GRAPHICAL ABSTRACT

INTRODUCTION

Liver cancer is the sixth most common malignant tumor and the third leading cause of cancer-related mortality worldwide, with 865,269 new cases and 757,948 deaths reported in 2022.¹ Hepatocellular carcinoma (HCC) accounts for approximately 90% of primary liver cancers, followed by intrahepatic cholangiocarcinoma and other less common liver malignancies.^2,3 Nearly 90% of HCC cases are linked to chronic viral hepatitis, excessive alcohol consumption, and non-alcoholic steatohepatitis.^2,3

Recent evidence highlights the critical role of metabolites, which are key substrates and products of metabolism essential for cellular processes, such as energy production, signal transduction, and apoptosis, in tumor development.^4-6 Metabolic alterations were reported to contribute to cancer-associated metabolic reprogramming, influencing gene expression, cellular differentiation, and the tumor microenvironment through six key mechanisms: 1) dysregulated glucose and amino acid uptake, 2) alternative nutrient acquisition pathways, 3) utilization of glycolysis/tricarboxylic acid cycle intermediates for biosynthesis and nicotinamide adenine dinucleotide phosphate production, 4) increased nitrogen demand, 5) metabolite-driven gene regulation, and 6) metabolic interactions within the tumor microenvironment.^5,6 Given their role in cancer progression, metabolites, particularly those detectable in plasma and serum, are promising biomarkers for screening, diagnosis, and assessment of therapeutic responses owing to their convenience and minimally invasive nature.⁷ However, metabolic heterogeneity is influenced by genetic mutations, cell and tissue origin, and epigenetic regulation.⁸

A previous observational study identified 150 metabolites that were significantly altered in patients with HCC compared with controls.⁹ Notably, a panel of six metabolites, including alpha-fetoprotein, 6-bromotryptophan, N-acetylglycine, salicyluric glucuronide, testosterone sulfate, and age, demonstrated strong discriminatory performance in distinguishing early stage HCC cases from controls.⁹ Furthermore, a recent meta-analysis of 68 cohort studies identified nearly 600 metabolites showing significant variations across different stages of liver disease progression from healthy controls to liver cirrhosis and HCC.¹⁰ Despite these findings, most studies remain observational, limiting their ability to establish causal relationships owing to confounding factors, reverse causation, and selection bias.¹¹ Furthermore, conventional epidemiological approaches and randomized controlled trials (RCTs) are yet to provide conclusive evidence on the role of metabolic markers in reducing the incidence of HCC. In this context, genome-wide association studies (GWAS) of metabolites offer an opportunity to identify genetic instruments for Mendelian randomization (MR), a method that can strengthen causal inference in the absence of large-scale RCTs.¹² Therefore, this study aimed to screen for genetically predicted metabolites associated with HCC development and progression, using robust genetic and metabolomic approaches.

MATERIALS AND METHODS

Study design

This study employed a two-sample MR design that relied on three key assumptions.¹³ First, the relevance assumption requires that the selected genetic variants are strongly associated with the metabolites.¹³ Second, the independence assumption states that there should be no unmeasured confounders affecting the relationship between genetic variants and HCC risk.¹³ Third, the exclusion restriction assumption ensures that genetic variants influence HCC risk solely through their effect on metabolites, with no alternative pathways involved.¹³

Data source

Exposure data summary statistics were retrieved from published GWAS on metabolites using the MetaboAnalyst database (https://www.mgwas.ca/mGWAS/upload/MGBrowseView.xhtml). We collected data for 337,881 genetic variants significantly associated with over 4,000 metabolite levels from 64 GWAS, excluding those from the UK Biobank. After removing variants with missing allele information and a zero-standard error, we performed a random-effects meta-analysis across 64 independent GWAS to derive pooled effects of each variant on metabolites, accounting for between-study variability. This integration across multiple cohorts, rather than relying on a single study, provided stronger and more reliable instrumental variables by increasing the sample size and enhancing the statistical power. Accordingly, summary statistics were obtained for 186,348 significant variants from which F-statistics were calculated; 183,631 strong instruments remained after excluding weak instruments (F-statistics ≤10). Additionally, the HCC meta- analysis integrated 32,342,897 variants from FinnGen and UK Biobank. By harmonizing the metabolite summary statistics with those for HCC through single nucleotide polymorphism (SNP)-based merging, we identified 147,562 variants that served as instrumental variables for assessing the MR association between 3,275 metabolites and HCC (Fig. 1).

Statistical analysis

For the primary analysis, we employed the inverse-variance weighted (IVW) method under the assumption of balanced pleiotropy.¹⁴ This approach utilized SNP-specific Wald estimates, which were calculated by dividing the SNP-outcome association by the SNP-exposure association. These estimates were then combined using a random effects model to account for potential heterogeneity. To ensure the robustness of our findings, we conducted additional sensitivity analyses using the weighted median method¹⁵ and the MR-Egger regression.¹⁶ While the IVW method provides the most precise estimates when all instrumental variables are valid, the weighted median method offers a compromise between efficiency and robustness, allowing for invalid instruments. Additionally, we applied MR-Egger regression to estimate the pleiotropy-adjusted causal effect estimate, which remains valid even when all instruments have direct effects on the outcome, although this method is less efficient than IVW.^14-16

We used multiple complementary approaches to assess horizontal pleiotropy. First, we calculated the MR-Egger intercept and its associated P-value, where a statistically significant deviation from zero indicated the presence of unbalanced horizontal pleiotropy.¹⁶ Second, we performed MR pleiotropy residual sum and outlier (MR-PRESSO) analyses, which detect and correct for horizontal pleiotropy by identifying outlier genetic variants.¹⁷ For this analysis, we reported the raw P-value for pleiotropy detection, outlier-corrected P-value after removing pleiotropic SNPs, and global test P-value, which evaluates the overall presence of horizontal pleiotropy in the instrument set. All MR analyses were performed using R statistical Software (R Foundation, Vienna, Austria).¹⁸

To further investigate the mechanisms linking metabolites to HCC, we conducted enrichment analyses of metabolic and lipid pathways related to HCC using an integrated KEGG pathway via the Human Metabolome Database, Reactome, and WikiPathways Databases. The analyses were performed using the online platform MetaboAnalyst (version 6.0). By utilizing a set of significant metabolites, we identified critical pathways that may play a role in HCC development and progression, offering valuable biological insights into the underlying processes.¹⁹

RESULTS

Metabolites positively associated with HCC risk and underlying pathways

Table 1 presents the associations between genetic predisposition to metabolites and increased risk of HCC. Using the IVW method, 257 metabolites were identified, 99 of which remained significant after sensitivity analyses using the median-weighted and MR-Egger methods.

Among the most significant metabolites, methyl glucopyranoside (α+β) showed a strong association with HCC risk (odds ratio [OR], 30.24; 95% confidence interval [CI], 19.65-46.54; false discovery rate [FDR] P=3.76×10^-54 by the IVW method) and demonstrated high instrument validity, with no evidence of heterogeneity (I²=0%, Cochran’s Q P>0.999) and no indication of directional horizontal pleiotropy (MR-Egger intercept P=0.087; MR-PRESSO global P>0.99). Similarly, phosphatidylcholine (acyl-alkyl) C38:3 was also strongly associated with increased HCC risk (OR, 1.62; 95% CI, 1.50-1.74; FDR P=1.07×10^-35), with no heterogeneity (I²=0%, Cochran’s Q P=0.994) and no evidence of directional horizontal pleiotropy (MR-Egger intercept P=0.101; MR-PRESSO global P=0.998) (Supplementary Table 1).

Enrichment analysis highlighted RaMB-DB pathways related to amino acid transport defects as the most significant pathways, followed by solute carrier (SLC) transporter disorders, amino acid transport across the plasma membrane, and Na⁺/Cl^- dependent neurotransmitter transporter pathways (Fig. 2).

Metabolites negatively associated with HCC risk and underlying pathways

Table 2 summarizes the associations between genetic predisposition to metabolites and a reduced risk of HCC. The IVW method identified 218 metabolites, with 36 remaining significant after sensitivity analyses using the median-weighted and MR-Egger methods.

Among the most significant metabolites, cholesteryl ester (20:4) showed a strong inverse association with HCC risk (OR, 0.02; 95% CI, 0.01-0.03; FDR P=7.77×10^-63 by the IVW method) and demonstrated high instrument validity, with no evidence of heterogeneity (I²=0%, Cochran’s Q P=0.996) and no indication of directional horizontal pleiotropy (MR-Egger intercept P=0.101; MR-PRESSO global P>0.999). Similarly, 10-undecenoate was also strongly associated with decreased HCC risk (OR, 0.93; 95% CI, 0.91-0.95; FDR P=4.36×10-8), with no heterogeneity (I²=0%, Cochran’s Q P>0.999) and no evidence of directional horizontal pleiotropy (MR-Egger intercept P=0.166; MR-PRESSO global P>0.999) (Supplementary Table 2).

Enrichment analysis revealed that inositol transporters were the most significant RaMB-DB pathway, followed by the synthesis of phosphatidylinositol, glycerophospholipid catabolism, and MeCP2 and associated Rett syndrome pathways (Fig. 3).

DISCUSSION

This study identified genetic predispositions to specific 135 metabolites that were significantly associated with either an increased or decreased risk of HCC. Among the most significant metabolites, methyl glucopyranoside (α+β) and phosphatidylcholine (acyl-alkyl) C38:3 were positively associated with HCC risk, whereas 3-dehydrocarnitine and 10-undecenoate were inversely associated, with all associations showing no evidence of heterogeneity, pleiotropy, or outlier influence. Enrichment analysis revealed that the shared significant metabolites associated with increased HCC risk were primarily related to amino acid transport and SLC transporter disorders, whereas those linked to reduced risk were mainly involved in inositol and phosphatidylinositol metabolism, glycerophospholipid catabolism, and MeCP2-related regulatory processes.

Recently, several studies have employed MR methods to investigate the relationship between metabolites and HCC. These studies utilized summary statistics data for metabolites derived from publicly available GWAS. For instance, Tang et al.²⁰ conducted an MR analysis using GWAS data encompassing 1,400 plasma metabolites and HCC. Their findings revealed that 36 metabolites were significantly associated with HCC in East Asians, including 21 that were linked to an increased incidence risk, while 15 were associated with overall HCC risk.²⁰ Similarly, a two-sample MR analysis was performed to examine 137 metabolites as potential causal mediators of five common gastrointestinal cancers.²¹ Their study showed that higher levels of isovalerylcarnitine were associated with an elevated risk of liver cancer in Europeans.²¹ More recently, Lin Ning conducted a comprehensive MR analysis to examine the associations between 1,400 systemic metabolites and the risk of HCC and cholangiocarcinoma.²² In this study, we identified 19 metabolites with significant links to these cancers. Among them, nine metabolites were associated with increased risk, including bilirubin (E, Z, Z, E), bilirubin (Z, Z) to taurocholate ratio, dimethylarginine (sdma+adma), N-methyltaurine, 4-vinylguaiacol sulfate, cholate-to-cAMP ratio, glycohyocholate, cholesterol, and 4-methylguaiacol sulfate.²² Additionally, ten metabolites, including ursodeoxycholate, 3-hydroxybutyroylglycine, linoleoylcholine, nonanoylcarnitine (C9), pristanate, heptenedioate (C7:1-DC), mannonate, N-acetyl-L-glutamine, sphinganine, and N-lactoyl isoleucine have been reported as protective metabolites.²² In contrast, a study by Ma used a bidirectional MR approach to assess the causal relationships between 21 amino acids and the risk of primary liver cancer.²³ These findings indicated no causal association between amino acids and primary liver cancer (PLC) risk, although PLC was found to influence serine concentration.²³ However, previous studies have notable limitations, primarily due to their reliance on summary statistics derived from a single GWAS for either metabolites or HCC. This approach may reduce the statistical power and limit the generalizability of the findings, especially when the sample sizes are relatively small. In contrast, our study addressed these limitations by integrating data from 64 independent GWASs for metabolites, thereby capturing a broader and more diverse range of genetic variation. Additionally, we utilized pooled GWAS data for HCC from both the UK Biobank and Finngen, which enhances the statistical power and reliability. This comprehensive approach strengthens the robustness of our findings and allows for more accurate identification of metabolite-HCC associations across populations.

Metabolic reprogramming has emerged as a central topic in cancer biology, reflecting dynamic alterations in cellular metabolism that support tumor growth and progression.^24-28 One of the earliest and most well-established examples is the Warburg effect, where cancer cells preferentially convert glucose into lactate through aerobic glycolysis, bypassing mitochondrial oxidative phosphorylation even in the presence of sufficient oxygen.^29,30 This metabolic shift not only facilitates rapid energy production, but also supports the biosynthetic and redox needs of proliferating tumor cells, underscoring its critical role in oncogenesis. In our study, methyl glucopyranoside level was positively associated with the risk of HCC. This metabolite has been previously linked to cancer biology, with significant differences observed between non-small cell lung cancer patients with poor and good survival outcomes in a cohort of 220 advanced-stage Caucasian patients,³¹ and MR analyses indicated a positive association with malignant neoplasms of bone and joint cartilage.³² Experimental evidence further supports its biological relevance, as methyl 6-O-cinnamoyl-α-D-glucopyranoside has been shown to ameliorate acute liver injury by inhibiting oxidative stress via Nrf2 signaling activation.³³ Similarly, our finding of a positive association between phosphatidylcholine (acyl-alkyl) C38:3 and HCC aligns with prior reports of elevated phosphatidylcholine levels in HCC patients compared with cirrhotic controls.³⁴ This observation was supported by the dysregulation of phosphatidylcholine synthesis enzymes in HCC, including the upregulation of choline kinase alpha and downregulation of phosphatidylethanolamine N-methyltransferase, the latter preferentially producing long-chain polyunsaturated phosphatidylcholines known for their anti-inflammatory properties.³⁴ A specific decrease in phosphatidylcholines in HCC tissues may reflect a disruption of protective lipid-mediated pathways, further highlighting the potential mechanistic links between altered lipid metabolism and hepatocarcinogenesis.³⁴

In contrast, our findings indicate that elevated 3-dehydrocarnitine levels are linked to a reduced likelihood of HCC. As a breakdown product of long-chain carnitines, higher 3-dehydrocarnitine levels may signal enhanced catabolism of these molecules, which normally transport fatty acids into mitochondria for energy generation.³⁵ The reduced availability of long-chain carnitines could limit the mitochondrial energy supply in cancer cells, potentially slowing their growth. This pattern of carnitine alteration aligns with reports that disturbances in fatty acid transport and metabolism are involved in HCC development.³⁵ In addition, our study found an inverse association between 10-undecenoate and HCC. Although direct evidence in the literature is limited, its related compound, undecylenic acid, an 11-carbon monounsaturated fatty acid derived from the distillation of castor oil via pyrolysis,³⁶ has been linked to both the Healthy Eating Index and Dietary Approaches to Stop Hypertension dietary indices,³⁷ which are associated with a lower risk of HCC.³⁸

In this study, enrichment analysis revealed that the top positively enriched metabolite pathways were largely associated with impaired SLC-mediated transport, a mechanism previously implicated in metabolic imbalance and liver tumorigenesis in earlier studies. SLC transporters are essential for maintaining the metabolic balance in the liver, and their dysregulation contributes to tumor progression.³⁹ Several SLCs are implicated in HCC: SLC26A6 is overexpressed and linked to cancer-related pathways, hOSCP1 variants increase non-viral liver cancer risk, SLC2A1 is upregulated while SLC2A2 is downregulated, correlating with prognosis, SLC13A5 supports energy homeostasis and hepatoma proliferation, SLCO2A1 is elevated in HCC, while SLCO1B1 expression decreases with cancer grade, and SLC46A3 shows increased expression in HCC.⁴⁰ In contrast, our study revealed that the metabolites negatively associated with HCC were mainly enriched in pathways related to inositol transport and phospholipid metabolism. This is supported by biological evidence showing that myo-inositol and its derivatives regulate cell signaling, lipid homeostasis, and hepatic insulin sensitivity, thereby exerting anti-inflammatory and antitumor effects in the liver.⁴¹ Epidemiological studies have also linked higher dietary inositol intake and better phospholipid profile with a reduced risk of non-alcoholic fatty liver disease,^42,43 and may thus lead to HCC development.

Despite its strengths, the main limitation is that our MR analyses mainly relied on GWAS summary statistics from European-ancestry populations, which may limit the generalizability to other ethnic groups given differences in metabolite profiles and HCC epidemiology. For example, with population-attributable fractions of more than 40% for hepatic B virus and 19% for alcohol consumption, chronic hepatitis B virus infection is more prevalent in East Asian populations (69%), whereas non-alcoholic fatty liver disease and alcohol-related liver disease predominate in Western cohorts (up to 35%), leading to different baseline HCC risks.^44-46 In addition, genetic variants influencing metabolite levels (e.g., SRD5A2 gene) may differ in allele frequency and linkage disequilibrium patterns across ancestries, potentially modifying SNP-metabolite effect estimates and altering the strength or direction of MR pathways.⁴⁷ Likewise, associations between genetic variants and HCC may vary with population-specific exposures, such as hepatic B virus prevalence, aflatoxin contamination, dietary patterns, and gut microbiome composition, affecting SNP-outcome estimates and the derived causal effects.⁴⁸ These cross-population differences in both the SNP-metabolite and SNP-HCC associations highlight the need for validation in non-European cohorts. However, large-scale metabolite and HCC GWAS in non-European populations remains scarce; future multi-ethnic and cross-ancestry MR studies are warranted to confirm and extend our findings. Finally, of the 475 metabolites identified as significant by the IVW after excluding weak instruments, only 135 remained in concordance with the weighted median and MR-Egger analyses. This stringent cross-method confirmation, while increasing confidence by reducing potential bias from horizontal pleiotropy, may also have excluded true associations with weaker or less consistent signals, potentially underestimating the full spectrum of metabolite-HCC relationships.

In summary, this study uncovered genetic predispositions to specific metabolites that were significantly associated with either an elevated or a reduced risk of HCC. Enrichment analysis indicated that metabolites associated with higher HCC risk were mainly involved in oxidative pathways, whereas those linked to lower risk were primarily related to transporter-mediated functions.

Article information

Acknowledgement

Tho Thi Anh Tran was funded by the Master’s, PhD Scholarship Program of Vingroup Innovation Foundation (VINIF), code VINIF.2023.TS.119.

Conflicts of Interest

The authors declare no competing interests.

Ethics Statement

This study utilized publicly available summary-level data and therefore did not require ethical approval.

Funding Statement

This research was also funded by Vietnam National University Ho Chi Minh City (VNU-HCM) under the grant number C2024-44-34.

Data Availability

The data that support the findings of this study are available from the corresponding author upon reasonable request.

Author Contributions

Conceptualization: TH, VMT, TTAT, BLTT, NHC

Data Curation: TH

Formal Analysis: TH

Methodology: TH, VMT, TTAT, BLTT, NHC

Writing - original draft: TH

Writing - review & editing: TH, BLTT, NHC

Supplementary Material

Figure 1.

Flowchart for identification of instrumental variables.

Figure 2.

Enriched pathways of metabolites positively associated with hepatocellular carcinoma. IEM, inborn errors of metabolism; SLC, solute carrier.

Figure 3.

Enriched pathways of metabolites negatively associated with hepatocellular carcinoma. PI, phosphatidylinositol; MECP2, methyl- CpG-binding protein 2; SLC, solute carrier.

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Figure & Data

References

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