ArticleBMC cancer2025
Serum untargeted metabolomics analysis of colorectal cancer.
Article in BMC cancer, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.
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Who cites it
1 citing paper in PubMed.
- Fusobacterium nucleatum, succinate signaling, and immunotherapy resistance in colorectal cancer: clinical relevance and translational opportunities.Clinical & translational oncology : official publication of the Federation of Spanish Oncology Societies and of the National Cancer Institute of Mexico · 2026Review
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6 authors.
Funding
Abstract
backgroundColorectal cancer (CRC) is a prevalent gastrointestinal tract disease with atypical manifestations. The objective of this study was to employ serum metabolomics for the identification of potential biomarkers in CRC diagnosis and treatment.
methodsA total of 136 healthy controls and 134 CRC patients were enrolled in our study. In total, 270 serum samples were analyzed using liquid chromatography–mass spectrometry (LC–MS) and divided into the discovery group and validation group. Meanwhile, we collected 65 CRC patients one week after surgery to systematically monitor postoperative metabolite changes. Statistical analysis and functional annotation were conducted to identify potential biomarker panels and altered metabolic pathways. Mfuzz expression pattern clustering analysis was employed to reveal the changing trends of metabolites across stages 1–4 of CRC.
resultsA total of 118 differential metabolites were identified in this study, demonstrating distinct separation between patients and controls based on serum metabolic profiles. Further functional annotation revealed associations between the differential metabolites and lipid metabolism, amino acid metabolism, nucleic metabolism, as well as pentose and glucuronate interconversions. Moreover, a panel consisting of Guanosine and Tyr Ser exhibited excellent predictive performance for CRC diagnosis with an AUC of 0.961 in the discovery group and an AUC of 0.948 in the validation group. The analysis of these differential metabolites in pre-and postoperative CRC serum samples showed that their levels returned to a healthy state after the operation, suggesting their potential specificity for CRC and highlighting their potential utility in monitoring therapeutic response. Furthermore, clustering analysis using Mfuzz identified dynamic metabolic expression patterns across CRC stages I to IV, with several metabolites exhibiting consistent upregulation or downregulation trends, implying a close association with CRC progression.
conclusionThis study highlighted significant differences in CRC serum metabolomic profiles, which may have potential value for distinguishing CRC patients from controls and for assessing pre- and post-operative states. The establishment of potentially diagnostic biomarker panels and alerted metabolic pathways provided valuable insights into deeper metabolic disorders associated with CRC. Our data showed that serum metabolomics might be used for CRC diagnosis, prognosis and classification.
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