ArticleFrontiers in pharmacology2025
Multi-tissue metabolomics analysis reveals susceptible factors for chemotherapy-induced hepatotoxicity in colorectal cancer patients.
Article in Frontiers in pharmacology, 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 clinical and cellular lipidomics identifies a serum metabolite signature predictive of oxaliplatin resistance in colorectal cancer.Functional & integrative genomics · 2026Article
- Development and validation of a plasma-urine metabolism diagnostic model for renal cell carcinoma using machine learning.World journal of urology · 2025Article
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Authors and funding
16 authors.
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Abstract
Amis: Chemotherapy-induced hepatotoxicity (CIH) is a significant concern in colorectal cancer (CRC) patients treated with the CAPEOX (capecitabine and oxaliplatin) regimen. Identifying predictive factors for CIH is crucial for clinical management. Patients and Methods: This study analyzed colorectal tissue (CRT), plasma, and urine samples from CRC patients. Differentially expressed metabolites (DEMs) across these tissues were integrated for multi-omics analysis, and predictive models for CIH susceptibility were developed. An independent set of 75 plasma samples was used for validation. Results: A total of 492 differentially expressed compounds were identified in samples from 63 CRC patients, including 105, 149, and 238 DEMs in CRT, plasma, and urine, respectively. Lipids and lipid-like molecules were predominant in all samples. Among these, urine samples exhibited the highest variability and provided the strongest predictive power for CIH susceptibility. Principal component analysis (PCA) effectively differentiated normal patients from those with CIH. The study revealed steatosis as the primary pathological feature of CIH, with disrupted lipid metabolism emerging as a key characteristic. Predictive models constructed from multi-tissue metabolites profile exhibited high accuracy, with the plasma model achieving an AUC of 0.933 in external validation set. Our study underscores the importance of individual metabolic variations in CIH susceptibility, reflecting the complex interplay of genetic, environmental, and lifestyle factors. Conclusion: This study emphasizes the critical role of alterations in lipid, polyamine, and purine metabolism, as well as impaired tissue repair mechanisms, were identified as key endogenous factors underlying CIH susceptibility. The developed predictive models demonstrate potential for clinical application in assessing CIH risk in CRC patients undergoing CAPEOX chemotherapy.
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