Hoang T, et al. Infect Chemother. 2021 Mar;53(1):13-28. https://doi.org/10.3947/ic.2020.0136

Hoang T, et al. Infect Chemother. 2020 Sep;52(3):317-334. https://doi.org/10.3947/ic.2020.52.3.317

Backgrounds/Aims: Identifying metabolic biomarkers can enhance early detection and risk stratification of hepatocellular carcinoma (HCC). We conducted a two-sample Mendelian randomization (MR) study to assess the potential causal effects of metabolites on HCC risk.Methods: We performed meta-analyses to pool the effects of genetic instruments from 64 previously published genome-wide association studies. Summary statistics for HCC were obtained from a meta-analysis of the UK BioBank and FinnGen cohorts. MR analyses for the association between 3,275 metabolites and HCC risk were performed using inverse variance weighted, weighted median, MR-Egger, and MR-PRESSO methods to estimate the association. Enrichment analyses were performed on the significant metabolites to identify biological pathways associated with macronutrient intake.Results: We identified 99 metabolites that were positively and 36 metabolites that were negatively associated with HCC risk. Methyl glucopyranoside and phosphatidylcholine C38:3 were positively associated with HCC risk, whereas while 3-dehydrocarnitine and 10-undecenoate were inversely associated, with no evidence of heterogeneity, pleiotropy, or outlier effects for any of these associations. Pathway enrichment analysis showed that metabolites associated with increased HCC risk were primarily related to amino acid transport and solute carrier transporter disorders, whereas those linked to reduced risk were mainly involved in inositol and phosphatidylinositol metabolism, glycerophospholipid catabolism, and MeCP2-related regulatory processes.Conclusions: This comprehensive MR study identified several metabolites with potential causal roles in HCC development. Our findings highlight nutrient transport, lipid metabolism, and related regulatory mechanisms as key components of HCC pathogenesis, offering new avenues for biomarker discovery and therapeutic intervention.

Background: We conducted a Mendelian randomization (MR) study to investigate the relationship between genetically predicted metabolites and the risk of colorectal cancer (CRC) and to explore the underlying pathways. Methods: Genetic instruments for metabolite levels were selected based on data from 64 genome-wide association studies involving a total of 362 750 individuals. Using a two-sample MR approach, we assessed associations with CRC utilizing summary statistics from a meta-analysis of the UK Biobank and FinnGen. The primary analysis was conducted using the inverse-variance weighted method, with additional sensitivity analyses employing the median-weighted and MR-Egger methods to account for potential pleiotropy. The identified significant metabolites were further analyzed through enrichment analysis to explore the metabolic and lipid pathways involved. Results: Across the three MR methods, we identified 67 metabolites that were positively associated and 92 metabolites that were inversely associated with CRC risk. Among these, 7-methylguanine and creatinine are the most strongly connected metabolites. Enrichment analysis revealed that positively associated metabolites were significantly linked to pathways such as small nuclear ribonucleoprotein assembly, non-coding RNA metabolism, and gut-liver indole metabolism. In contrast, inversely associated metabolites were enriched in pathways related to the urea cycle, amino group metabolism, pyrimidine metabolism disorders, and pyrimidine catabolism. Conclusion: This study highlights genetically predicted metabolites associated with CRC risk, suggesting that metabolite panels and lipid-based biomarkers have potential for CRC risk assessment and early detection. However, further standardization and extensive validation are necessary before its clinical application.

Assessing the impact of metabolomic markers on gastric cancer risk: a two-sample Mendelian randomization study