GRAPHICAL ABSTRACT

INTRODUCTION

Liver cancer is the sixth most commonly diagnosed cancer and the third leading cause of cancer-related mortality worldwide by 2022.¹ Among the liver cancer subtypes, hepatocellular carcinoma (HCC) is the most prevalent, accounting for approximately 75-85% of liver cancer cases.¹ Notably, almost 90% of HCC cases occur in the context of chronic liver disease, with cirrhosis being the main risk factor for development.² Therefore, any condition that causes chronic hepatic injury leading to cirrhosis is considered to be a risk factor for HCC.^2,3 Chronic hepatitis B virus (HBV) and hepatitis C virus (HCV) are the primary etiological agents; however, alcoholic liver disease and non-alcoholic fatty liver disease also contribute significantly to the HCC burden.⁴

Early detection of HCC markedly improves overall survival.5 However, early stage liver cancer frequently manifests asymptomatically, leading to most patients being diagnosed with the disease at middle or advanced stages. Current screening methods, which primarily rely on liver ultrasound and serum alpha-fetoprotein (AFP) testing, have notable limitations.^6-8 Ultrasonography is highly operator-dependent and influenced by patient-related factors such as obesity and cirrhosis.⁹ Meanwhile, the sensitivity and specificity of AFP are insufficient, with approximately 40% of HCC cases remaining undetectable.^10-12 Although magnetic resonance imaging and computed tomography offer higher diagnostic accuracy, their cost limits accessibility in high-prevalence regions of HCC.^9,13,14 Consequently, the identification of novel biomarkers, especially those that indicate metabolic changes before symptom onset, is currently attracting attention for assessing the risk of liver cancer.

The liver serves as the metabolomic hub for carbohydrates, amino acids, bile acids, and lipids; thus, chronic liver diseases often disrupt these critical metabolic pathways.^15,16 Metabolomics provides a comprehensive snapshot of the metabolic state of an organism and enables the detection of metabolic changes associated with carcinogenesis.¹⁷ Therefore, studying metabolites related to liver cancer will facilitate the discovery of potential biomarkers and elucidate the underlying pathways and exposures involved in the disease process.

In this review, we systematically reviewed previously published studies and present a list of metabolites associated with liver cancer risk in general and potential effect modification by HBV and HCV infection. In addition, we report the diagnostic performance of metabolite-based models in identifying liver cancer. In particular, we present the metabolites that differentiate tumors from adjacent normal tissues in case-only studies. We then summarized the metabolites that significantly differed between liver cancer cases and non-liver cancer controls, with results specified according to the type of comparison group, including healthy controls, cirrhosis controls, and HBV/HCV-infected controls. Findings from nested case-control and cohort studies were then reported, focusing on metabolites that were prospectively associated with incident liver cancer. Finally, we highlight the performance of diagnostic metabolites when incorporated into predictive models to evaluate their potential clinical applications.

MATERIALS AND METHODS

This systematic review was reported followed the PRISMA 2020 guidelines (Supplementary Table 1).¹⁸ The study protocol was registered with PROSPERO (registration No. CRD42024596289).

Data source and study selection

We searched PubMed, Embase, and Web of Science over the past 20 years (from January 1, 2004 to September 19, 2024). The initial search was conducted with the strings “(metabolite OR metabolites OR metabolome OR metabolomic OR metabolomics) AND (liver cancer OR hepatocellular carcinoma),” restricted to titles and abstracts. After deduplication and exclusion based on the publication date and relevance, full-text articles were assessed for eligibility. Studies that met the inclusion criteria were included in the final systematic review. In addition, the reference lists of the included articles were manually screened to identify any further relevant studies.

Inclusion and exclusion criteria

The inclusion criteria were as follows: 1) original studies reporting metabolic features associated with liver cancer, 2) studies evaluating outcomes related to liver cancer risk, and 3) full-text articles published in English from 2004 onward. Exclusion criteria were review or commentary articles, non-clinical studies (in vitro or in vivo), publications in languages other than English, and studies published before 2004. Eligible articles were independently screened by BLTT and NHC. Any discrepancies were resolved by TH. Inter-reviewer agreement was 79% for abstract screening and 92% for full-text screening, indicating a high level of consistency between the reviewers.

Data extraction and quality evaluation

The following data were extracted from the studies: title, first author, publication year, country, study design, sample size, biospecimen type, metabolomics platform, HBV or HCV infection, targeted and untargeted metabolites that differentiated liver cancer patients from non-liver cancer controls, area under the receiver operating characteristic curve (AUC), sensitivity, and specificity.

For case-control and cohort studies, the risk of bias was assessed using the Newcastle-Ottawa scale (NOS), which evaluates three domains: selection of the study population (up to 4 points), comparability of groups (up to 2 points), and assessment of exposure or outcome (up to 3 points). The total NOS score ranged from 0 to 9, with higher scores indicating better methodological quality. For case-only studies (e.g., comparisons between tumor and adjacent normal tissue), NOS is not directly applicable; therefore, these studies were described qualitatively with attention to their design and reporting features.^19-21

Two reviewers (BLTT and NHC) independently extracted the data and assessed the study quality, and a third reviewer (TH) reviewed the results.

RESULTS

Study selection

A total of 4,927 records were initially identified through database searches, including PubMed (n=1,313), Embase (n=1,548), and Web of Science (n=2,066). After removing duplicates, excluding records published before 2004, and screening titles and abstracts for relevance, 4,602 records were excluded (Fig. 1). Subsequently, 325 articles were assessed for eligibility through full-text review, resulting in 96 studies involving 3,359 participants included in the final systematic review.^12,22-116 To ensure a comprehensive search, we also reviewed the bibliographies of relevant prior reviews.^117,118 However, no additional articles were identified through cross-referencing.

Study characteristics and quality assessment

The key characteristics of the included studies are summarized in Supplementary Table 2. These studies employed various designs, including case-only studies (10 studies),^22-31 case-control studies (52 studies),^12,32-82 nested case-control studies (eight studies),^83-90 cohort studies (25 studies),^91-115 and one nested cohort study.¹¹⁶ Most case-control studies were hospital-based, with the exception of one outpatient-based study.³⁹ Several cohort studies^{95,101,103,114} recruited additional participants for validation purposes. The number of HCC cases varied by study design: 5-80 cases in case-only studies, 7-224 cases in case-control studies, and 10-475 in nested case-control, cohort, and nested cohort studies. Studies with at least 100 cases were predominantly case-control and nested case-control studies.^{37,41,48,83-89}

Most studies employed targeted metabolomics (66 studies),^{23-26,28,29,31-35,37,39-43,45,47-49,51-53,55-58,60,61,63-65,67,68,70-72,75,77,78,80-82,84,86-88,90,92,93,96-103,105,108,110,112,113} while 21 used untargeted metabolomics,^{12,22,30,36,38,54,59,62,69,73,74,85,89,91,94,104,106,107,114-116} six studies utilized both targeted and untargeted approaches,^{27,50,76,79,95,111} and three others used a pseudotargeted metabolomics approach.^44,66,83

The majority of the studies were conducted in China (40 studies), followed by the USA (10 studies), Taiwan (four studies), and Egypt (four studies). Other countries included Korea (three studies), France (three studies), Germany (two studies), and Japan (two studies). Studies were also conducted in Nigeria, Gambia, the Czech Republic, and Singapore, each contributing one study. In addition, multiple studies have been conducted across various hospitals in China, including notable locations such as Shanghai, Dalian, and Beijing, and several European countries (including Italy, Spain, Poland, the UK, Romania, and Bangladesh) each contributed one study.

Metabolomic analyses were performed on various of biospecimens: serum (47 studies),^{12,24,32,34,36-38,41,43,45-47,49,52,55,57-60,63,64,66,69-74,76,79-81,83,84,87,89,92,93,95,103,105,107,109,110,113,115} plasma (18 studies),^{33,42,54,58,67,68,75,85,86,88,90,94,96,104,106,108,111,112} tissue (nine studies),^{23,25-29,31,65,116} urine (nine studies),^{39,44,48,53,76,97-100} saliva (one study),⁶² blood (one study),³⁵ feces (two studies),^50,82 and gallbladder bile (one study).⁵¹ Some studies used combinations of sample types, such as serum and tissue (three studies),^22,30,78 serum and plasma (one study),⁵⁶ serum and urine (one study),⁶¹ saliva and plasma (one study),⁶² serum, plasma, and tissue (one study),¹⁰¹ and serum, tissue, and stool (one study).¹¹⁴

Regarding analytical methods, most studies used liquid chromatography- mass spectrometry (LC-MS) (55 studies), followed by proton nuclear magnetic resonance (¹H-NMR) (14 studies), gas chromatography-mass spectrometry (GC-MS) (13 studies), combined LC-MS and GC-MS (nine studies), and combined ¹H-NMR and LC-MS (two studies). The seropositivity rates for HBV and HCV were reported in 15 of the 96 studies reviewed in this section.

Supplementary Table 3 presents the quality assessment of the included case-control studies, most of which were of moderate quality, with scores ranging from 5 to 6. Supplementary Table 4 summarizes the evaluation of cohort studies, which were generally of high quality, with scores between 7 and 8.

Targeted and untargeted metabolites related to liver risk

Summary of metabolites significantly associated with liver cancer from case-only studies

Supplementary Table 5 lists the metabolites with higher or lower concentrations in HCC tumors and non-tumors than their counterparts, stratified by the metabolomics approach. In the case-only studies, several differential metabolic changes were consistently identified in HCC tumor vs. non-tumor comparisons in at least four studies. Among these metabolites, lactate (reported in five studies)^22-24,27,29 and glutamate (reported in four studies),^22,24,27,29 were reported to be higher in HCC tumor than non-tumor cases. In contrast, malate was reported to be lower in tumor than non-tumor HCC cases in four studies.^22,23,25,27

Summary of metabolites significantly associated with liver cancer from case-control studies

Supplementary Table 6 lists metabolites significantly associated with liver cancer from case-control studies. Studies have used used various biological samples such as serum, plasma, urine, feces samples to observe the increase or decrease in metabolite concentrations between HCC and health controls, many studies have consistently reported significant increases in the concentrations of metabolites such as glycocholic acid (reported in nine studies),^{34,35,44,53,60,61,63,73,75} phenylalanine (reported in eight studies),^{40,54,66,68,70,71,73,75} tyrosine (reported in six studies),^{54,59,66,68,70,73} acetylcarnitine (reported in three studies),^48,63,71 taurochenodeoxycholic acid (TCDCA) (reported in four studies),^32,34-36 tautour-sodeoxycholic acid (TUDCA) (reported in four studies),^32,36,73,75 proline (reported in four studies),^39,59,70,81 linoleic acid (reported in four studies).^32,36,71,81 In contrast, lysophosphatidylcholine (LPC) (16:0) (reported in six studies),^{33,34,66,71,73,75} tryptophan (reported in six studies),^{37,44,60,61,66,72} LPC (14:0) (reported in five studies),^{49,66,73,75,81} LPC (18:0) (reported in four studies),^33,34,66,75 and leucine (reported in four studies)^44,54,61,66 were reported to be lower in HCC cases than in healthy controls.

Cirrhosis is considered a major risk factor for the development of HCC. Therefore, early diagnosis of potential HCC in patients with cirrhosis is urgently required. Many metabolomic studies have described the differences in metabolic profiles between HCC and cirrhosis. Among them, carnitine was consistently upregulated in five studies,^{32,38,48,71,76} whereas glycodeoxycholic acid was consistently downregulated in four studies.^38,44,52,79

Given that HCC develops in the context of HBV and HCV, many metabolomic studies have focused on analyzing metabolite differences between HCC patients and HBV or HCV hepatitis. In one study, xanthosine and guanosine were found to be increased in the serum of HCC patients, while guanine and inosine were lower in HCC patients than in HBV individuals. This shows that purine metabolism may alter as the disease progresses from chronic HBV to HCC.⁶⁴ Additionally, phenylalanine, malic acid, and 5-methoxytryptamine have been reported as metabolites capable of distinguishing HBV-infected individuals from healthy controls.⁵⁹ Regarding HCV, another study showed that 48 metabolites were significantly different between the HCC and HCV groups, and 31 of these compounds were endogenous.⁴³ Of these, five were characteristic of liver disease, including xanthine, uric acid, cholyglycine, D-leucic acid, and 3-hydroxycapric acid, which were decreased in HCC compared to HCV.⁴³ Additionally, N-fructosyl tyrosine, hydroxyindoleacetic acid,⁷⁴ choline, and valine⁴⁷ were reported to be increased in HCC, whereas L-aspartyl-L-phenylalanine, thyroxine,⁷⁴ and creatinine⁴⁷ were decreased in HCC compared with HCV individuals.

Summary of metabolites significantly associated with liver cancer from nested case-control, cohort, and nested cohort studies

In nested case-control studies, patients with liver cancer showed increases in tyrosine (reported in five studies)^84-86,89,90 and TCDCA (reported in four studies) (Supplementary Table 7).^83,85,86,88 Tyrosine was also reported to be associated with liver cancer in three cohort studies.^110,113,116

Model performance of metabolites in early detection of liver cancer

Tables 1-4 summarizes the diagnostic performance of metabolites proposed as biomarkers to differentiate different stages of liver cancer progression, with a total of 38 studies, including three case-only studies (Table 1),^{22,26,30 22} case-control studies (Table 2),^{35,36,38,39,41-43,47,49,55,57,59,64,67-69,71,72,74,76,77,81} three nested case-control studies (Table 3),^83,85,86 and 10 cohort studies (Table 4).^{92-96,102,106,107,109,115} Studies reported model performance metrics including AUC, sensitivity, and specificity, using various discrimination models such as logistic regression, orthogonal partial least squares discriminant analysis, partial least squares discriminant analysis, linear discriminant analysis, orthogonal signal-corrected partial least squares, and support vector machine.

Several metabolites have been consistently reported to distinguish HCC patients from healthy controls and HBV/HCV-infected patients. Notably, xanthine demonstrated strong discriminatory performance in two contexts: it was part of a seven-metabolite panel that distinguished HCC from HCV patients with an AUC of 0.93, sensitivity of 92%, and specificity of 95%.⁴³ It was also combined with guanine to differentiate HCC from HBV-infected controls with an AUC of 0.885, sensitivity of 75%, and specificity of 92.3%.⁶⁴ Similarly, glycocholate emerged as a shared biomarker, significantly discriminating HCC from healthy controls in one study⁸³ and appeared in diagnostic panels that distinguished HCC from HBV/HCV-infected controls.¹¹⁵ Taurocholic acid also consistently differentiated HCC from healthy individuals³⁵ and showed altered levels in comparisons between the HCC and HBV/HCV groups.¹¹⁵ Thus, xanthine, glycocholate, and taurocholic acid represent promising shared metabolites for distinguishing HCC patients from both healthy and HBV/HCV-infected patients.

Several key metabolites were also shared in distinguishing HCC patients from both healthy controls and patients with cirrhosis. Acetylcarnitine, identified in a case-only study, effectively differentiated HCC from healthy controls with an AUC of 0.803, and from cirrhotic patients with an AUC of 0.808, maintaining good sensitivity (72-74%) and specificity (75-79%).²² Glycine is another important shared metabolite, contributing to an eight-metabolite panel that achieved an AUC of 1.00 for distinguishing HCC from healthy individuals,²⁹ and individually achieved an AUC of 0.951 (sensitivity 81.82%, specificity 100%) for differentiating HCC from cirrhosis.⁶⁷ Phytosphingosine demonstrated strong discriminatory ability in both comparisons, achieving AUCs ranging from 0.917 to 1.000, with sensitivities and specificities of up to 100%.^69,71 Formate, another metabolite, showed excellent diagnostic performance, reaching an AUC of 1.000 in distinguishing HCC from healthy controls, and 0.995 in the second validation set against cirrhosis.⁷¹ Additionally, glycochenodeoxycholic acid was elevated in HCC patients compared to both healthy individuals and cirrhotic patients, supporting its role as a shared biomarker.^35,115 Finally, the dipeptide phenylalanyl-tryptophan (Phe-Trp) consistently differentiated HCC from healthy controls and cirrhosis, outperforming AFP in both comparisons (AUCs of 0.875 and 0.807, respectively).⁸³ These metabolites, including acetylcarnitine, glycine, phytosphingosine, formate, glycochenodeoxycholic acid, and Phe-Trp, demonstrated strong and reproducible diagnostic values in both healthy and cirrhotic patients.

Overall, individual metabolites such as acetylcarnitine, xanthine, glycine, glycocholate, taurocholic acid, phytosphingosine, and formate consistently showed strong potential as diagnostic biomarkers for HCC, both in comparison to healthy controls and to clinically relevant at-risk groups, such as patients with cirrhosis or HBV/HCV infection. Combining these metabolites into multi-marker panels often further improves diagnostic accuracy, with several studies reporting AUCs ranging from 0.95 to 1.00, sensitivities between 84% and 100%, and specificities between 85% and 98%.^39,41,72,81 These findings suggest that metabolite-based approaches offer promising avenues for the early detection and risk stratification in liver cancer screening programs.

DISCUSSION

In a multi-omics framework, metabolomics represents the endpoint of biological processes, reflecting the molecular phenotype through comprehensive metabolomic profiling. It captures the integrated effects of genetic, environmental, and pathological factors.¹¹⁹ By leveraging advanced analytical technologies, such as MS and NMR, researchers can identify and quantify small-molecule metabolites in a variety of biological samples.¹²⁰ These include primary liquid-based sources, such as plasma, serum, urine, and other matrices, such as feces, tumor cells, and normal tissues.

Previous metabolomics studies have distinguished different forms of specific metabolites, such as gamma-glutamyl dipeptide, bile acids, fatty acids, glycocholic acid, sphingosine, and urinary carnitine.^{34,53,61,109,121,122} Notably, several of these metabolites have been reported to differentiate patients with liver cancer from non-cancer controls, providing potential insights into the metabolic changes associated with disease onset. However, the strength of the evidence varies, as many studies have been based on small case-control designs without independent validation. To provide a more balanced assessment, we highlighted metabolites that were replicated across multiple studies, supported by prospective or nested case-control data, and confirmed them in both discovery and validation cohorts. Among these, elevated circulating tyrosine has been one of the most consistently reported findings, with multiple prospective studies demonstrating higher levels in patients with HCC than in healthy controls.^{84-86,89,90,110,113,116} This accumulation likely reflects disrupted amino acid metabolism resulting from impaired liver function. Similarly, TCDCA showed consistent elevation across independent studies, with evidence from both LC-MS and ¹H-NMR platforms and across sample types (plasma and serum).^{83,85,86,88,90,113} These findings highlight the robustness and reproducibility of these associations.

In the study by Li et al.,⁸⁶ the authors applied least absolute shrinkage and selection operator (LASSO) regression to identify metabolites independently related to the risk of liver cancer and built a logistic regression model. The model, incorporating 10 key metabolites including tyrosine and TCDCA, achieved a high predictive with an AUC of 0.86 (range, 0.82-0.88). These results suggest that tyrosine and TCDCA not only reflect metabolic dysregulation but may also serve as viable candidates for clinical biomarker development.

Previous reviews have also explored metabolic biomarkers for HCC. For instance, Kimhofer et al.¹¹⁷ summarized promising proteomic and metabolomic markers from blood and urine samples, many of which overlapped with the findings in the present review. However, lactate, a metabolite identified in the study, may be due to differences in sample sources or selection criteria. The most recent review by Feng et al.¹¹⁸ synthesized data from 55 studies and similarly reported widespread metabolic alterations in HCC patients, reinforcing the general consistency of dysregulated metabolic pathways. However, these large-scale studies face challenges owing to the high heterogeneity across studies. Notably, only a few metabolites were consistently replicated across different biological matrices (urine, serum, plasma, tissue, and feces), reflecting the differences in the metabolic signatures of biological fluids. Furthermore, methodological factors may have influenced our results. The instrument and technical platform used for metabolomic analysis (such as LC-MS, GC-MS, or ¹H-NMR) and study designs further contribute to inter-study inconsistency.

While observational studies have provided valuable data on the association between metabolites and liver cancer, evidence from these studies remains limited in their ability to infer causality due to the influence of confounding factors and reverse causality. Mendelian randomization offers a complementary approach that uses genetic variants as instrumental variables to strengthen causal inference. Our results are in accordance with the findings of a recent two-sample Mendelian randomization analysis by Tang et al.,¹²³ in which TCDCA, a prominent metabolite identified in our review, was also reported to be associated with an increased risk of liver cancer, providing further validation of its biological relevance.

In the context of improvements in analytical technologies and data availability, our study aimed to update and expand the current understanding of the metabolic landscape of liver cancer. This review provides evidence for the metabolic landscape of liver cancer. Tyrosine and TCDCA were among the most consistently reported metabolites and were validated in both the discovery and validation groups, reinforcing their potential utility as diagnostic or prognostic biomarkers.

Despite its strengths, this study had some limitations. First, owing to the high heterogeneity across studies, we were unable to conduct a meta-analysis of effect sizes for individual metabolites. Second, diverse analytical platforms have been employed, with MS, particularly when coupled to GC or LC, demonstrating high sensitivity, reproducibility, and broad metabolite coverage. In contrast, NMR offers high reproducibility and nondestructive sample treatment but suffers from lower sensitivity and challenges in quantification owing to overlapping signals from co-resonant metabolites.¹²⁴ Third, metabolomics investigations have been conducted on various types of biospecimens, and blood metabolomics has been widely used to identify cancer biomarkers and the ability to reflect systemic metabolic states.^124,125 In addition, tissue metabolomics provides a direct and fast diagnosis, while urine and fecal samples are the better choice for a non-invasive technique.¹²⁴ Fourth, many studies had small sample sizes, which reduced the statistical power. Fifth, some metabolites that distinguish HCC from both healthy controls and cirrhotic patients may also be elevated in cirrhosis, and the potential for false-positive classifications cannot be excluded. Therefore, future studies should include cirrhotic controls and longitudinal follow-up to assess whether these diagnostic metabolites also serve as prognostic biomarkers for recurrence or survival after treatment. Finally, the included studies were largely restricted to HBV, HCV, or cirrhosis controls in comparison with liver cancer cases and did not comprehensively cover other important etiologies such as non-alcoholic and alcoholic-related fatty liver diseases, which limits the generalizability of the metabolite findings across liver cancer development.

Metabolomics is the latest mature omics technique, but its potential in cancer research is rapidly emerging. Its capacity to detect subtle metabolic changes holds promise for early detection of liver cancer. Our review highlights the contribution of tyrosine and TCDCA to the development of liver cancer. Further studies should focus on standardizing analytical protocols, expanding sample sizes, and integrating metabolomics with other omics layers, such as genomics, transcriptomics, and proteomics. Such an integration will provide a more comprehensive understanding of liver cancer pathogenesis and enhance the accuracy and clinical relevance of metabolic biomarkers.

Article information

Conflicts of Interest

The authors have no conflicts of interests to declare.

Ethics Statement

This study was a systematic review of previously published literature and did not involve any new studies with human participants or animals performed by the authors. Therefore, ethical approval was not required for this study.

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: NHC, BLTT, TH

Data curation: NHC, BLTT, TH

Investigation: TH

Methodology: NHC, BLTT, TH

Writing - original draft: NHC, BLTT, TH

Writing - review & editing: BLTT, TH

Supplementary Materials

Figure 1.

PRISMA flowchart for study selection.

References

• 1. Bray F, Laversanne M, Sung H, Ferlay J, Siegel RL, Soerjomataram I, et al. Global cancer statistics 2022: GLOBOCAN estimates of incidence and mortality worldwide for 36 cancers in 185 countries. CA Cancer J Clin 2024;74:229−263.ArticlePubMedPMC
• 2. Lleo A, de Boer YS, Liberal R, Colombo M. The risk of liver cancer in autoimmune liver diseases. Ther Adv Med Oncol 2019;11:1758835919861914. ArticlePubMedPMCPDF
• 3. Jaber F, Cholankeril G, El-Serag HB. Contemporary epidemiology of hepatocellular carcinoma: understanding risk factors and surveillance strategies. J Can Assoc Gastroenterol 2024;7:331−345.ArticlePubMedPMCPDF
• 4. El-Serag HB, Kanwal F. Epidemiology of hepatocellular carcinoma in the United States: where are we? Where do we go? Hepatology 2014;60:1767−1775.ArticlePubMedPMC
• 5. Melendez-Torres J, Singal AG. Early detection of hepatocellular carcinoma: roadmap for improvement. Expert Rev Anticancer Ther 2022;22:621−632.ArticlePubMedPMC
• 6. Cao M, Li H, Sun D, He S, Yu Y, Li J, et al. Cancer screening in China: the current status, challenges, and suggestions. Cancer Lett 2021;506:120−127.ArticlePubMed
• 7. Sohn W, Lee YS, Lee JG, An J, Jang ES, Lee DH, et al. A survey of liver cancer specialists' views on the National Liver Cancer Screening Program in Korea. J Liver Cancer 2020;20:53−59.ArticlePubMedPMCPDF
• 8. Forner A, Reig M, Bruix J. Hepatocellular carcinoma. Lancet 2018;391:1301−1314.ArticlePubMedPMC
• 9. Colli A, Fraquelli M, Casazza G, Massironi S, Colucci A, Conte D, et al. Accuracy of ultrasonography, spiral CT, magnetic resonance, and alpha-fetoprotein in diagnosing hepatocellular carcinoma: a systematic review. Am J Gastroenterol 2006;101:513−523.PubMed
• 10. Piñero F, Dirchwolf M, Pessôa MG. Biomarkers in hepatocellular carcinoma: diagnosis, prognosis and treatment response assessment. Cells 2020;9:1370. ArticlePubMedPMC
• 11. Patel M, Shariff MI, Ladep NG, Thillainayagam AV, Thomas HC, Khan SA, et al. Hepatocellular carcinoma: diagnostics and screening. J Eval Clin Pract 2012;18:335−342.ArticlePubMed
• 12. Jiang Y, Tie C, Wang Y, Bian D, Liu M, Wang T, et al. Upregulation of serum sphingosine (d18:1)-1-P potentially contributes to distinguish HCC including AFP-negative HCC from cirrhosis. Front Oncol 2020;10:1759. ArticlePubMedPMC
• 13. Venook AP, Papandreou C, Furuse J, de Guevara LL. The incidence and epidemiology of hepatocellular carcinoma: a global and regional perspective. Oncologist 2010;15(Suppl 4): 5−13.ArticlePDF
• 14. Harris PS, Hansen RM, Gray ME, Massoud OI, McGuire BM, Shoreibah MG. Hepatocellular carcinoma surveillance: an evidence-based approach. World J Gastroenterol 2019;25:1550−1559.ArticlePubMedPMC
• 15. Fitian AI, Cabrera R. Disease monitoring of hepatocellular carcinoma through metabolomics. World J Hepatol 2017;9:1−17.ArticlePubMedPMC
• 16. Santos AA, Delgado TC, Marques V, Ramirez-Moncayo C, Alonso C, Vidal-Puig A, et al. Spatial metabolomics and its application in the liver. Hepatology 2024;79:1158−1179.ArticlePubMedPMC
• 17. Schmidt DR, Patel R, Kirsch DG, Lewis CA, Vander Heiden MG, Locasale JW. Metabolomics in cancer research and emerging applications in clinical oncology. CA Cancer J Clin 2021;71:333−358.ArticlePubMedPMCPDF
• 18. Page MJ, McKenzie JE, Bossuyt PM, Boutron I, Hoffmann TC, Mulrow CD, et al. The PRISMA 2020 statement: an updated guideline for reporting systematic reviews. BMJ 2021;372:n71. ArticlePubMedPMC
• 19. Lo CK, Mertz D, Loeb M. Newcastle-Ottawa scale: comparing reviewers' to authors' assessments. BMC Med Res Methodol 2014;14:45. ArticlePubMedPMCPDF
• 20. Ma LL, Wang YY, Yang ZH, Huang D, Weng H, Zeng XT. Methodological quality (risk of bias) assessment tools for primary and secondary medical studies: what are they and which is better? Mil Med Res 2020;7:7. ArticlePubMedPMCPDF
• 21. Wells GA, Shea B, O'Connell D, Peterson J, Welch V, Losos M, et al. The Newcastle-Ottawa scale (NOS) for assessing the quality of nonrandomised studies in meta-analyses [Internet]. Ottawa (CA): Ottawa Hospital Research Institute; [cited 2024 Nov 1]. Available from: https://ohri.ca/en/who-we-are/core-facilities-and-platforms/ottawa-methods-centre/newcastle-ottawa-scale
• 22. Lu Y, Li N, Gao L, Xu YJ, Huang C, Yu K, et al. Acetylcarnitine is a candidate diagnostic and prognostic biomarker of hepatocellular carcinoma. Cancer Res 2016;76:2912−2920.ArticlePubMedPDF
• 23. Morine Y, Utsunomiya T, Yamanaka-Okumura H, Saito Y, Yamada S, Ikemoto T, et al. Essential amino acids as diagnostic biomarkers of hepatocellular carcinoma based on metabolic analysis. Oncotarget 2022;13:1286−1298.ArticlePubMedPMC
• 24. Bai C, Wang H, Dong D, Li T, Yu Z, Guo J, et al. Urea as a by-product of ammonia metabolism can be a potential serum biomarker of hepatocellular carcinoma. Front Cell Dev Biol 2021;9:650748. ArticlePubMedPMC
• 25. Beyoğlu D, Imbeaud S, Maurhofer O, Bioulac-Sage P, Zucman-Rossi J, Dufour JF, et al. Tissue metabolomics of hepatocellular carcinoma: tumor energy metabolism and the role of transcriptomic classification. Hepatology 2013;58:229−238.ArticlePubMedPMC
• 26. Evangelista EB, Kwee SA, Sato MM, Wang L, Rettenmeier C, Xie G, et al. Phospholipids are a potentially important source of tissue biomarkers for hepatocellular carcinoma: results of a pilot study involving targeted metabolomics. Diagnostics (Basel) 2019;9:167. ArticlePubMedPMC
• 27. Tambay V, Raymond VA, Goossens C, Rousseau L, Turcotte S, Bilodeau M. Metabolomics-guided identification of a distinctive hepatocellular carcinoma signature. Cancers (Basel) 2023;15:3232. ArticlePubMedPMC
• 28. Lu X, Zhang X, Zhang Y, Zhang K, Zhan C, Shi X, et al. Metabolic profiling analysis upon acylcarnitines in tissues of hepatocellular carcinoma revealed the inhibited carnitine shuttle system caused by the downregulated carnitine palmitoyltransferase 2. Mol Carcinog 2019;58:749−759.ArticlePubMedPDF
• 29. Buchard B, Teilhet C, Samarakoon NA, Massoulier S, Joubert-Zakeyh J, Blouin C, et al. Two metabolomics phenotypes of human hepatocellular carcinoma in non-alcoholic fatty liver disease according to fibrosis severity. Metabolites 2021;11:54. ArticlePubMedPMC
• 30. Han J, Han ML, Xing H, Li ZL, Yuan DY, Wu H, et al. Tissue and serum metabolomic phenotyping for diagnosis and prognosis of hepatocellular carcinoma. Int J Cancer 2020;146:1741−1753.ArticlePubMedPDF
• 31. Ferrarini A, Di Poto C, He S, Tu C, Varghese RS, Balla AK, et al. Metabolomic analysis of liver tissues for characterization of hepatocellular carcinoma. J Proteome Res 2019;18:3067−3076.ArticlePubMedPMC
• 32. Lin X, Yang F, Zhou L, Yin P, Kong H, Xing W, et al. A support vector machine-recursive feature elimination feature selection method based on artificial contrast variables and mutual information. J Chromatogr B Analyt Technol Biomed Life Sci 2012;910:149−155.ArticlePubMed
• 33. Patterson AD, Maurhofer O, Beyoglu D, Lanz C, Krausz KW, Pabst T, et al. Aberrant lipid metabolism in hepatocellular carcinoma revealed by plasma metabolomics and lipid profiling. Cancer Res 2011;71:6590−6600.ArticlePubMedPMCPDF
• 34. Yin P, Wan D, Zhao C, Chen J, Zhao X, Wang W, et al. A metabonomic study of hepatitis B-induced liver cirrhosis and hepatocellular carcinoma by using RP-LC and HILIC coupled with mass spectrometry. Mol Biosyst 2009;5:868−876.ArticlePubMedPDF
• 35. Zhang X, Yang Z, Shi Z, Zhu Z, Li C, Du Z, et al. Analysis of bile acid profile in plasma to differentiate cholangiocarcinoma from benign biliary diseases and healthy controls. J Steroid Biochem Mol Biol 2021;205:105775. ArticlePubMed
• 36. Zhao T, Liang Y, Zhen X, Wang H, Song L, Xing D, et al. Analysis of serum exosome metabolites identifies potential biomarkers for human hepatocellular carcinoma. Metabolites 2024;14:462. ArticlePubMedPMC
• 37. Ko BS, Liang SM, Chang TC, Wu JY, Lee PH, Hsu YJ, et al. Association of tumor hydroxyindole O-methyltransferase and serum 5-methoxytryptophan with long-term survival of hepatocellular carcinoma. Cancers (Basel) 2021;13:5311. ArticlePubMedPMC
• 38. Rashid MM, Varghese RS, Ding Y, Ressom HW. Biomarker discovery for hepatocellular carcinoma in patients with liver cirrhosis using untargeted metabolomics and lipidomics studies. Metabolites 2023;13:1047. ArticlePubMedPMC
• 39. Osman D, Ali O, Obada M, El-Mezayen H, El-Said H. Chromatographic determination of some biomarkers of liver cirrhosis and hepatocellular carcinoma in Egyptian patients. Biomed Chromatogr 2017;31:e3893.ArticlePDF
• 40. Lin X, Zhang Y, Ye G, Li X, Yin P, Ruan Q, et al. Classification and differential metabolite discovery of liver diseases based on plasma metabolic profiling and support vector machines. J Sep Sci 2011;34:3029−3036.ArticlePubMed
• 41. Lu X, Nie H, Li Y, Zhan C, Liu X, Shi X, et al. Comprehensive characterization and evaluation of hepatocellular carcinoma by LC-MS based serum metabolomics. Metabolomics 2015;11:1381−1393.ArticlePDF
• 42. Kralova K, Vrtelka O, Fouskova M, Smirnova TA, Michalkova L, Hribek P, et al. Comprehensive spectroscopic, metabolomic, and proteomic liquid biopsy in the diagnostics of hepatocellular carcinoma. Talanta 2024;270:125527. ArticlePubMed
• 43. Bowers J, Hughes E, Skill N, Maluccio M, Raftery D. Detection of hepatocellular carcinoma in hepatitis C patients: biomarker discovery by LC-MS. J Chromatogr B Analyt Technol Biomed Life Sci 2014;966:154−162.ArticlePubMedPMC
• 44. Shao Y, Zhu B, Zheng R, Zhao X, Yin P, Lu X, et al. Development of urinary pseudotargeted LC-MS-based metabolomics method and its application in hepatocellular carcinoma biomarker discovery. J Proteome Res 2015;14:906−916.ArticlePubMed
• 45. Powell H, Coarfa C, Ruiz-Echartea E, Grimm SL, Najjar O, Yu B, et al. Differences in prediagnostic serum metabolomic and lipidomic profiles between cirrhosis patients with and without incident hepatocellular carcinoma. J Hepatocell Carcinoma 2024;11:1699−1712.ArticlePubMedPMCPDF
• 46. Zhang Y, Ding N, Cao Y, Zhu Z, Gao P. Differential diagnosis between hepatocellular carcinoma and cirrhosis by serum amino acids and acylcarnitines. Int J Clin Exp Pathol 2018;11:1763−1769.PubMedPMC
• 47. Wei S, Suryani Y, Gowda GA, Skill N, Maluccio M, Raftery D. Differentiating hepatocellular carcinoma from hepatitis C using metabolite profiling. Metabolites 2012;2:701−716.ArticlePubMedPMC
• 48. Ladep NG, Dona AC, Lewis MR, Crossey MM, Lemoine M, Okeke E, et al. Discovery and validation of urinary metabotypes for the diagnosis of hepatocellular carcinoma in West Africans. Hepatology 2014;60:1291−1301.ArticlePubMedPDF
• 49. Zhou PC, Sun LQ, Shao L, Yi LZ, Li N, Fan XG. Establishment of a pattern recognition metabolomics model for the diagnosis of hepatocellular carcinoma. World J Gastroenterol 2020;26:4607−4623.ArticlePubMedPMC
• 50. Cao H, Huang H, Xu W, Chen D, Yu J, Li J, et al. Fecal metabolome profiling of liver cirrhosis and hepatocellular carcinoma patients by ultra performance liquid chromatography-mass spectrometry. Anal Chim Acta 2011;691:68−75.ArticlePubMed
• 51. Gowda GAN, Shanaiah N, Cooper A, Maluccio M, Raftery D. Visualization of bile homeostasis using (1)H-NMR spectroscopy as a route for assessing liver cancer. Lipids 2009;44:27−35.ArticlePubMedPMC
• 52. Ressom HW, Xiao JF, Tuli L, Varghese RS, Zhou B, Tsai TH, et al. Utilization of metabolomics to identify serum biomarkers for hepatocellular carcinoma in patients with liver cirrhosis. Anal Chim Acta 2012;743:90−100.ArticlePubMedPMC
• 53. Zhang A, Sun H, Yan G, Han Y, Ye Y, Wang X. Urinary metabolic profiling identifies a key role for glycocholic acid in human liver cancer by ultra-performance liquid-chromatography coupled with high-definition mass spectrometry. Clin Chim Acta 2013;418:86−90.ArticlePubMed
• 54. Böhmová A, Mikoška M, Syslová K, Šindelářová D, Hříbek P, Urbánek P, et al. Untargeted metabolomics of blood plasma samples of patients with hepatocellular carcinoma. J Pharm Biomed Anal 2024;248:116263. ArticlePubMed
• 55. Zhang L, Wu GY, Wu YJ, Liu SY. The serum metabolic profiles of different Barcelona stages hepatocellular carcinoma associated with hepatitis B virus. Oncol Lett 2018;15:956−962.ArticlePubMedPMC
• 56. Shariff MIF, Kim JU, Ladep NG, Gomaa AI, Crossey MME, Okeke E, et al. The plasma and serum metabotyping of hepatocellular carcinoma in a Nigerian and Egyptian cohort using proton nuclear magnetic resonance spectroscopy. J Clin Exp Hepatol 2017;7:83−92.ArticlePubMedPMC
• 57. Baniasadi H, Gowda GA, Gu H, Zeng A, Zhuang S, Skill N, et al. Targeted metabolic profiling of hepatocellular carcinoma and hepatitis C using LC-MS/MS. Electrophoresis 2013;34:2910−2917.ArticlePubMedPMC
• 58. Liu HN, Wu H, Chen YJ, Tseng YJ, Bilegsaikhan E, Dong L, et al. Serum microRNA signatures and metabolomics have high diagnostic value in hepatocellular carcinoma. Oncotarget 2017;8:108810−108824.ArticlePubMedPMC
• 59. Gao R, Cheng J, Fan C, Shi X, Cao Y, Sun B, et al. Serum metabolomics to identify the liver disease-specific biomarkers for the progression of hepatitis to hepatocellular carcinoma. Sci Rep 2015;5:18175. ArticlePubMedPMCPDF
• 60. Zhou L, Wang Q, Yin P, Xing W, Wu Z, Chen S, et al. Serum metabolomics reveals the deregulation of fatty acids metabolism in hepatocellular carcinoma and chronic liver diseases. Anal Bioanal Chem 2012;403:203−213.ArticlePubMedPDF
• 61. Chen T, Xie G, Wang X, Fan J, Qiu Y, Zheng X, et al. Serum and urine metabolite profiling reveals potential biomarkers of human hepatocellular carcinoma. Mol Cell Proteomics 2011;10:M110.004945. ArticlePubMedPMC
• 62. Hershberger CE, Rodarte AI, Siddiqi S, Moro A, Acevedo-Moreno LA, Brown JM, et al. Salivary metabolites are promising non-invasive biomarkers of hepatocellular carcinoma and chronic liver disease. Liver Cancer Int 2021;2:33−44.ArticlePubMedPMCPDF
• 63. Li H, Fan SF, Wang Y, Shen SG, Sun DX. Rapid detection of small molecule metabolites in serum of hepatocellular carcinoma patients using ultrafast liquid chromatography-ion trap-time of flight tandem mass spectrometry. Anal Sci 2017;33:573−578.ArticlePubMedPDF
• 64. Zhang Y, Yang J, Wang J, Chen L, Huang H, Xiong Y, et al. Quantification of serum purine metabolites for distinguishing patients with hepatitis B from hepatocellular carcinoma. Bioanalysis 2019;11:1003−1013.ArticlePubMed
• 65. Stevens AP, Dettmer K, Kirovski G, Samejima K, Hellerbrand C, Bosserhoff AK, et al. Quantification of intermediates of the methionine and polyamine metabolism by liquid chromatography-tandem mass spectrometry in cultured tumor cells and liver biopsies. J Chromatogr A 2010;1217:3282−3288.ArticlePubMed
• 66. Chen S, Kong H, Lu X, Li Y, Yin P, Zeng Z, et al. Pseudotargeted metabolomics method and its application in serum biomarker discovery for hepatocellular carcinoma based on ultra high-performance liquid chromatography/triple quadrupole mass spectrometry. Anal Chem 2013;85:8326−8333.ArticlePubMed
• 67. Nomair AM, Madkour MA, Shamseya MM, Elsheredy HG, Shokr A. Profiling of plasma metabolomics in patients with hepatitis C-related liver cirrhosis and hepatocellular carcinoma. Clin Exp Hepatol 2019;5:317−326.ArticlePubMedPMC
• 68. Chen Y, Zhou J, Li J, Feng J, Chen Z, Wang X. Plasma metabolomic analysis of human hepatocellular carcinoma: diagnostic and therapeutic study. Oncotarget 2016;7:47332−47342.ArticlePubMedPMC
• 69. Wang S, He T, Wang H. Non-targeted metabolomics study for discovery of hepatocellular carcinoma serum diagnostic biomarker. J Pharm Biomed Anal 2024;239:115869. ArticlePubMed
• 70. Xiao R, Zhang X, Rong Z, Xiu B, Yang X, Wang C, et al. Non-invasive detection of hepatocellular carcinoma serum metabolic profile through surface-enhanced Raman spectroscopy. Nanomedicine 2016;12:2475−2484.ArticlePubMed
• 71. Liu Y, Hong Z, Tan G, Dong X, Yang G, Zhao L, et al. NMR and LC/MS-based global metabolomics to identify serum biomarkers differentiating hepatocellular carcinoma from liver cirrhosis. Int J Cancer 2014;135:658−668.ArticlePubMed
• 72. Zeng J, Yin P, Tan Y, Dong L, Hu C, Huang Q, et al. Metabolomics study of hepatocellular carcinoma: discovery and validation of serum potential biomarkers by using capillary electrophoresis-mass spectrometry. J Proteome Res 2014;13:3420−3431.ArticlePubMed
• 73. Jee SH, Kim M, Kim M, Yoo HJ, Kim H, Jung KJ, et al. Metabolomics profiles of hepatocellular carcinoma in a Korean prospective cohort: the Korean cancer prevention study-II. Cancer Prev Res (Phila) 2018;11:303−312.ArticlePubMedPDF
• 74. Kumari S, Ali A, Roome T, Razzak A, Iqbal A, Siddiqui AJ, et al. Metabolomics approach to understand the hepatitis C virus induced hepatocellular carcinoma using LC-ESI-MS/MS. Arab J Chem 2021;14:102907. Article
• 75. Du Z, Yin S, Liu B, Zhang W, Sun J, Fang M, et al. Metabolomics and network analysis uncovered profound inflammation-associated alterations in hepatitis B virus-related cirrhosis patients with early hepatocellular carcinoma. Heliyon 2023;9:e16083.ArticlePubMedPMC
• 76. Gong ZG, Zhao W, Zhang J, Wu X, Hu J, Yin GC, et al. Metabolomics and eicosanoid analysis identified serum biomarkers for distinguishing hepatocellular carcinoma from hepatitis B virus-related cirrhosis. Oncotarget 2017;8:63890−63900.ArticlePubMedPMC
• 77. Wu H, Xue R, Dong L, Liu T, Deng C, Zeng H, et al. Metabolomic profiling of human urine in hepatocellular carcinoma patients using gas chromatography/mass spectrometry. Anal Chim Acta 2009;648:98−104.ArticlePubMed
• 78. Zhu Y, Zhao Y, Ning Z, Deng Y, Li B, Sun Y, et al. Metabolic self-feeding in HBV-associated hepatocarcinoma centered on feedback between circulation lipids and the cellular MAPK/mTOR axis. Cell Commun Signal 2024;22:280. ArticlePubMedPMCPDF
• 79. Xiao JF, Varghese RS, Zhou B, Ranjbar MRN, Zhao Y, Tsai TH, et al. LC-MS based serum metabolomics for identification of hepatocellular carcinoma biomarkers in Egyptian cohort. J Proteome Res 2012;11:5914−5923.ArticlePubMedPMC
• 80. Nahon P, Amathieu R, Triba MN, Bouchemal N, Nault JC, Ziol M, et al. Identification of serum proton NMR metabolomic fingerprints associated with hepatocellular carcinoma in patients with alcoholic cirrhosis. Clin Cancer Res 2012;18:6714−6722.ArticlePubMedPDF
• 81. Lu Y, Huang C, Gao L, Xu YJ, Chia SE, Chen S, et al. Identification of serum biomarkers associated with hepatitis B virus-related hepatocellular carcinoma and liver cirrhosis using mass-spectrometry-based metabolomics. Metabolomics 2015;11:1526−1538.ArticlePDF
• 82. Zhang J, Yang S, Wang J, Xu Y, Zhao H, Lei J, et al. Integrated LC-MS metabolomics with dual derivatization for quantification of FFAs in fecal samples of hepatocellular carcinoma patients. J Lipid Res 2021;62:100143. ArticlePubMedPMC
• 83. Luo P, Yin P, Hua R, Tan Y, Li Z, Qiu G, et al. A large-scale, multicenter serum metabolite biomarker identification study for the early detection of hepatocellular carcinoma. Hepatology 2018;67:662−675.ArticlePubMedPMCPDF
• 84. Stepien M, Duarte-Salles T, Fedirko V, Floegel A, Barupal DK, Rinaldi S, et al. Alteration of amino acid and biogenic amine metabolism in hepatobiliary cancers: findings from a prospective cohort study. Int J Cancer 2016;138:348−360.ArticlePubMed
• 85. Hang D, Yang X, Lu J, Shen C, Dai J, Lu X, et al. Untargeted plasma metabolomics for risk prediction of hepatocellular carcinoma: a prospective study in two Chinese cohorts. Int J Cancer 2022;151:2144−2154.ArticlePubMedPDF
• 86. Li ZY, Shen QM, Wang J, Tuo JY, Tan YT, Li HL, et al. Prediagnostic plasma metabolite concentrations and liver cancer risk: a population-based study of Chinese men. EBioMedicine 2024;100:104990. ArticlePubMedPMC
• 87. Butler LM, Arning E, Wang R, Bottiglieri T, Govindarajan S, Gao YT, et al. Prediagnostic levels of serum one-carbon metabolites and risk of hepatocellular carcinoma. Cancer Epidemiol Biomarkers Prev 2013;22:1884−1893.ArticlePubMedPMCPDF
• 88. Stepien M, Lopez-Nogueroles M, Lahoz A, Kühn T, Perlemuter G, Voican C, et al. Prediagnostic alterations in circulating bile acid profiles in the development of hepatocellular carcinoma. Int J Cancer 2022;150:1255−1268.ArticlePubMedPDF
• 89. Stepien M, Keski-Rahkonen P, Kiss A, Robinot N, Duarte-Salles T, Murphy N, et al. Metabolic perturbations prior to hepatocellular carcinoma diagnosis: findings from a prospective observational cohort study. Int J Cancer 2021;148:609−625.ArticlePubMedPDF
• 90. Huang BY, Tsai MR, Hsu JK, Lin CY, Lin CL, Hu JT, et al. Longitudinal change of metabolite profile and its relation to multiple risk factors for the risk of developing hepatitis B-related hepatocellular carcinoma. Mol Carcinog 2020;59:1269−1279.ArticlePubMedPDF
• 91. Hershberger CE, Raj R, Mariam A, Aykun N, Allende DS, Brown M, et al. Characterization of salivary and plasma metabolites as biomarkers for HCC: a pilot study. Cancers (Basel) 2023;15:4527. ArticlePubMedPMC
• 92. Kim DJ, Cho EJ, Yu KS, Jang IJ, Yoon JH, Park T, et al. Comprehensive metabolomic search for biomarkers to differentiate early stage hepatocellular carcinoma from cirrhosis. Cancers (Basel) 2019;11:1497. ArticlePubMedPMC
• 93. Xiao J, Zhao Y, Varghese RS, Zhou B, Di Poto C, Zhang L, et al. Evaluation of metabolite biomarkers for hepatocellular carcinoma through stratified analysis by gender, race, and alcoholic cirrhosis. Cancer Epidemiol Biomarkers Prev 2014;23:64−72.ArticlePubMedPMCPDF
• 94. Ranjbar MRN, Luo Y, Di Poto C, Varghese RS, Ferrarini A, Zhang C, et al. GC-MS based plasma metabolomics for identification of candidate biomarkers for hepatocellular carcinoma in Egyptian cohort. PLoS One 2015;10:e0127299.ArticlePubMedPMC
• 95. Zhang S, Tuo P, Ji Y, Huang Z, Xiong Z, Li H, et al. Identification of 1-methylnicotinamide as a specific biomarker for the progression of cirrhosis to hepatocellular carcinoma. J Cancer Res Clin Oncol 2024;150:310. ArticlePubMedPMCPDF
• 96. Liu Z, Liu H, Chen Z, Deng C, Zhou L, Chen S, et al. Identification of a novel plasma metabolite panel as diagnostic biomarker for hepatocellular carcinoma. Clin Chim Acta 2023;543:117302. ArticlePubMed
• 97. Cox IJ, Aliev AE, Crossey MM, Dawood M, Al-Mahtab M, Akbar SM, et al. Urinary nuclear magnetic resonance spectroscopy of a Bangladeshi cohort with hepatitis-B hepatocellular carcinoma: a biomarker corroboration study. World J Gastroenterol 2016;22:4191−4200.ArticlePubMedPMC
• 98. Shariff MI, Kim JU, Ladep NG, Crossey MM, Koomson LK, Zabron A, et al. Urinary metabotyping of hepatocellular carcinoma in a UK cohort using proton nuclear magnetic resonance spectroscopy. J Clin Exp Hepatol 2016;6:186−194.ArticlePubMedPMC
• 99. Haznadar M, Diehl CM, Parker AL, Krausz KW, Bowman ED, Rabibhadana S, et al. Urinary metabolites diagnostic and prognostic of intrahepatic cholangiocarcinoma. Cancer Epidemiol Biomarkers Prev 2019;28:1704−1711.ArticlePubMedPMCPDF
• 100. Shariff MI, Gomaa AI, Cox IJ, Patel M, Williams HR, Crossey MM, et al. Urinary metabolic biomarkers of hepatocellular carcinoma in an Egyptian population: a validation study. J Proteome Res 2011;10:1828−1836.ArticlePubMed
• 101. Lewinska M, Santos-Laso A, Arretxe E, Alonso C, Zhuravleva E, Jimenez-Agüero R, et al. The altered serum lipidome and its diagnostic potential for non-alcoholic fatty liver (NAFL)-associated hepatocellular carcinoma. EBioMedicine 2021;73:103661. ArticlePubMedPMC
• 102. Grammatikos G, Schoell N, Ferreirós N, Bon D, Herrmann E, Farnik H, et al. Serum sphingolipidomic analyses reveal an upregulation of C16-ceramide and sphingosine-1-phosphate in hepatocellular carcinoma. Oncotarget 2016;7:18095−18105.ArticlePubMedPMC
• 103. Banales JM, Iñarrairaegui M, Arbelaiz A, Milkiewicz P, Muntané J, Muñoz-Bellvis L, et al. Serum metabolites as diagnostic biomarkers for cholangiocarcinoma, hepatocellular carcinoma, and primary sclerosing cholangitis. Hepatology 2019;70:547−562.ArticlePubMedPMCPDF
• 104. Li K, Shi W, Song Y, Qin L, Zang C, Mei T, et al. Reprogramming of lipid metabolism in hepatocellular carcinoma resulting in downregulation of phosphatidylcholines used as potential markers for diagnosis and prediction. Expert Rev Mol Diagn 2023;23:1015−1026.ArticlePubMed
• 105. Petrick JL, Florio AA, Koshiol J, Pfeiffer RM, Yang B, Yu K, et al. Prediagnostic concentrations of circulating bile acids and hepatocellular carcinoma risk: REVEAL-HBV and HCV studies. Int J Cancer 2020;147:2743−2753.PubMedPMC
• 106. Liang KH, Cheng ML, Lo CJ, Lin YH, Lai MW, Lin WR, et al. Plasma phenylalanine and glutamine concentrations correlate with subsequent hepatocellular carcinoma occurrence in liver cirrhosis patients: an exploratory study. Sci Rep 2020;10:10926. ArticlePubMedPMCPDF
• 107. Nenu I, Stefanescu H, Procopet B, Sparchez Z, Minciuna I, Mocan T, et al. Navigating through the lipid metabolism maze: diagnosis and prognosis metabolites of hepatocellular carcinoma versus compensated cirrhosis. J Clin Med 2022;11:1292. ArticlePubMedPMC
• 108. Lai MW, Chu YD, Hsu CW, Chen YC, Liang KH, Yeh CT. Multi-omics analyses identify signatures in patients with liver cirrhosis and hepatocellular carcinoma. Cancers (Basel) 2022;15:210. ArticlePubMedPMC
• 109. Wang B, Chen D, Chen Y, Hu Z, Cao M, Xie Q, et al. Metabonomic profiles discriminate hepatocellular carcinoma from liver cirrhosis by ultraperformance liquid chromatography-mass spectrometry. J Proteome Res 2012;11:1217−1227.ArticlePubMed
• 110. Fages A, Duarte-Salles T, Stepien M, Ferrari P, Fedirko V, Pontoizeau C, et al. Metabolomic profiles of hepatocellular carcinoma in a European prospective cohort. BMC Med 2015;13:242. ArticlePubMedPMCPDF
• 111. Di Poto C, Ferrarini A, Zhao Y, Varghese RS, Tu C, Zuo Y, et al. Metabolomic characterization of hepatocellular carcinoma in patients with liver cirrhosis for biomarker discovery. Cancer Epidemiol Biomarkers Prev 2017;26:675−683.ArticlePubMedPMCPDF
• 112. Weber S, Unger K, Alunni-Fabbroni M, Hirner-Eppeneder H, Öcal E, Zitzelsberger H, et al. Metabolomic analysis of human cirrhosis and hepatocellular carcinoma: a pilot study. Dig Dis Sci 2024;69:2488−2501.ArticlePubMedPMCPDF
• 113. Kim SJ, Jung CW, Anh NH, Yoon YC, Long NP, Hong SS, et al. Metabolic phenotyping combined with transcriptomics metadata fortifies the diagnosis of early-stage hepatocellular carcinoma. J Adv Res 2025;74:153−163.ArticlePubMedPMC
• 114. Liu J, Geng W, Sun H, Liu C, Huang F, Cao J, et al. Integrative metabolomic characterisation identifies altered portal vein serum metabolome contributing to human hepatocellular carcinoma. Gut 2022;71:1203−1213.ArticlePubMedPMC
• 115. Fitian AI, Nelson DR, Liu C, Xu Y, Ararat M, Cabrera R. Integrated metabolomic profiling of hepatocellular carcinoma in hepatitis C cirrhosis through GC/MS and UPLC/MS-MS. Liver Int 2014;34:1428−1444.PubMedPMC
• 116. Sanchez JI, Fontillas AC, Kwan SY, Sanchez CI, Calderone TL, Lee JL, et al. Metabolomics biomarkers of hepatocellular carcinoma in a prospective cohort of patients with cirrhosis. JHEP Rep 2024;6:101119. ArticlePubMedPMC
• 117. Kimhofer T, Fye H, Taylor-Robinson S, Thursz M, Holmes E. Proteomic and metabonomic biomarkers for hepatocellular carcinoma: a comprehensive review. Br J Cancer 2015;112:1141−1156.ArticlePubMedPMCPDF
• 118. Feng N, Yu F, Yu F, Feng Y, Zhu X, Xie Z, et al. Metabolomic biomarkers for hepatocellular carcinoma: a systematic review. Medicine (Baltimore) 2022;101:e28510.PubMedPMC
• 119. Chen Y, Wang B, Zhao Y, Shao X, Wang M, Ma F, et al. Metabolomic machine learning predictor for diagnosis and prognosis of gastric cancer. Nat Commun 2024;15:1657. ArticlePubMedPMCPDF
• 120. Wu X, Wang Z, Luo L, Shu D, Wang K. Metabolomics in hepatocellular carcinoma: from biomarker discovery to precision medicine. Front Med Technol 2023;4:1065506. ArticlePubMedPMC
• 121. Soga T, Sugimoto M, Honma M, Mori M, Igarashi K, Kashikura K, et al. Serum metabolomics reveals γ-glutamyl dipeptides as biomarkers for discrimination among different forms of liver disease. J Hepatol 2011;55:896−905.ArticlePubMed
• 122. Chen J, Wang W, Lv S, Yin P, Zhao X, Lu X, et al. Metabonomics study of liver cancer based on ultra performance liquid chromatography coupled to mass spectrometry with HILIC and RPLC separations. Anal Chim Acta 2009;650:3−9.ArticlePubMed
• 123. Tang X, Xue J, Zhang J, Zhou J. Causal effect of immunocytes, plasma metabolites, and hepatocellular carcinoma: a bidirectional two-sample Mendelian randomization study and mediation analysis in East Asian populations. Genes (Basel) 2024;15:1183. ArticlePubMedPMC
• 124. Yu J, Zhao J, Zhang M, Guo J, Liu X, Liu L. Metabolomics studies in gastrointestinal cancer: a systematic review. Expert Rev Gastroenterol Hepatol 2020;14:9−25.ArticlePubMed
• 125. Yin P, Lehmann R, Xu G. Effects of pre-analytical processes on blood samples used in metabolomics studies. Anal Bioanal Chem 2015;407:4879−4892.ArticlePubMedPMCPDF

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