 0.9.
conclusionThis finding highlights potential early-driver metabolites in CRA and early-stage CRC. We also find potential metabolic markers for discriminating the microsatellite state of CRC. Our study and diagnostic model have potential applications for non-invasive CRC and CRA detection.
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