Systematic review of metabolomic profiles linked to liver cancer
- Author:
Bao Le Thai TRAN
1
;
Ngoc Hong CAO
;
Tung HOANG
Author Information
- Publication Type:Original Article
- From:Journal of Liver Cancer 2026;26(1):124-146
- CountryRepublic of Korea
- Language:English
-
Abstract:
Background:s/Aims: Increasing evidence indicates that metabolites play a significant role in the pathogenesis of liver cancer and have potential as biomarkers for early detection. This review summarizes the current literature on the utility of metabolomic profiling as a screening strategy for early diagnosis of liver cancer.
Methods:We searched PubMed, Embase, and Web of Science for studies published between 2004 and 2024 that examined metabolite alterations in liver cancer. The metabolites differentially expressed in liver cancer versus healthy controls, cirrhosis, and hepatic B virus cases are summarized. The diagnostic performance of the metabolite-based models was also evaluated, highlighting their potential as early detection biomarkers for liver cancer.
Results:A total of 96 studies were included in this review, encompassing case-only, case-control, nested case-control, and cohort designs. The analysis identified taurine and taurochenodeoxycholic acid to be consistently associated with an increased risk of liver cancer, supported by findings from both the discovery and validation cohorts. Notably, a diagnostic model incorporating 10 metabolites including taurine and taurochenodeoxycholic acid, achieved an area under the receiver operating characteristic curve of 0.86 (95% confidence interval, 0.82-0.88), indicating strong discriminatory power for early liver cancer detection. Nevertheless, heterogeneity across studies was observed, largely owing to differences in biological sample types and metabolomic platforms.
Conclusions:This review highlights the significant roles of taurine and taurochenodeoxycholic acid in liver cancer development. Future research should prioritize the standardization of analytical methodologies, increased sample sizes, and integration of metabolomics with other omics layers to enhance our understanding of liver cancer biology and improve biomarker accuracy and clinical utility.
