Review Open Access
Copyright ©The Author(s) 2017. Published by Baishideng Publishing Group Inc. All rights reserved.
World J Hepatol. Jan 8, 2017; 9(1): 1-17
Published online Jan 8, 2017. doi: 10.4254/wjh.v9.i1.1
Disease monitoring of hepatocellular carcinoma through metabolomics
Asem I Fitian, School of Medicine, Virginia Commonwealth University, Richmond, VA 23298, United States
Roniel Cabrera, Department of Medicine, Division of Gastroenterology, Hepatology and Nutrition, Section of Hepatobiliary Diseases, University of Florida, Gainesville, FL 32610, United States
Author contributions: Fitian AI and Cabrera R conceptualized the study, searched and reviewed the literature, and drafted the manuscript; both authors reviewed and approved the final version of the manuscript.
Conflict-of-interest statement: Authors declare no conflicts of interest for this article.
Open-Access: This article is an open-access article which was selected by an in-house editor and fully peer-reviewed by external reviewers. It is distributed in accordance with the Creative Commons Attribution Non Commercial (CC BY-NC 4.0) license, which permits others to distribute, remix, adapt, build upon this work non-commercially, and license their derivative works on different terms, provided the original work is properly cited and the use is non-commercial. See:
Correspondence to: Roniel Cabrera, MD, MS, Department of Medicine, Division of Gastroenterology, Hepatology and Nutrition, Section of Hepatobiliary Diseases, University of Florida, 1600 SW Archer Rd M440, Gainesville, FL 32610, United States.
Telephone: +1-352-2739500 Fax: +1-352-3927393
Received: June 30, 2016
Peer-review started: June 30, 2016
First decision: August 18, 2016
Revised: September 20, 2016
Accepted: October 22, 2016
Article in press: October 24, 2016
Published online: January 8, 2017


We elucidate major pathways of hepatocarcinogenesis and accurate diagnostic metabolomic biomarkers of hepatocellular carcinoma (HCC) identified by contemporary HCC metabolomics studies, and delineate a model HCC metabolomics study design. A literature search was carried out on Pubmed for HCC metabolomics articles published in English. All relevant articles were accessed in full text. Major search terms included “HCC”, “metabolomics”, “metabolomics”, “metabonomic” and “biomarkers”. We extracted clinical and demographic data on all patients and consolidated the lead candidate biomarkers, pathways, and diagnostic performance of metabolomic expression patterns reported by all studies in tables. Where reported, we also extracted and summarized the metabolites and pathways most highly associated with the development of cirrhosis in table format. Pathways of lysophospholipid, sphingolipid, bile acid, amino acid, and reactive oxygen species metabolism were most consistently associated with HCC in the cited works. Several studies also elucidate metabolic alterations strongly associated with cirrhosis, with γ-glutamyl peptides, bile acids, and dicarboxylic acids exhibiting the highest capacity for stratifying cirrhosis patients from appropriately matched controls. Collectively, global metabolomic profiles of the referenced works exhibit a promising diagnostic capacity for HCC at a capacity greater than that of conventional diagnostic biomarker alpha-fetoprotein. Metabolomics is a powerful strategy for identifying global metabolic signatures that exhibit potential to be leveraged toward the screening, diagnosis, and management of HCC. A streamlined study design and patient matching methodology may improve concordance among metabolomic datasets in future works.

Key Words: Metabolomics, Hepatocellular carcinoma, Biomarkers, Metabolic profiling, Chromatography/mass spectrometry, Noninvasive biomarkers, Cirrhosis

Core tip: The high-throughput, validated nature of metabolomics makes it an ideal methodology for rapidly identifying the global metabolic alterations associated with hepatocarcinogenesis - alterations that not only enhance our understanding of the metabolic underpinnings of cirrhosis and hepatocellular carcinoma (HCC), but that can be leveraged to improve HCC diagnostic, therapeutic, and disease monitoring efficacy. Indeed, contemporary HCC metabolomics works time and again demonstrate this promise that metabolomics platforms hold in serving as standalone non-invasive HCC diagnostic and disease monitoring modalities.


Hepatocellular carcinoma (HCC) is the world’s third most lethal cancer, possessing a five-year survival rate of 10% that results in between 250000 to 1000000 deaths per year[1,2]. HCC culminates from a preexisting long-term condition of cirrhosis in 90% of cases[3] and cirrhosis patients are among the best characterized individuals at high risk for developing cancer. Notwithstanding the opportunity for clinical surveillance of these patients, the dismal survival rate persists and HCC has now emerged as the fastest rising cause of cancer related death in the United States[4,5]. HCC patients in Asia and sub-Saharan Africa face a particularly grim outlook, with > 90% of patients in rural areas of these regions progressing within the first year of HCC onset[6]. A major hindrance to successful early diagnosis of HCC stems from the substandard accuracy of the principal HCC diagnostic modalities. Alpha-fetoprotein (AFP) is the principal biomarker for HCC and despite the inexpensive and reproducible nature of the AFP blood test, its sensitivity of 25%-65%[7,8] for HCC has exacerbated early detection of this cancer. The low sensitivity of AFP can be explained by the fact that up to 40% of HCC and cirrhosis patients have normal AFP levels, and that AFP is often elevated in patients without HCC[9-11]. Moreover, only 10%-20% of patients with early-stage HCC have elevated AFP levels[12]. Exclusion of AFP as an HCC diagnostic modality in the AASLD guidelines for HCC surveillance underscores AFP’s unreliability to accurately screen for early HCC[13]. Efforts to overcome this substandard performance have resulted in the identification and commercialization of novel HCC biomarkers des-gamma-carboxyprothrombin and lectin-bound AFP (AFP-L3). These markers are ineffective when used alone as HCC biomarkers, however, and even when combined with AFP still demonstrate poor sensitivity for HCC, particularly in the detection of lesions < 3 cm[14]. While magnetic resonance imaging and computed tomography offer better accuracy in HCC diagnosis, these sophisticated diagnostic modalities are both economically and logistically incompatible with the resource-poor areas experiencing the brunt of HCC’s mortality rate[15-17]. Improving early HCC detection and patient outcome globally requires fulfilling of the urgent need for a reproducible, inexpensive, and accurate HCC diagnostic test.

Metabolomics as an HCC biomarker discovery tool

Numerous genomic and proteomic screening studies have been employed to identify potential biomarkers of HCC[18-24] but to date the markers identified in these studies have not been clinically fruitful. Because the liver is the hub of carbohydrate, amino acid, and lipid metabolism[25,26], chronic liver diseases undoubtedly disrupt normal metabolic function. A metabolomics analysis of HCC and cirrhotic tissue can therefore elucidate not just the metabolic pathways most relevant to the hepatocarcinogenic process, thereby identifying metabolites showing promise as HCC biomarkers, but also global metabolic patterns that serve as comprehensive disease signatures and which may therefore be used to stratify cases from controls. Metabolomics is the comprehensive identification of all small metabolites < 2 kD in a tissue sample. Through combined gas or liquid chromatography/mass spectrometry, surface-enhanced laser desorption/ionization time-of-flight mass spectrometry (SELDI TOF-MS) or nuclear magnetic resonance instrumentation, metabolomics platforms enable investigators to rapidly screen hundreds of metabolites in a large series of biofluid or solid tissue samples and are capable of simultaneously detecting metabolites belonging to a diverse array of pathways including amino acids, lipids, carbohydrates, and nucleotides. Metabolomics platforms are translationally optimal and hold potential for clinical implementation because they reveal global metabolite expression pattern differences among cases and controls in an automated, rapid, high-throughput, quality controlled, and reproducible manner. Metabolomics facilitates rapid identification of diagnostic markers, prognostic markers, and lead drug target pathways and their implementation in drug discovery divisions of pharmaceutical giants underscores their important role in the lead target generation realm[27].

Nearly two dozen HCC metabolomics studies have been reported[28-46]. These studies illustrate the key pathways involved in stepwise hepatocarcinogenesis and reveal metabolites that may have utility as biomarkers of HCC and cirrhosis. The findings of these studies are summarized in Tables 1 and 2. These works reveal deregulation of bile acid metabolism, fatty acid β-oxidation by way of the carnitine palmitoyltransferase (CPT) shuttle system, amino acid metabolism, and glycerophospholipid metabolism in HCC vs cirrhosis, and further delineate metabolites with potential utility in stratifying patients with cirrhosis from the healthy population.

Table 1 Significantly altered metabolites in hepatocellular carcinoma patients vs cirrhosis controls.
Ref.PlatformTissue (organism)HCC etiologySignificantly altered metabolites in HCC patients vs cirrhosis controlsMain pathways distinguishing HCC from cirrhosis
Patterson et al[29]UPLC/QTOF-MS; UPLC/ESI-TQMS; GC/MSPlasma (human)HCC n = 20:LPC (14:0) ↓LPC metabolism
EtOH n = 6LPC (18:1) ↓
HBV n = 3LPC (20:4) ↓
HCV n = 5LPC (20:3) ↓
NASH n = 3LPC (22:6) ↓
NASH/alcoholic steatohepatitis n = 1FFA (24:0) - lignoceric acid ↓Very long chain fatty acid metabolism
HH n = 2FFA (24:1) - nervonic acid ↓Hemoglobin metabolism
Bilirubin ↑
Biliverdin ↑
Xiao et al[37]UPLC/QTOF-MSSerum (human)HCV3β, 6β-dihydroxy-5β-cholan-24-oic acid ↓Bile acid biosynthesis
3α, 7β-dihydroxy-5β-cholest-24-en-26-oic acid ↓
Linoelaidyl carnitine ↓CPT shuttle system
Oleoylcarnitine ↓
Palmitoyl carnitine ↓
O-octanoyl-R-carnitine ↓
LPC (20:1) ↓LPC metabolism
LPC (20:4) ↓
PE (20:4/18:1) ↓
4E;15Z-Bilirubin IXa ↓Hemoglobin metabolism
15,16-dihydrobiliverdin ↓
3-ganidinopropionicacid ↓
Tetracosahexaenoic acid ↓Eicosanoid metabolism
3-hydroxy-eicosanoic acid ↓
Oleamide ↓
Phe-Phe ↑
Wang et al[38]UPLC/MS-MS; LC/QTOF-MSSerum (human)HCC n = 82:LPC-16:0 ↑LPC metabolism
HBV-cirrhosis n = 41LPC-18:0 ↑
HBV-only n = 4116:0/18:1-PC ↑
16:0/18:2-PC ↑
16:0/20:4-PC ↑
16:0/22:6-PC ↑
18:0/18:2-PC ↑
Phenylalanine ↓Gut flora metabolism
GCDCA ↓Bile acid metabolism
Canavaninosuccinate ↑↑Organic acid metabolism
Zhou et al[39]UPLC/QTOF-MSSerum (human)HCC n = 69:LPC (14:0) HCC-C ↓LPC metabolism
HBV-HCC (HCC-B) n = 38LPC (16:1) HCC-C ↓
HCV-HCC (HCC-C) n = 31LPC (18:3) HCC-B ↓
LPC (18:2) HCC-B ↓
LPC (18:1) HCC-B ↓
LPC (18:0) HCC-C ↓
LPC (20:5) HCC-C ↓
LPC (20:4) HCC-B ↓
LPC (20:3) HCC-B ↓
LPC (20:2) HCC-C ↓
LPC (20:0) HCC-B ↓
LPC (22:6) HCC-C ↓
LPC (22:5) HCC-B ↓
LPC (22:4) HCC-B ↓
Wu et al[31]SELDI-TOF-MS; HPLC/MSSerum (human)HBVGRO-α↑Cytokine
Thrombin light chain ↑Protease cleavage
Ressom et al[36]UPLC/QTOF-MSSerum (human)HCC n = 78:GDCA ↓Bile acid metabolism
→ HCV 67%TCA ↓
→ HBV 15%TCDCA ↓
→ Alcoholism 29%Sphingosine 1-phosphate ↑Sphingolipid metabolism
→ NASH 13%LPC (16:0) ↑LPC metabolism
→ Cryptogenic (8%)LPC (17:0) ↑
→ Autoimmune (3%)LPC (18:0) ↑
LPC (15:0) ↑
LPC (22:6) ↑
LPE (22:6) ↑
LPE (20:4) ↑
LPE (20:3) ↑
PS ↑
Yang et al[28]HRMAS 1H NMRBiopsy (human)HCC n = 17:Glucose ↓Glycolysis
→ Cirrhosis n = 9Creatine ↓
→ No cirrhosis n = 8PE ↑LPC metabolism
Glutamine ↑Amino acid metabolism
Glutamate ↑
PC + GPC ↑Bile acid metabolism
Nahon et al[40]NMRSerum (human)EtOH cirrhosisHigh density lipoproteinsHDL biosynthesis
Acetate ↑Ketone body metabolism
N-acetyl-glycoproteins ↑N-acetylglycoprotein
Glutamate ↑Amino acid metabolism
Glutamine ↓
Budhu et al[43]GC/MS, UPLC/MS-MSBiopsy samples (human)HCC n = 356Study reported on markers involved in cancer aggressivity through comparison of stem-like HCC to less benign mature hepatocyte HCCN/A
Training cohort n = 30
Testing cohort n = 217
Validation cohort n = 139
Beyoğlu et al[44]GC/MSBiopsy samples (human)Six HCC subtypes, liver fibrosis status unknownGlucose ↓Glycolysis
Glycerol 3-phosphate ↓
Glycerol 2-phosphate ↓
Malate ↓
Alanine ↓
Myo-inositol ↓PI3K pathway
Linoleic acid ↓Prostaglandin biosynthesis
Fitian et al[45]UPLC/MS-MS and GC/MSSerum (human)HCV cirrhosis-associated HCC n = 30Sphingosine ↑Sphingolipid
HCV-cirrhosis n = 27Xanthine ↑Oxidative stress metabolism
Healthy volunteers n = 302-Pyrrolidinone ↑GABA metabolism
2-Hydroxybutyrate ↑Oxidative stress metabolism
Serine ↑Amino acid
Glycine ↑
Aspartate ↑
12-HETE ↑Inflammation pathway
15-HETE ↑
Isovalerate ↑Gut microflora metabolism
Dihomo-linolenate ↑Inflammation pathway
Gao et al[46]GC-TOF/MSSerum (human)HBV cirrhosis-associated HCC n = 39Stearic acidFatty acid biosynthesis
HBV-cirrhosis (n = 52)Heptadecanoic acid
Palmitic acid
5-Aminovaleric acidGut microflora metabolism
Cholesterol ↑Cholesterol metabolism
3-hydroxybutyric acid ↑Ketogenesis
Malic acid ↑TCA metabolism
Glutamine ↑Amino acid
Asparagine ↓
Alanine ↑
Threonine ↓
Leucine ↓
Glutamic acid ↑
β-glutamate ↑
5-oxoproline ↓Glutathione metabolism
1,2,4-cyclopropranodicarboxylic acid ↓Dicarboxylic acid metabolism
Table 2 Significantly altered metabolites in cirrhosis patients vs healthy volunteers.
Ref.PlatformTissue (organism)Significantly altered metabolites in cirrhosis patients vs healthy volunteersMain pathways distinguishing cirrhosis from healthy volunteers
Gao et al[33]1H NMRSerum (human)Isoleucine ↓Amino acid metabolism
Leucine ↓
Valine ↓
Glutamine ↑
Tyrosine ↑
Phenylalanine ↑
1-methylhistidine ↑
N-acetylglycoproteins ↑
Acetate ↑N-acetylglycoprotein
Acetoacetate ↓Ketonogenesis
Pyruvate ↑
α-ketoglutarate ↑Glycolysis
Choline ↓TCA cycle
Taurine ↑
Glycerol ↑Bile acid metabolism
Li et al[42]UPLC/QTOF-MSSerum (mouse)Leucine ↓Amino acid metabolism
Phenylpyruvic acid ↓
Phenylalanine ↓
Tryptophan ↓
LPE (16:0) ↓LPE metabolism
LPE (18:0) ↓
LPC (16:0) ↓LPC metabolism
LPC (20:1) ↓
LPC (22:6) ↑
PC (16:0/18:3) ↑Phosphatidylcholine metabolism
PC (12:1/24:3) ↑
PC (16:0/20:4) ↑
PC (16:0/22:6) ↑
PC (18:0/20:4) ↑Sphingomyelin metabolism
SM (d18:0/16:1) ↓
Soga et al[35]Capillary elecrtophore-sis/TOF-MSSerum (human)γ-glutamylalanine ↑Glutathione metabolism
γ-glutamylvaline ↑
γ-glutamylglutamine ↑
γ-glutamylcitrulline ↑
Alanine ↑Amino acid metabolism
Methionine sulfoxide ↑
Wang et al[38]UPLC/MS-MS; LC/QTOF-MSSerum (human)LPC-16:0 ↓LPC metabolism
LPC-18:0 ↓
16:0/18:1-PC ↓
16:0/18:2-PC ↓
16:0/20:4-PC ↓
16:0/22:6-PC ↓
18:0/18:2-PC ↓
Oleamide ↑Fatty acid metabolism
Phenylalanine ↑
GCDCA ↑Bile acid metabolism
Canavaninosuccinate ↓Arginosuccinate synthetase pathway
Zhou et al[39]UPLC/QTOF-MSSerum (human)Phenylalanine ↑Amino acid metabolism
GCA ↑Bile acid metabolism
Bilirubin ↑Hemoglobin metabolism
LPE (18:2) ↓Lysolipid metabolism
LPC (22:6) ↓
LPC (18:2) ↓
LPC (20:4) ↓
LPC (16:0) ↓
LPC (18:0) ↓
C18:1-CN ↑CPT shuttle system
Chen et al[30]UPLC/QTOF-MSSerum (human);Inositol ↓TCA cycle
Urine (human)2,2-bipyridine ↓
Methionine ↓Amino acid metabolism
Tyrosine ↓
Arginine ↓Fatty acid metabolism
Stearic acid ↓
Palmitic acid ↓
Citric acid ↓
2-piperidine carboxylic acid ↓
5-Hydroxy-tryptophan ↓
Cao et al[32]UPLC/MSFecal (human)ChenodeoxycholicBile acid metabolism
Acid dimeride ↓
Urobilin ↓Hemoglobin metabolism
Urobilinogen ↓
7-ketolithocholic acid ↓Microbiome metabolism
LPC C18:0 ↑LPC metabolism
LPC C16:0 ↑
Yin et al[41]RPLC/MSSerum (human)Hypoxanthine ↓Purine synthesis
Inosine ↓
Bilirubin ↑Hemoglobin metabolism
GCA ↑Bile acid metabolism
Taurine ↓
LPC C18:2 ↓LPC metabolism
LPC C18:3 ↓
LPC C16:1 ↓
LPC C18:0 ↓
LPC C16:1 ↓
L-acetylcarnitine ↑CPT shuttle system
6-Methylnicotinic acid ↓Nicotine metabolism
Fitian et al[45]Integrated UPLC/MS-MS and GC/MSSerum (human)Glycocholate (GCA) ↑Bile acid metabolism
Tauroursodeoxycholate ↑
Glychochemodeoxycholate ↑
Azelate (nonanedioate) ↑Dicarboxylic acid metabolism
Undecanedioate ↑
Sebacate (decanedioate) ↑
DSGEGDFXAEGGGVR ↑Fibrinogen cleavage peptide
Bilirubin (Z,Z) ↑Hemoglobin catabolism metabolite
Biliverdin ↑
1,2-propanediol ↑Ketogenesis
Succinylcarnitine ↑CPT shuttle system
Acetylcarnitine ↑
Glutarylcarnitine ↑
Gao et al[46]GC-TOF/MSSerum (human)Palmitic acid ↑Fatty acid metabolism
Stearic acid ↑
Oleic acid ↑
Arachidic acid ↑Arachidonic acid metabolism
Aminomalonic acid ↑Dicarboxylic acid metabolism
Phenylalanine ↑Amino acid metabolism
Cysteine ↑
Leucine ↑
Citric acid ↑
Oxoproline ↑
Pathways of importance in hepatocarcinogenesis

Glycerophospholipids: The liver is the principal organ of lipid metabolism and the presence of cirrhosis and hepatocellular carcinoma results in a dramatic shift in the normal metabolism of fatty acids. Among the first HCC metabolomics studies to elucidate this massive deregulation of lipid metabolism in HCC was the Yang et al[28] work in 2007 which employed high resolution magic angle-spinning 1H nuclear magnetic resonance to delineate the metabolomic profile differences in low-grade HCC, high-grade HCC, and non-involved adjacent cirrhosis patient biopsy specimens. The group reported higher levels of several phospholipids in HCC vs cirrhosis, including glycerophosphocholine, phosphatidylcholine, choline and the phosphorylethanolamine. Increases in phosphatidylcholine were further observed in low-grade HCC vs uninvolved cirrhotic tissue, suggesting possible deregulation of glycerophospholipid metabolism at the early phases of HCC development. These phospholipids also exhibited a direct, positive relationship to tumor burden.

In Yang et al[28], the elevation of choline, the head group of many phospholipids that comprise the plasma membrane, not only reflects increased plasma membrane synthesis demand by the growing tumor but is also consistent with the observed elevations in bile (cholic) acid concentration in HCC vs control tissue.

Lysophospholipids, free fatty acids, and acylcarnitines: Significant alterations in the expression of a subtype of glycerophospholipids known as lysophosphatidylcholines (LPC) have routinely been observed in HCC metabolomics studies[29,36-39]. Still other classes of lipids observed to be significantly altered in HCC vs cirrhosis include free fatty acids (FFA)[29,45], very long chain fatty acids[29], and acylcarnitines[37,45]. Downregulated LPCs and FFA in HCC vs cirrhosis were among the major metabolomic trends reported by Patterson et al[29], with LPC (14:0), LPC (20:3), LPC (22:6) and very long chain fatty acids FFA (24:0) (lignoceric acid) and FFA (24:1) (nervonic acid) all trending lower in HCC vs cirrhosis. The decreases of lignoceric and nervonic acid in HCC vs cirrhosis were especially patent and may reflect peroxisome proliferator-activated receptor-alpha (PPAR-α) induced enhancement of peroxisomal β-oxidation. Previous reports implicating heightened PPAR-α activity in HCC support this hypothesis[47,48]. Decreases in these FFAs may also be related to increased activity of lignoceryl-CoA ligase, the enzyme responsible for very long chain fatty acid catabolism and one acted upon by PPAR-α[49]. LPCs are the glycerophospholipid building blocks of cell membranes and elevation of these metabolites may reflect the heightened metabolic needs of growing HCCs. LPCs are also major lipids bound to human albumin[50]. Decreased serum albumin is a signature of liver cirrhosis and liver cancer and elevations of systemic LPCs may be attributed to the shortage of appropriate albumin binding sites that results in increased circulating levels of these metabolites.

Further metabolomics work by Xiao et al[37] also identified this downregulation of LPCs and LPEs in 40 hepatitis C virus (HCV)-associated HCC patients and 49 cirrhosis controls. Among the novel findings of this investigation included decreased levels of acylcarnitines in HCC vs cirrhosis. Results also showed that as tumor burden worsened, the expression of acylcarnitines and bile acids trended significantly downward. Stage II and III HCC exhibited lower levels of these metabolites in comparison to stage 1 [staging based on the American Joint Committee on Cancer Tumor Lymph Node Metastatic Disease (TNM) system]. The downregulation of fatty acids, acylcarnitines, and bile acids in HCC vs cirrhosis supports the cancer Warburg effect involving a metabolic shift from tricarboxylic acid (TCA) cycle and mitochondrial β-oxidation to a heightened reliance on glycolysis for energy production. To undergo β-oxidation in the mitochondrial matrix, free fatty acids (fatty acyl-CoA) must link with cytosolic carnitine via CPT shuttle system enzymes to form acylcarnitines. Acylcarnitines are capable of penetrating the inner mitochondrial membrane and once inside the matrix, CPT enzymes liberate fatty acyl-CoA allowing β-oxidation to ensue. Decreased concentration of acylcarnitines in HCC vs cirrhosis suggests impairment of CPT1-mediated formation of these compounds from FFA and carnitine.

Sphingolipids: One major lipid expression alteration in HCC reported in HCC metabolomics is a perturbation of sphingosine metabolism, with overexpressed sphingosine-1-phosphate (S1P) and sphingosine reported in HCC vs cirrhosis. The overexpression of LPCs is in agreement with the Patterson study while the upregulation of S1P, a signaling lipid, was a novel finding in HCC metabolomics. S1P has been heavily implicated in promoting the progression of several cancers including HCC[51,52] and building the case for this pathway’s involvement in HCC development, our HCC metabolomics work identified S1P’s precursor sphingosine as one of the most strongly upregulated metabolites in HCC vs cirrhosis[45]. Sphingosine is produced via acid ceramidase (AC) activity on ceramide. Ceramides are shown to possess apoptotic effects, while sphingosine 1-phosphate is demonstrated as an anti-apoptotic and angiogenic molecule[53]. This cell turnover control mechanism is known as the “sphingosine rheostat”, and AC is an important modulator of cell death homeostasis. Higher S1P in HCC may also reflect an independent enhancement of sphingosine kinase (SPHK) activity. One study demonstrated the antitumor property of a selective SPHK2 inhibitor in HCC xenografts[54], implicating SPHK as a promoter of HCC progression. Heightened AC activity that results in increased sphingosine may lead to a larger reservoir of S1P via sphingosine kinase and may promote the establishment of a microenvironment conducive to HCC initiation.

Bile acids

Bile acids are synthesized in the liver and aid in fatty acid absorption and digestion. Bile acid elevations in HCC have been reported previously[55] and may be explained by HCC invasion and obstruction of the bile duct. Bile duct blockage can impede adequate transfer of bile acids to the small intestine thereby impairing sufficient absorption and digestion of fats and leading to a buildup of both bile acids and cholinergic lipids in the hepatic tumor microenvironment. The majority of studies comparing HCC and cirrhosis metabolomes did not uncover a significant differential expression of bile acids, but where a significant trend was seen[36-38], the metabolites collectively trended downward, in contrast to the aforementioned previous reports[55,56]. A significant negative correlation between bile acid levels and tumor burden was also observed[37]. Similar to the Yang et al’s work, our metabolomics investigation[45] identified a significant elevation of choline in HCC vs cirrhosis, and bile acids in our study were strongly elevated in cirrhosis patients vs healthy subjects. The elevation of choline, a building block of bile acids, is consistent with an impairment of bile acid synthesis. Bile acid downregulation in HCC may also reflect a metabolic shift away from β-oxidation and the reduced de novo bile acid production caused by the obliteration of healthy hepatocytes during chronic liver disease. Diminished bile acids may also reflect constitutive activation of farnesyl X receptor (FXR), a bile acid-activated nuclear receptor that is also activated by a variety of other lipids including eicosanoids[57]. FXR silences Cyp7A1-catalyzed production of bile acids and is implicated in promoting progression of HCC by multiple studies[58,59]. In the Fujino study of FXR-induced promotion of HCC progression[58], siR-mediated supplementation of FXR enhanced HepG2, Huh7, and HLE HCC cell line progression while FXR knockdown halted this progression.

Oxidative stress metabolism

Among the other pathways found to be significantly associated with HCC in metabolomics investigations are pathways of reactive oxygen species metabolism, notable metabolites of which include the γ-glutamyl peptides. A recent capillary electrophoresis-time of flight mass spectrometry analysis involving sera obtained from HCV-associated HCC patients, cirrhosis patients, hepatitis B virus (HBV) and chronic hepatitis C (HCV) patients, and healthy volunteers showed markedly significant variations in γ-glutamyl expression among these groups[35]. No differences in γ-glutamyl peptide expression were observed between HCC and cirrhosis controls, but several significant alterations were witnessed in the HCC vs viral hepatitis, HCC vs normal healthy controls (NHC), cirrhosis vs viral hepatitis, and cirrhosis vs NHC comparisons. In general, HCC γ-glutamyl expression was increased in comparison to healthy controls, while γ-glutamyl peptides were decreased in HCC vs viral hepatitis. γ-glutamylglycine, γ-glutamylalanine, γ-glutamylvaline and γ-glutamylserine, γ-glutamyltaurine, γ-glutamylleucine, and γ-glutamyllysine were all strongly (0.0001 < P < 0.001) downregulated in HCC vs viral hepatitis B (HBV) and C infection. γ-glutamyl peptides are precursors to glutathione, the chief antioxidant compound primarily synthesized in the liver. The respective elevation of these intermediates in HCC and cirrhosis patients vs NHC suggests that increased oxidative stress contributing to liver dysfunction calls for heighted production of these precursors to combat this deteriorative process. This is consistent with reports implicating oxidative damage as a key pathway in HCC progression and one that increases patient vulnerability for HCC recurrence[60,61]. γ-glutamyl peptides are also liberated in free form by gamma-glutamyl transpeptidase (GGT) mediated breakdown of glutathione. GGT is an enzymatic signature of liver disease and a marker routinely used in the clinic to assess the severity of liver dysfunction. Excess breakdown of glutathione may explain the relative elevation of γ-glutamyl peptides in cirrhosis vs NHC and the impaired oxidative stress neutralization commonly witnessed in HCC.

The Wang et al[38] study also found an oxidative stress signature in HCC, showed a striking 677-fold elevation in canavaninosuccinate (CSA) level in HCC patients vs their cirrhosis counterparts (P < 0.01). Subsequent receiver operator characteristic (ROC) analysis revealed that the sensitivity and specificity of AFP and CSA for distinguishing the HCC patients from cirrhosis controls were: AFP20 ng/mL 74% and 38%; AFP200 ng/mL 52% and 90%; CSA 79.3% and 100%; combined CSA and AFP20 ng/mL 96.4% and 100%. CSA is the precursor to fumarate, a key metabolite of the TCA cycle, and elevation of CSA may reflect impairment in the formation of TCA intermediates and promotes the above referenced Warburg theory involving a metabolic shift from oxidative to anaerobic energy production in cancer microenvironments, which are often more hypoxic than healthy tissue[62].

In our work[45], a strong oxidative stress signature in HCC was also observed, with xanthine, 2-hydroxybutyrate and several γ-glutamylpeptides trending significantly higher in HCC vs cirrhosis. Still, the work of Gao et al[46] uncovered trends suggestive of an opposite phenomenon: Elevations of glutamic acid, lysine and cysteine in HCC compared with healthy controls were observed and are curious in the context of reactive oxygen species (ROS) given that these amino acids are precursors of glutathione (GSH). Increased levels of these amino acids suggest a possible enhancement of GSH production that establishes a microenvironment conducive for tumor survival[46].

Protein metabolites

The liver is the major organ of protein metabolism and not surprisingly, HCC metabolomics shows that the expression profiles of patients with advanced liver disease exhibit major differences in amino acid expression when compared to metabolic profiles of appropriate diseased or healthy controls. In the Yang et al[28] study, significant increases in creatine, glutamine, and glutamate were found in HCC vs cirrhosis, and these trends were juxtaposed by decreases in lactate, alanine, leucine, glutamate, glutamine in HCC vs cirrhosis. The finding of increased amino acids in HCC vs cirrhosis is consistent with numerous studies implicating elevated amino acids and the enzymes responsible for their production in cancer initiation and progression[63-66]. In one large scale metabolomics analysis of 60 cancer cell lines, Jain et al[66] identified glycine as the most significantly and consistently upregulated metabolite in cancer cells vs healthy lines. Enhanced amino acid production is consistent with the metabolic remodeling hallmark of cancer known as the Warburg effect, which involves a shift from TCA cycle and β-oxidation to a heightened reliance on glycolysis for energy production[67]. Amino acids are important glycolytic enzyme activators, and one recent study demonstrated that serine was an activator of pyruvate kinase M2[68], the cancer isoform of glycolytic enzyme pyruvate kinase responsible for conversion of phosphoenolpyruvic acid (PEP) to pyruvate. The group’s concomitant observation of elevated lactate in HCC vs cirrhosis is consistent with the Warburg hypothesis. Gao et al[46] propose that their observed elevation of pyruvate, which would seem to counteract the tumor’s reliance on the lower-activity PKM2 compared to PKM1, is due to enhanced production of glutamine which is used as an alternative pyruvate precursor. Our study identified a strong amino acid signature associated with HCC with serine, glycine and aspartate being the most significantly upregulated metabolites in HCC vs cirrhosis. These elevations are corroborated by a recent metabolomic analysis of HBV-associated HCC showing a strong upregulation of serine, alanine, glycine, cysteine, aspartic acid, methionine, tyrosine, tryptophan, and phenylalanine in HCC vs healthy controls. Upregulated amino acids in HCC may be explained by the greater protein turnover that transpires in rapidly dividing tumors vs the surrounding non-cancerous tissue along with impaired amino acid utilization in liver.

In addition to very small molecular weight amino acids, metabolomics is also useful for detecting larger protein metabolites weighing near 10 kDa. SELDI TOF-MS metabolomics is a highly versatile methodology for large-scale identification and quantification of these larger metabolites. Wu et al[31] tapped this technology to investigate the metabolomic expression profiles of HBV-associated HCC patients, cirrhosis patients, and healthy controls. Their results reveal the upregulation of two proteins, growth related oncogene-alpha (GRO-α) and thrombin light chain (TLC), in HCC patients vs cirrhosis controls. To validate these putative markers, serum from an alternative series of HCC, cirrhosis, healthy, and cancer control patients was subjected to SephdexTM Peptide 10/300 GL, HitrapTM CM, and Mono Q 5/50 GL liquid chromatography for protein separation. The separated products were purified using SDS-PAGE and their identities were confirmed using electrospray ionization mass spectrometry. In this alternative series of patients, GRO-α was upregulated four-fold in HCC patients vs cirrhosis controls and its level directly correlated with HCC tumor burden. GRO-α also trended higher in gastric, nasopharyngeal and lung cancers relative to cirrhosis patients, but the magnitude of GRO-α elevation in cancer controls vs cirrhosis was smaller than the elevation witnessed in HCC vs cirrhosis, suggesting that GRO-α may play a more prominent role in HCC progression. Interestingly, TLC was 1.4 times more elevated in HCC patients vs cirrhosis controls but downregulated in the cancer control patients vs cirrhosis, suggesting that TLC is a unique signature of HCC. The combined sensitivity and specificity of GRO-α + TLC + AFP for discriminating HCC patients from cirrhosis and healthy controls was 91.7% and 92.7% respectively. At the 400 ng/mL cutoff for HCC diagnosis, AFP had a sensitivity of 69% and a specificity of 83%.

GRO-α is a chemokine involved in invoking leukocyte cell migration and is associated with pro-inflammatory processes, angiogenesis, and cancer[69-71]. Its elevation may be a signature of a viral hepatitis-associated HCC immune deregulation involving heightened monocyte migration and increased inflammation, and a subsequent increased likelihood for successful host evasion of tumor prophylactic mechanisms[72]. TLC is a protein cleavage fragment that is generated from matrix metalloproteinase-associated (MMP) peptide cleavage[31]. Its upregulation may be explained by simultaneous E-cadherin loss and MMP activation by Twist1 which has been shown to promote HCC expansion[73]. Its utility as a cancer biomarker was also shown in a SELDI TOF-MS study of gastric cancer by Ebert et al[74], where TLC accurately distinguished gastric cancer patients from patients without cancer with a sensitivity of 89.9% and a specificity of 90%.

The destruction of cholinergic receptors in amyloid plaques and neurofibrillary tangles of the brain has also been linked to encephalopathy, a common neurological disorder witnessed in HCC patients[75]. The molecular mechanisms linking hepatic encephalopathy (HE) and HCC are not fully understood, but the parallel elevations of choline and glutamine in HCC patients vs cirrhosis controls may partially be associated with a cataclysmic loss in cholinergic receptor concentration and the initial stages of HE onset, respectively[76]. A possible neurological metabolic signature was one of the most strongly correlated trends observed in HCC patients vs cirrhosis and vs healthy controls in our study, with 2-pyrrolidinone, a γ-aminobutyric acid (GABA) metabolite, was strongly elevated in HCC when compared to levels in both the cirrhosis controls and the healthy volunteers. GABA is a major inhibitory chemical messenger in the brain, and the sharp elevation of its metabolic byproducts in HCC may reflect heightened production of GABA and may coincide with the neurodegenerative hallmarks of advanced liver disease.

Two previously unidentified fibrinogen cleavage peptides, denoted by amino acid sequence as DSGEGDFXAEGGGVR and ADSGEGDFXAEGGGVR, were observed to be significantly overexpressed in HCC in our study. While they trended higher in HCC vs cirrhosis, their elevation in cirrhosis vs healthy controls was more patent and these metabolites exhibited a better diagnostic utility for cirrhosis. Taken together, the aberrations in protein and amino acid expression in HCC revealed by metabolomics may be harnessed toward the development of clinically fruitful HCC diagnostics.

Markers of cirrhosis

A number of HCC metabolomics studies report significant alterations between cirrhosis patients and healthy volunteers[30,32,33,35,38,39,41,42,45,46] and within these findings are opportunities for development of biomarkers for cirrhosis. Currently, the best cirrhosis diagnostic is liver biopsy, a procedure that by virtue of its invasive nature is not a gold standard diagnostic approach[77]. Identification of accurate cirrhosis diagnostic biomarkers can streamline development of a less invasive diagnostic that minimizes patient discomfort and the expenses associated with biopsy while simultaneously enabling early detection of cirrhosis in both industrialized nations and in resource poor areas of the globe. Among the putative markers of cirrhosis identified in these works include the purine metabolites hypoxanthine[41,45] and inosine[41], both significantly downregulated five and six-fold respectively in cirrhosis vs NHC. γ-glutamylalanine, γ-glutamylvaline, γ-glutamylglutamine, γ-glutamylphenylalanine and γ-glutamylcitrulline were also significantly elevated in cirrhosis vs NHCs[41,45]. Increases in the γ-glutamyl peptides in cirrhosis vs NHC and HCC vs NHC indicates heighted production of glutathione to combat the oxidative damage process commonly implicated as a promoter of tumor initiation and progression. γ-glutamyl peptides are also liberated in free form by GGT mediated breakdown of glutathione. The elevation of γ-glutamyl peptides in cirrhosis vs NHC may therefore conversely be explained by excessive breakdown of glutathione resulting in impaired oxidative stress neutralization that is commonly witnessed in HCC.

Other promising markers of cirrhosis include bile acids[45] and dicarboxylic acids[45,46], with these metabolite classes exhibiting the strongest and most significant fold-differences among all markers significantly altered between cirrhotics and healthy controls in the references metabolomic studies.

Etiological metabolomic differences

Because HCC is linked to a variety of diverse etiologies including viral hepatitis, alcoholic cirrhosis, non-alcoholic fatty liver disease, steatohepatitis, and aflatoxin B1, metabolomics is useful for revealing the variation in metabolism these etiologies cause. The majority of studies have focused on characterizing the metabolomic signatures of single HCC etiologies, or have not accounted for etiology. Studies to date have not queried the metabolomic expression pattern differences between the various etiologies of HCC and as the metabolic underpinnings of each etiology are better characterized, HCC etiology metabolome comparison studies will prove vital in establishing the utility for metabolomics as an accurate diagnostic modality of different HCC subtypes. Only one study, by Zhou et al[39], compared the serum metabolomic expression patterns of HCV cirrhosis-associated HCC and HBV cirrhosis-HCC. The group also looked at whether metabolomic alterations in HCV-HCC and HBV-HCC were significantly different from cirrhosis and healthy control metabolomes. The study revealed a greater magnitude decrease of LPC expression in HBV-infected HCC patients vs cirrhosis controls than the magnitude decrease of LPCs HCV-HCC vs cirrhosis. The analysis also showed significant elevation of bile acids, heme pigmentation compounds bilirubin and biliverdin, upregulation of acylcarnitines and downregulation of glycerophospholipids in cirrhosis patients vs healthy controls, suggesting that these metabolites were signatures of the onset of cirrhosis. Metabolomic profile comparison between HBV-cirrhosis patients vs patients with HBV-only revealed a similar global downregulation of LPCs in the cirrhosis cohort vs viral hepatitis controls. The findings of this study suggest a progressive downregulation of LPCs during the course of progression from viral hepatitis to cirrhosis and a bottomed out expression occurring with HCC. The downregulation of LPC in HCC may be explained by their well-known anti-tumor roles that include induction of apoptosis, anti-invasive effects, and a direct effect on tumor sensitization to treatment. This trend of diminished LPCs may reflect first the substantial cell death that occurs during cirrhosis resulting in diminished LPC levels, followed by a subsequent massive turnover of residual LPCs by the growing HCC that further exhausts the LPC reservoir. The magnitude of LPC decrease in HBV-HCC vs cirrhosis was greater than HCV-HCC vs cirrhosis, suggesting that HBV exerts a more prominent influence on LPC metabolism than HCV.

Integrated “omics” approaches

As the above studies demonstrate, metabolomics is a powerful strategy for identifying a large panel of metabolites that exhibit promise in accurately diagnosing HCC. What is unclear from these works, however, are the genomic and proteomic synergies that culminate in these metabolic manifestations. Integrating two or more “omics” approaches can unveil the complex genomic-proteomic-metabolomic network galvanizing cancer development. Two recent studies tapped the power of such an approach, coupling metabolomics with transcriptomics to identify the genetic underpinnings of metabolomic disruptions. The first study by Budhu et al[43] investigated the network of metabolic pathways and corresponding genes involved in HCC aggressivity. Patient demographic and clinical characteristics, including body mass index, were extensively characterized for 356 HCC cases, which were divided into a training set (n = 30), a testing set (n = 217), and a validation cohort (n = 139). The training cohort consisted of 15 epithelial cell adhesion molecule-positive/AFP-positive (EpCAM+AFP+) HpSC-HCC patients, representing aggressive stem-like HCC, and 15 less aggressive (EpCAM-AFP-) mature hepatocyte MH-HCC patients. Analysis of tumor specimens and uninvolved healthy tissue by principal component analysis revealed clear demarcation between tumor and non-tumor metabolomes. Non-targeted metabolic profiling of tumor and non-tumor specimens identified 48 markers that were significantly altered between HpSC-HCC patients and patients with MH-HCC. This metabolite panel resolved aggressive HCC from the less subtype at a sensitivity of 72% and a specificity of 83% (P < 0.05). The group further demonstrated that within the subset of 48 metabolites associated with HCC aggressivity, 28 were significantly associated with overall survival. Subsequent microarray gene expression profiling of the paired tumor specimens identified 169 genes that could significantly distinguish the two HCC subtypes. The group then gauged the correlation between this genetic signature and the panel of 28 metabolites that were associated with both HCC aggressivity and overall survival and found that each of the 28 metabolites were associated with at least one of the 169 genes identified by microarray analysis. To determine the principal metabolite-gene pairs potentially influencing HCC aggressivity, the group performed correlation analysis with randomization and found 15 metabolites and 121 genes most highly associated with the tumor and stem-like HCC genes. Validation of the genetic signature in an independent testing cohort (n = 217) revealed a panel of 273 genes that distinguished HpSC-HCC from MH-HCC with a sensitivity of 72% and a specificity of 91% (P < 0.01), and this genetic signature was also strongly (P < 0.0001) associated with overall survival. This gene set was also a significant independent predictor of overall survival, progression free survival and recurrence, highlighting its utility as a prognostic panel of aggressive HCC. Many of the genes were associated with fatty acid metabolism and the phosphatidylinositide 3-kinase signaling pathway. Elevated palmitoleate expression in HpSC-HCC tumor vs paired non-tumor specimens reflected overexpression or enhanced activity of stearoyl-CoA dehydrogenase (SCD), an important cell turnover regulatory pathway. Significant downregulation of arachidonate and linoleate, the parent molecules of the eicosanoid signaling cascade, suggested overconsumption of these metabolites from hyperactivity of cycloxygenase, lipoxygenase, and/or cytochrome P450c in aggressive HCC. SCD, which converts saturated palmitic acid to palmitoleic acid, was among the gene set. To validate the possible role of SCD in cancer aggressivity, in vitro and in vivo analysis was done and showed that selective SCD inhibitor CGX0168 abrogated Huh7 cell migration and invasion. Supplementation of Huh7 with palmitoleate, the end product of SCD activity, enhanced cell migration and invasion. Xenografted tumors in nude mice showed that SCD-siRNA inhibited tumor migration and colony formation and increased apoptosis, likely owing to the accumulation of pro-apoptotic palmitate. Taken together, this approach showed a strong lipid signature associated with HCC aggressivity. Elevated palmitoleate reflected upregulated SCD, a key regulator of the ratio between saturated and unsaturated fatty acids. A tilt in the delicate balance between saturated and unsaturated fatty acids toward unsaturated has been implicated in cancer aggressiveness.

Remarkably, the integrated metabolomics-transcriptomics study by Beyoğlu et al[44] also revealed a consistent role for palmitate, linoleate, and the PI3K pathway in HCC. Microarray analysis found that 11 genes involved in fatty acid were associated with HBV-positive HCC cases. More broadly, the comparison of tumor vs paired non-tumor tissues identified a metabolic shift toward glycolysis reflective of the Warburg effect implicated in other HCC metabolomics works. Levels of glucose, glycerol 3-phosphate, glycerol 2-phosphate, malate, alanine, and myo-inositol were all significantly decreased in tumor vs non-tumor specimens, indicating both impaired mitochondrial respiration and enhanced glycolysis. Myo-inositol is the metabolic precursor of the messenger molecule inositol triphosphate (PI3) required by PI3K-AKT-mTOR, and its downregulation in tumor vs non-tumor specimens is consistent with the hyperactivity of this pathway implicated in numerous cancers. Furthermore, the group showed that 1-stearoylglycerol and 1-palmitoylglycerol were decreased in tumor vs non-tumor. 1-acylglycerols are synthesized by phospholipase activity on LPCs and the routinely reported decrease in LPCs in HCC vs controls may explain the reduced 1-acylglycerols in this study. Through integration of metabolomics and transcriptomics, these groups together show a possible role for SCD in HCC, implicate the PI3K pathway as important to HCC progression, and show a strong lipid signature associated with HCC, in agreement with other HCC metabolomics works. More broadly, these works demonstrate that the achievement of consistent trends in independent, integrated “omics” studies can be achieved and lay important groundwork for future studies of this nature.

Sensitivity and specificity of metabolomics in HCC diagnosis

To evaluate the utility for metabolomic profiles to distinguish between patients with cirrhosis and patients with HCC, these studies primarily employed principal component analysis, orthogonal projection to latent structures, supervised projection to latent structures discriminant analysis, the random forest machine learning algorithm, or ROC curve class prediction analyses. Reported sensitivity/specificity/area under the curve values reflecting the accuracy of metabolomics in distinguishing HCC from cirrhosis are shown in Table 3. In general, metabolomics was a highly accurate diagnostic method and clear demarcation between healthy controls and/or cirrhosis controls vs HCC patients were realized. Where metabolomics profile class prediction was compared with AFP, the metabolomics approach showed greater class prediction power. In general, the focus of these works was the potential standardization of metabolomics as a high-throughput clinical diagnostic platform-big-data biomarkers rather than single metabolite alterations-which may explain the lack of emphasis on the sensitivity and specificity of individual metabolites for diagnosing HCC. Just two studies verified the metabolite expression patterns in vitro, and with the exception of the in vivo validation performed by Budhu et al[43], no studies used in vivo models to validate their metabolomics data. It is therefore recommended that future validation of HCC metabolomics data include preliminary information on metabolite concentrations in HCC cases vs cirrhosis controls as measured in vitro or in vivo to reveal their utility as potential biomarkers that can be employed in a rapid in vitro diagnostic.

Table 3 Utility of significantly altered (P < 0.05) metabolites in accurately predicting hepatocellular carcinoma (hepatocellular carcinoma cases vs patients with cirrhosis).
Ref.PlatformComparisonClass prediction methodologyClassification accuracy or sensitivity/specificityAFP sensitivity/specificity
Patterson et al[29]UPLC/ESI-QTOF-MSHCC (n = 20) vs cirrhosis (n = 7)Random forest96.3-
Chen et al[30]Integrated GC/QTOF-MS + UPLC/QTOF-MSHCC (n = 82) vs healthy (n = 71)OPLS-DA100.0-
Wu et al[31]SELDI-TOF MSHCC (n = 48) vs cirrhosis (n = 54) or healthy (n = 42)GRO-α + thrombin light chain PS20 Protein immunoassay89.6/89.669/83
Cao et al[32]UPLC/QTOF-MSHCC (n = 23) vs cirrhosis (n = 22)PLS-DA67.0-
Gao et al[33]NMRHCC (n = 39) vs cirrhosis (n = 36)PLS-DA45.7-
Wu et al[34]GC/MSHCC (n = 20) vs healthy (n = 20)PCA with ROC curve analysisAUC=88.3; AUCAFP = 92.5 when combined with AFP-
Soga et al[35]LC/MS-MSHCC (n = 32) vs HCV-only (n = 35) or cirrhosis (n = 18)Multiple logistic regression; ROC curve analysis88.10.760
Wang et al[38]UPLC-MSHCC (59) vs cirrhosis (20) or NHC (20)PLS-DA, ROC curve analysisCSA 79.3/100 CSA + AFP20 96.4/100 UPLC-MS 100/100AFP20 74/38 AFP200 52/90
Zhou et al[39]UPLC-QTOF-MSHCC (n = 69) vs cirrhosis (n = 28)PLS-DA, ROC curve analysisAEA 88.0 PEA 82.0 AEA + PEA 88.0-
Nahon et al[40]NMRSmall HCC (n = 28) vs cirrhosis (n = 93); Large HCC (n = 33) vs cirrhosis (n = 93)OPLSSmall HCC: 61.0/100.0 Large HCC: 100.0/100.0-
Yin et al[41]RPLC/QTOF-MS; HILIC/QTOF-MSHCC (n = 25) vs cirrhosis (n = 24) or healthy (n = 25)OPLSRPLC: 61.8 HILIC: 57.0 RPLC + HILIC = 63.6-
Li et al[42]UPLC/QTOF-MSHCC (n = 8) vs cirrhosis (n = 6) or healthy (n = 6) (murine samples)OPLS-DA88.2-
Budhu et al[43]Training set1: GC/MS + UPLC/MS-MS; Testing set2: Affymetrix GeneChipTraining set: Stem-like aggressive HpSC-HCC (n = 15) vs Mature hepatocyte less aggressive MH-HCC (n = 15); Testing set: HpSC-HCC and MH-HCC (n = 217)Multivariate analysis172.0/83.0, AUC = 0.830 272.0/91.0, AUC = 0.860-
Fitian et al[45]UPLC/MS-MS + GC/MSHCC (n = 30) vs HCV-cirrhosis (n = 27)Random forest72% 12-HETE 73.3/69.2AFP20 63.3/83.6
ROC analysis15-HETE 83.3/59.3
Aspartate 100/51.9
Glycine 83.3/63.0
Serine 73.3/85.2
Phenylalanine 73.3/81.5
Homoserine 70.0/85.2
Sphingosine 58.3/86.7
Xanthine 63.3/88.9
2-Hydroxybutyrate 76.7/77.8
Gao et al[46]GC-TOF/MSHCC (n = 39) vs HBV-cirrhosis (n = 52)Random forest (validation set)96.8% in HCC vs HBV-cirrhosis 100% in HBV-cirrhosis vs HBV-
100% in HBV vs NHC
ROC analysis (validation set)100/95.2 HBV vs NC
83.3/100 HBV-cirrhosis vs HBV
76.9/83.3 HCC vs HBV-cirrhosis
Bayes discriminant function model (validation set)76.9% HCC 100% HBV-cirrhosis
94.1% HBV
100% NHC
Summary of major findings

The results of these studies show that aberrations in bile acid, LPC, acylcarnitine, ROS, and protein metabolism may be signatures of HCCs emerging in the setting of cirrhosis. Bile acids trended lower in HCC vs cirrhosis controls in all studies reporting a significant difference in expression of these metabolites[36-38]. In general, LPCs were shown to be downregulated in HCC vs cirrhosis[37-39,45]. But the works by Ressom et al[36] and Wang et al[38] found that LPCs and LPEs were consistently elevated in the HCC vs cirrhosis comparison. Acylcarnitines, which function as liaisons of fatty acid β-oxidation by enabling fatty acid importation to the mitochondrial matrix, were significantly downregulated in HCC vs cirrhosis. Amino acids also trended higher in HCC in all studies reporting significant expression differences between HCC and cirrhosis. The hemoglobin metabolite bilirubin was also consistently elevated in HCC vs cirrhosis[29,39,41,45], consistent with the clinical utility this metabolite possesses for diagnosing advanced liver disease. Biliverdin, another heme catabolite, was significantly upregulated in HCC vs cirrhosis in two studies[29,45] and a metabolic derivative of biliverdin downregulated in a third[37].

The elevation of LPCs in HCC vs cirrhosis seen by Ressom et al[36] is consistent with increased demand of glycerophospholipids by the growing tumor. LPCs comprise only 3% of the total phospholipids in plasma membranes, however, and a better explanation for their elevation may be that LPCs are major lipids bound to albumin. The loss of albumin commonly witnessed in chronic liver disease and HCC may mean a shortage of docking sites for LPCs, resulting in increased systemic levels of these glycerophospholipids. Diminished bile acids in HCC vs cirrhosis reinforces the Warburg effect commonly witnessed in cancer studies involving a shift in energy production away from oxidative processes like β-oxidation and the TCA cycle toward anaerobic glycolysis, which is more suitable to the hypoxic or anoxic tumor microenvironment. The loss of bile acids in HCC significantly impairs fatty acid absorption and digestion and is consistent with the general trend of increased circulating lipids in HCC vs cirrhosis and cirrhosis vs appropriate controls. The elevation of bile acids has historically served as a clinical indicator of chronic liver disease and specifically cirrhosis and consistent with this function, our metabolomics work showed a global upregulation of bile acids in cirrhosis vs healthy volunteers[45]. Decreased acylcarnitines further reflects diminished reliance on β-oxidation, and it may also signify impairment of the mitochondrial carnitine palmitoyltransferase shuttle system.

Metabolomic heterogeneity attributed to differences in study design

While perturbations in phospholipid, bile acid, hemoglobin, acylcarnitine and amino acid metabolism were routinely encountered in these studies, the referenced works did not achieve uniform conclusions regarding the directional shifts of the metabolites’ expression in HCC vs cirrhosis, with examples including contradictory patterns of LPC[36,39] and amino acid[28,46] expression in HCC vs cirrhosis. The heterogeneity among these studies’ metabolomes is likely due to differences in study design. Although some studies matched patients by age and gender, the majority of the referenced HCC metabolomics studies did not extensively characterize HCC and cirrhosis patients by demographic and clinical characteristic parameters. Only four studies reported MELD scores for HCC and cirrhosis patients[36,37,40,45], and just three studies indicated the Child-Pugh status of their HCC or cirrhosis cohorts[29,40,45]. This lack of information on whether liver function of the study participants is compensated or decompensated complicates interpretation of metabolomic data. Body mass index (BMI) also went unreported for HCC, cirrhosis, and healthy study subjects in all but two studies[43,45] referenced in this review. BMI can have a significant influence on the relative metabolite expression differences between patients, particularly with regard to adiposity. Because these studies did not control for BMI, is likely that the patients recruited for these metabolomics studies had wide-ranging BMIs that may further explain the noticeably different trends of LPC, FFA, and acylcarnitine expression in HCC vs controls. To limit the influence of potential cofounders such as comorbidity, BMI, age or gender, and etiology on HCC metabolomes, patient clinical characteristics should be controlled for more conscientiously in future HCC metabolomics studies.

There was also marked variation in how each group diagnosed and staged their HCC patients. Three studies[29,43,45] staged according to the Barcelona Clinic for Liver Cancer staging criteria. The TNM Classification of Malignant Tumors (TNM) was used to diagnose HCC in five studies[28,30,37,38,43], imaging in two, histopathology in one[36], and six studies made no mention of their HCC diagnosis method[31,33,35,39,41,42]. Differences in HCC diagnosis and staging among these studies likely contributed to the discordant global metabolomic alterations in HCC vs cirrhosis among these studies and further confounded the interpretation of these metabolomic trends.

Lack of emphasis on HCC vs cirrhosis comparison

The critical metabolomic comparison between HCC patients and cirrhosis controls was reported in just ten out of the twenty studies referenced in this review[28,29,31,35-39,45,46], with half of the cited works instead focusing on the metabolomic profile differences between HCC patients and NHC subjects. While this comparison sheds light on altered pathways during hepatocarcinogensis, it may not be as applicable from a clinical standpoint as the trends elucidated through a comparison of HCC vs cirrhosis. This owes to the fact that the majority of primary liver cancer cases occur in patients with a preexisting condition of cirrhosis. Therefore, the metabolomic comparison between HCC and cirrhosis is more clinically informative and potentially translational than the comparison of HCC vs NHC. HCC is a complex heterogeneous disease and HCC patients often present with multiple comorbidities. It is therefore likely that marked metabolomic differences will be observed between HCC patients and healthy subjects. Differences between HCC patients and cirrhosis controls are subtler than HCC vs NHC[45,46] and it would be expected that conspicuous metabolomic differences exist between HCC/NHC and cirrhosis/NHC. Given that HCC arose in the background of cirrhosis in all reported HCC metabolomics studies, it is impossible to determine whether the alterations witnessed in HCC vs NHC are related to HCC or cirrhosis, further reinforcing the need for future metabolomic comparisons between HCC and cirrhosis.


Bile acids, acylcarnitines, amino acids, free fatty acids, LPCs, and heme pigmentation molecules exhibited utility in stratifying HCC patients from patients with cirrhosis. Canavaninosuccinate, which showed a striking 680-fold elevation in HCC patients vs cirrhosis controls and outperformed AFP in sensitivity and specificity. Sphingosine 1-phosphate (↑), sphingosine (↑), GRO-α (↑), and thrombin light chain (↑) were other putative HCC biomarkers that had superior predictive utility for HCC than AFP. Bile acids, fibrinogen cleavage byproducts, dicarboxylic fatty acids, and ROS-related γ-glutamyl peptides exhibited strong association with cirrhosis and the further development of these metabolites as diagnostic markers of cirrhosis may be valuable. In addition to individual metabolites, global patient metabolomes exhibited superior sensitivity for diagnosis of HCC vs AFP where a comparison was made[31,35,38,45].

Although these data are preliminary in nature, they reflect the early promise that metabolomics platforms hold in potential clinical implementation for disease diagnosis. Future metabolomics studies with larger, better demographically and clinically characterized patient cohorts may resolve the heterogeneous metabolomic expression patterns in HCC vs cirrhosis.

Given that > 90% of HCCs emerge in the setting of cirrhosis, future HCC metabolomics studies will be most impactful and clinically relevant if they compare the expression patterns of HCC patients vs cirrhosis controls. Moreover, patients with HCC and cirrhosis should be matched by etiology (viral vs non-viral), liver function, and BMI to limit the influence of data confounders. Furthermore, it is paramount the appropriate metabolomic profile comparisons are made, namely HCC vs cirrhosis, cirrhosis vs etiology of cirrhosis (viral or non-viral), and cirrhosis vs NHC. In a recent study, the entire stepwise hepatocarcinogenic process from NHC to HBV, through cirrhosis, and culminating in HCC was analyzed. Appropriately, Gao et al[46] focused their expression profile comparisons on HCC vs cirrhosis, and cirrhosis vs HBV/NHC. This approach enables investigators to make adequate conclusions about the interval at which putative biomarkers become relevant in the hepatocarcinogenic process and demonstrates the relevance of the dataset comparisons. Moreover, investigation of the pathways involved in the progression from the initial liver insult to cirrhosis remains a largely untapped realm of biomarker discovery within metabolomics, and may streamline the identification of potential cirrhosis diagnostic markers. More broadly, metabolomics is well-suited for clarifying the entirety of the metabolic remodeling that occurs throughout hepatocarcinogensis, and can hence streamline biomarker discovery efforts at each pathological interval.

The findings of these works demonstrate the powerful resource that is metabolomics for identifying potential novel diagnostic biomarkers of HCC. The translational optimality of metabolomics is underscored by its capability to simultaneously process high volumes of patient specimens and interpret metabolic expression profiles through robust, validated and automated software. Still greater, metabolomics holds promise as a novel disease screening and diagnostic modality that, through characterization of a patient’s global metabolic profile, can in a more sophisticated and comprehensive fashion accurately predict the presence of disease.


Manuscript source: Invited manuscript

Specialty type: Gastroenterology and hepatology

Country of origin: United States

Peer-review report classification

Grade A (Excellent): 0

Grade B (Very good): B

Grade C (Good): 0

Grade D (Fair): 0

Grade E (Poor): 0

P- Reviewer: Wei JF S- Editor: Qi Y L- Editor: A E- Editor: Li D

1.  Altekruse SF, McGlynn KA, Reichman ME. Hepatocellular carcinoma incidence, mortality, and survival trends in the United States from 1975 to 2005. J Clin Oncol. 2009;27:1485-1491.  [PubMed]  [DOI]  [Cited in This Article: ]  [Cited by in Crossref: 1196]  [Cited by in F6Publishing: 1424]  [Article Influence: 101.7]  [Reference Citation Analysis (0)]
2.  El-Serag HB. Hepatocellular carcinoma. N Engl J Med. 2011;365:1118-1127.  [PubMed]  [DOI]  [Cited in This Article: ]  [Cited by in Crossref: 2881]  [Cited by in F6Publishing: 3048]  [Article Influence: 254.0]  [Reference Citation Analysis (0)]
3.  Hussain SM, Semelka RC, Mitchell DG. MR imaging of hepatocellular carcinoma. Magn Reson Imaging Clin N Am. 2002;10:31-52, v.  [PubMed]  [DOI]  [Cited in This Article: ]  [Cited by in Crossref: 88]  [Cited by in F6Publishing: 86]  [Article Influence: 4.1]  [Reference Citation Analysis (0)]
4.  El-Serag HB, Rudolph KL. Hepatocellular carcinoma: epidemiology and molecular carcinogenesis. Gastroenterology. 2007;132:2557-2576.  [PubMed]  [DOI]  [Cited in This Article: ]  [Cited by in Crossref: 3846]  [Cited by in F6Publishing: 4170]  [Article Influence: 260.6]  [Reference Citation Analysis (2)]
5.  El-Serag HB, Mason AC. Rising incidence of hepatocellular carcinoma in the United States. N Engl J Med. 1999;340:745–750.  [PubMed]  [DOI]  [Cited in This Article: ]
6.  Kew MC. Epidemiology of hepatocellular carcinoma in sub-Saharan Africa. Ann Hepatol. 2013;12:173-182.  [PubMed]  [DOI]  [Cited in This Article: ]
7.  Cedrone A, Covino M, Caturelli E, Pompili M, Lorenzelli G, Villani MR, Valle D, Sperandeo M, Rapaccini GL, Gasbarrini G. Utility of alpha-fetoprotein (AFP) in the screening of patients with virus-related chronic liver disease: does different viral etiology influence AFP levels in HCC? A study in 350 western patients. Hepatogastroenterology. 2000;47:1654-1658.  [PubMed]  [DOI]  [Cited in This Article: ]
8.  Gupta S, Bent S, Kohlwes J. Test characteristics of alpha-fetoprotein for detecting hepatocellular carcinoma in patients with hepatitis C. A systematic review and critical analysis. Ann Intern Med. 2003;139:46-50.  [PubMed]  [DOI]  [Cited in This Article: ]  [Cited by in Crossref: 274]  [Cited by in F6Publishing: 287]  [Article Influence: 14.4]  [Reference Citation Analysis (0)]
9.  Tateishi R, Yoshida H, Matsuyama Y, Mine N, Kondo Y, Omata M. Diagnostic accuracy of tumor markers for hepatocellular carcinoma: a systematic review. Hepatol Int. 2008;2:17-30.  [PubMed]  [DOI]  [Cited in This Article: ]  [Cited by in Crossref: 122]  [Cited by in F6Publishing: 105]  [Article Influence: 7.0]  [Reference Citation Analysis (0)]
10.  Bialecki ES, Di Bisceglie AM. Diagnosis of hepatocellular carcinoma. HPB (Oxford). 2005;7:26-34.  [PubMed]  [DOI]  [Cited in This Article: ]  [Cited by in Crossref: 186]  [Cited by in F6Publishing: 198]  [Article Influence: 11.0]  [Reference Citation Analysis (0)]
11.  Chen CH, Lin ST, Kuo CL, Nien CK. Clinical significance of elevated alpha-fetoprotein (AFP) in chronic hepatitis C without hepatocellular carcinoma. Hepatogastroenterology. 2008;55:1423-1427.  [PubMed]  [DOI]  [Cited in This Article: ]
12.  Lai Q, Melandro F, Pinheiro RS, Donfrancesco A, Fadel BA, Levi Sandri GB, Rossi M, Berloco PB, Frattaroli FM. Alpha-fetoprotein and novel tumor biomarkers as predictors of hepatocellular carcinoma recurrence after surgery: a brilliant star raises again. Int J Hepatol. 2012;2012:893103.  [PubMed]  [DOI]  [Cited in This Article: ]  [Cited by in Crossref: 28]  [Cited by in F6Publishing: 35]  [Article Influence: 3.2]  [Reference Citation Analysis (0)]
13.  Bruix J, Sherman M. Management of hepatocellular carcinoma. Hepatology. 2005;42:1208-1236.  [PubMed]  [DOI]  [Cited in This Article: ]  [Cited by in Crossref: 4333]  [Cited by in F6Publishing: 4694]  [Article Influence: 260.8]  [Reference Citation Analysis (0)]
14.  Weitz IC, Liebman HA. Des-gamma-carboxy (abnormal) prothrombin and hepatocellular carcinoma: a critical review. Hepatology. 1993;18:990-997.  [PubMed]  [DOI]  [Cited in This Article: ]  [Cited by in Crossref: 102]  [Cited by in F6Publishing: 107]  [Article Influence: 3.6]  [Reference Citation Analysis (0)]
15.  Wild CP, Hall AJ. Primary prevention of hepatocellular carcinoma in developing countries. Mutat Res. 2000;462:381-393.  [PubMed]  [DOI]  [Cited in This Article: ]  [Cited by in Crossref: 160]  [Cited by in F6Publishing: 167]  [Article Influence: 7.3]  [Reference Citation Analysis (0)]
16.  El-Serag HB, Davila JA. Surveillance for hepatocellular carcinoma: in whom and how? Therap Adv Gastroenterol. 2011;4:5-10.  [PubMed]  [DOI]  [Cited in This Article: ]  [Cited by in Crossref: 85]  [Cited by in F6Publishing: 92]  [Article Influence: 7.7]  [Reference Citation Analysis (0)]
17.  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.  [PubMed]  [DOI]  [Cited in This Article: ]  [Cited by in Crossref: 626]  [Cited by in F6Publishing: 704]  [Article Influence: 54.2]  [Reference Citation Analysis (0)]
18.  Qiao B, Wang J, Xie J, Niu Y, Ye S, Wan Q, Ye Q. Detection and identification of peroxiredoxin 3 as a biomarker in hepatocellular carcinoma by a proteomic approach. Int J Mol Med. 2012;29:832-840.  [PubMed]  [DOI]  [Cited in This Article: ]
19.  Marshall A, Lukk M, Kutter C, Davies S, Alexander G, Odom DT. Global gene expression profiling reveals SPINK1 as a potential hepatocellular carcinoma marker. PLoS One. 2013;8:e59459.  [PubMed]  [DOI]  [Cited in This Article: ]  [Cited by in Crossref: 56]  [Cited by in F6Publishing: 56]  [Article Influence: 5.6]  [Reference Citation Analysis (0)]
20.  Lok AS, Sterling RK, Everhart JE, Wright EC, Hoefs JC, Di Bisceglie AM, Morgan TR, Kim HY, Lee WM, Bonkovsky HL. Des-gamma-carboxy prothrombin and alpha-fetoprotein as biomarkers for the early detection of hepatocellular carcinoma. Gastroenterology. 2010;138:493-502.  [PubMed]  [DOI]  [Cited in This Article: ]  [Cited by in Crossref: 376]  [Cited by in F6Publishing: 417]  [Article Influence: 32.1]  [Reference Citation Analysis (0)]
21.  Woo HG, Park ES, Thorgeirsson SS, Kim YJ. Exploring genomic profiles of hepatocellular carcinoma. Mol Carcinog. 2011;50:235-243.  [PubMed]  [DOI]  [Cited in This Article: ]  [Cited by in Crossref: 54]  [Cited by in F6Publishing: 58]  [Article Influence: 4.8]  [Reference Citation Analysis (0)]
22.  Shang S, Plymoth A, Ge S, Feng Z, Rosen HR, Sangrajrang S, Hainaut P, Marrero JA, Beretta L. Identification of osteopontin as a novel marker for early hepatocellular carcinoma. Hepatology. 2012;55:483-490.  [PubMed]  [DOI]  [Cited in This Article: ]  [Cited by in Crossref: 213]  [Cited by in F6Publishing: 229]  [Article Influence: 20.8]  [Reference Citation Analysis (0)]
23.  Rodrigues RR, Barry CT. Gene pathway analysis of hepatocellular carcinoma genomic expression datasets. J Surg Res. 2011;170:e85-e92.  [PubMed]  [DOI]  [Cited in This Article: ]  [Cited by in Crossref: 5]  [Cited by in F6Publishing: 5]  [Article Influence: 0.4]  [Reference Citation Analysis (0)]
24.  Pleguezuelo M, Lopez-Sanchez LM, Rodriguez-Ariza A, Montero JL, Briceno J, Ciria R, Muntane J, de la Mata M. Proteomic analysis for developing new biomarkers of hepatocellular carcinoma. World J Hepatol. 2010;2:127-135.  [PubMed]  [DOI]  [Cited in This Article: ]
25.  Nguyen P, Leray V, Diez M, Serisier S, Le Bloc’h J, Siliart B, Dumon H. Liver lipid metabolism. J Anim Physiol Anim Nutr (Berl). 2008;92:272-283.  [PubMed]  [DOI]  [Cited in This Article: ]  [Cited by in Crossref: 471]  [Cited by in F6Publishing: 503]  [Article Influence: 33.5]  [Reference Citation Analysis (1)]
26.  Mitra V, Metcalf J. Metabolic functions of the liver. Anaes Inten Care Med. 2009;10:334-335.  [PubMed]  [DOI]  [Cited in This Article: ]  [Cited by in Crossref: 26]  [Cited by in F6Publishing: 27]  [Article Influence: 1.9]  [Reference Citation Analysis (0)]
27.  Xu EY, Schaefer WH, Xu Q. Metabolomics in pharmaceutical research and development: metabolites, mechanisms and pathways. Curr Opin Drug Discov Devel. 2009;12:40-52.  [PubMed]  [DOI]  [Cited in This Article: ]
28.  Yang Y, Li C, Nie X, Feng X, Chen W, Yue Y, Tang H, Deng F. Metabonomic studies of human hepatocellular carcinoma using high-resolution magic-angle spinning 1H NMR spectroscopy in conjunction with multivariate data analysis. J Proteome Res. 2007;6:2605-2614.  [PubMed]  [DOI]  [Cited in This Article: ]  [Cited by in Crossref: 196]  [Cited by in F6Publishing: 206]  [Article Influence: 12.9]  [Reference Citation Analysis (0)]
29.  Patterson AD, Maurhofer O, Beyoglu D, Lanz C, Krausz KW, Pabst T, Gonzalez FJ, Dufour JF, Idle JR. Aberrant lipid metabolism in hepatocellular carcinoma revealed by plasma metabolomics and lipid profiling. Cancer Res. 2011;71:6590-6600.  [PubMed]  [DOI]  [Cited in This Article: ]  [Cited by in Crossref: 199]  [Cited by in F6Publishing: 209]  [Article Influence: 17.4]  [Reference Citation Analysis (0)]
30.  Chen T, Xie G, Wang X, Fan J, Qiu Y, Zheng X, Qi X, Cao Y, Su M, Wang X. Serum and urine metabolite profiling reveals potential biomarkers of human hepatocellular carcinoma. Mol Cell Proteomics. 2011;10:M110.004945.  [PubMed]  [DOI]  [Cited in This Article: ]
31.  Wu FX, Wang Q, Zhang ZM, Huang S, Yuan WP, Liu JY, Ban KC, Zhao YN. Identifying serological biomarkers of hepatocellular carcinoma using surface-enhanced laser desorption/ionization-time-of-flight mass spectroscopy. Cancer Lett. 2009;279:163-170.  [PubMed]  [DOI]  [Cited in This Article: ]  [Cited by in Crossref: 23]  [Cited by in F6Publishing: 23]  [Article Influence: 1.6]  [Reference Citation Analysis (0)]
32.  Cao H, Huang H, Xu W, Chen D, Yu J, Li J, Li L. Fecal metabolome profiling of liver cirrhosis and hepatocellular carcinoma patients by ultra performance liquid chromatography-mass spectrometry. Anal Chim Acta. 2011;691:68-75.  [PubMed]  [DOI]  [Cited in This Article: ]  [Cited by in Crossref: 83]  [Cited by in F6Publishing: 87]  [Article Influence: 7.3]  [Reference Citation Analysis (0)]
33.  Gao H, Lu Q, Liu X, Cong H, Zhao L, Wang H, Lin D. Application of 1H NMR-based metabonomics in the study of metabolic profiling of human hepatocellular carcinoma and liver cirrhosis. Cancer Sci. 2009;100:782-785.  [PubMed]  [DOI]  [Cited in This Article: ]  [Cited by in Crossref: 148]  [Cited by in F6Publishing: 159]  [Article Influence: 11.4]  [Reference Citation Analysis (0)]
34.  Wu H, Xue R, Dong L, Liu T, Deng C, Zeng H, Shen X. Metabolomic profiling of human urine in hepatocellular carcinoma patients using gas chromatography/mass spectrometry. Anal Chim Acta. 2009;648:98-104.  [PubMed]  [DOI]  [Cited in This Article: ]  [Cited by in Crossref: 130]  [Cited by in F6Publishing: 136]  [Article Influence: 9.7]  [Reference Citation Analysis (0)]
35.  Soga T, Sugimoto M, Honma M, Mori M, Igarashi K, Kashikura K, Ikeda S, Hirayama A, Yamamoto T, Yoshida H. Serum metabolomics reveals γ-glutamyl dipeptides as biomarkers for discrimination among different forms of liver disease. J Hepatol. 2011;55:896-905.  [PubMed]  [DOI]  [Cited in This Article: ]  [Cited by in Crossref: 184]  [Cited by in F6Publishing: 190]  [Article Influence: 15.8]  [Reference Citation Analysis (0)]
36.  Ressom HW, Xiao JF, Tuli L, Varghese RS, Zhou B, Tsai TH, Ranjbar MR, Zhao Y, Wang J, Di Poto C. Utilization of metabolomics to identify serum biomarkers for hepatocellular carcinoma in patients with liver cirrhosis. Anal Chim Acta. 2012;743:90-100.  [PubMed]  [DOI]  [Cited in This Article: ]  [Cited by in Crossref: 111]  [Cited by in F6Publishing: 120]  [Article Influence: 10.9]  [Reference Citation Analysis (0)]
37.  Xiao JF, Varghese RS, Zhou B, Nezami Ranjbar MR, Zhao Y, Tsai TH, Di Poto C, Wang J, Goerlitz D, Luo Y. LC-MS based serum metabolomics for identification of hepatocellular carcinoma biomarkers in Egyptian cohort. J Proteome Res. 2012;11:5914-5923.  [PubMed]  [DOI]  [Cited in This Article: ]  [Cited by in Crossref: 92]  [Cited by in F6Publishing: 99]  [Article Influence: 9.0]  [Reference Citation Analysis (0)]
38.  Wang B, Chen D, Chen Y, Hu Z, Cao M, Xie Q, Chen Y, Xu J, Zheng S, Li L. Metabonomic profiles discriminate hepatocellular carcinoma from liver cirrhosis by ultraperformance liquid chromatography-mass spectrometry. J Proteome Res. 2012;11:1217-1227.  [PubMed]  [DOI]  [Cited in This Article: ]  [Cited by in Crossref: 102]  [Cited by in F6Publishing: 107]  [Article Influence: 9.7]  [Reference Citation Analysis (0)]
39.  Zhou L, Ding L, Yin P, Lu X, Wang X, Niu J, Gao P, Xu G. Serum metabolic profiling study of hepatocellular carcinoma infected with hepatitis B or hepatitis C virus by using liquid chromatography-mass spectrometry. J Proteome Res. 2012;11:5433-5442.  [PubMed]  [DOI]  [Cited in This Article: ]  [Cited by in Crossref: 53]  [Cited by in F6Publishing: 57]  [Article Influence: 5.2]  [Reference Citation Analysis (0)]
40.  Nahon P, Amathieu R, Triba MN, Bouchemal N, Nault JC, Ziol M, Seror O, Dhonneur G, Trinchet JC, Beaugrand M. Identification of serum proton NMR metabolomic fingerprints associated with hepatocellular carcinoma in patients with alcoholic cirrhosis. Clin Cancer Res. 2012;18:6714-6722.  [PubMed]  [DOI]  [Cited in This Article: ]  [Cited by in Crossref: 54]  [Cited by in F6Publishing: 60]  [Article Influence: 5.5]  [Reference Citation Analysis (0)]
41.  Yin P, Wan D, Zhao C, Chen J, Zhao X, Wang W, Lu X, Yang S, Gu J, Xu G. 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.  [PubMed]  [DOI]  [Cited in This Article: ]  [Cited by in Crossref: 173]  [Cited by in F6Publishing: 179]  [Article Influence: 12.8]  [Reference Citation Analysis (0)]
42.  Li S, Liu H, Jin Y, Lin S, Cai Z, Jiang Y. Metabolomics study of alcohol-induced liver injury and hepatocellular carcinoma xenografts in mice. J Chromatogr B Analyt Technol Biomed Life Sci. 2011;879:2369-2375.  [PubMed]  [DOI]  [Cited in This Article: ]  [Cited by in Crossref: 40]  [Cited by in F6Publishing: 44]  [Article Influence: 3.7]  [Reference Citation Analysis (0)]
43.  Budhu A, Roessler S, Zhao X, Yu Z, Forgues M, Ji J, Karoly E, Qin LX, Ye QH, Jia HL. Integrated metabolite and gene expression profiles identify lipid biomarkers associated with progression of hepatocellular carcinoma and patient outcomes. Gastroenterology. 2013;144:1066-1075.e1.  [PubMed]  [DOI]  [Cited in This Article: ]  [Cited by in Crossref: 149]  [Cited by in F6Publishing: 162]  [Article Influence: 16.2]  [Reference Citation Analysis (0)]
44.  Beyoğlu D, Imbeaud S, Maurhofer O, Bioulac-Sage P, Zucman-Rossi J, Dufour JF, Idle JR. Tissue metabolomics of hepatocellular carcinoma: tumor energy metabolism and the role of transcriptomic classification. Hepatology. 2013;58:229-238.  [PubMed]  [DOI]  [Cited in This Article: ]  [Cited by in Crossref: 145]  [Cited by in F6Publishing: 154]  [Article Influence: 15.4]  [Reference Citation Analysis (0)]
45.  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.  [PubMed]  [DOI]  [Cited in This Article: ]  [Cited by in Crossref: 71]  [Cited by in F6Publishing: 77]  [Article Influence: 8.6]  [Reference Citation Analysis (0)]
46.  Gao R, Cheng J, Fan C, Shi X, Cao Y, Sun B, Ding H, Hu C, Dong F, Yan X. Serum Metabolomics to Identify the Liver Disease-Specific Biomarkers for the Progression of Hepatitis to Hepatocellular Carcinoma. Sci Rep. 2015;5:18175.  [PubMed]  [DOI]  [Cited in This Article: ]  [Cited by in Crossref: 82]  [Cited by in F6Publishing: 96]  [Article Influence: 12.0]  [Reference Citation Analysis (0)]
47.  Meyer K, Jia Y, Cao WQ, Kashireddy P, Rao MS. Expression of peroxisome proliferator-activated receptor alpha, and PPARalpha regulated genes in spontaneously developed hepatocellular carcinomas in fatty acyl-CoA oxidase null mice. Int J Oncol. 2002;21:1175-1180.  [PubMed]  [DOI]  [Cited in This Article: ]
48.  Lalwani ND, Reddy MK, Qureshi SA, Reddy JK. Development of hepatocellular carcinomas and increased peroxisomal fatty acid beta-oxidation in rats fed [4-chloro-6-(2,3-xylidino)-2-pyrimidinylthio] acetic acid (Wy-14,643) in the semipurified diet. Carcinogenesis. 1981;2:645-650.  [PubMed]  [DOI]  [Cited in This Article: ]  [Cited by in Crossref: 74]  [Cited by in F6Publishing: 78]  [Article Influence: 1.9]  [Reference Citation Analysis (0)]
49.  Reddy JK, Hashimoto T. Peroxisomal beta-oxidation and peroxisome proliferator-activated receptor alpha: an adaptive metabolic system. Annu Rev Nutr. 2001;21:193-230.  [PubMed]  [DOI]  [Cited in This Article: ]  [Cited by in Crossref: 680]  [Cited by in F6Publishing: 698]  [Article Influence: 31.7]  [Reference Citation Analysis (0)]
50.  Christensen E, Woldseth B, Hagve TA, Poll-The BT, Wanders RJ, Sprecher H, Stokke O, Christophersen BO. Peroxisomal beta-oxidation of polyunsaturated long chain fatty acids in human fibroblasts. The polyunsaturated and the saturated long chain fatty acids are retroconverted by the same acyl-CoA oxidase. Scand J Clin Lab Invest Suppl. 1993;215:61-74.  [PubMed]  [DOI]  [Cited in This Article: ]  [Cited by in Crossref: 47]  [Cited by in F6Publishing: 50]  [Article Influence: 1.7]  [Reference Citation Analysis (0)]
51.  Ojala PJ, Hirvonen TE, Hermansson M, Somerharju P, Parkkinen J. Acyl chain-dependent effect of lysophosphatidylcholine on human neutrophils. J Leukoc Biol. 2007;82:1501-1509.  [PubMed]  [DOI]  [Cited in This Article: ]  [Cited by in Crossref: 73]  [Cited by in F6Publishing: 76]  [Article Influence: 4.8]  [Reference Citation Analysis (0)]
52.  Furuya H, Shimizu Y, Kawamori T. Sphingolipids in cancer. Cancer Metastasis Rev. 2011;30:567-576.  [PubMed]  [DOI]  [Cited in This Article: ]  [Cited by in Crossref: 89]  [Cited by in F6Publishing: 93]  [Article Influence: 8.5]  [Reference Citation Analysis (0)]
53.  Bao M, Chen Z, Xu Y, Zhao Y, Zha R, Huang S, Liu L, Chen T, Li J, Tu H. Sphingosine kinase 1 promotes tumour cell migration and invasion via the S1P/EDG1 axis in hepatocellular carcinoma. Liver Int. 2012;32:331-338.  [PubMed]  [DOI]  [Cited in This Article: ]  [Cited by in Crossref: 77]  [Cited by in F6Publishing: 79]  [Article Influence: 7.2]  [Reference Citation Analysis (0)]
54.  Yang L, Yue S, Yang L, Liu X, Han Z, Zhang Y, Li L. Sphingosine kinase/sphingosine 1-phosphate (S1P)/S1P receptor axis is involved in liver fibrosis-associated angiogenesis. J Hepatol. 2013;59:114-123.  [PubMed]  [DOI]  [Cited in This Article: ]  [Cited by in Crossref: 79]  [Cited by in F6Publishing: 80]  [Article Influence: 8.0]  [Reference Citation Analysis (0)]
55.  El-Mir MY, Badia MD, Luengo N, Monte MJ, Marin JJ. Increased levels of typically fetal bile acid species in patients with hepatocellular carcinoma. Clin Sci (Lond). 2001;100:499-508.  [PubMed]  [DOI]  [Cited in This Article: ]  [Cited by in Crossref: 25]  [Cited by in F6Publishing: 25]  [Article Influence: 1.1]  [Reference Citation Analysis (0)]
56.  El-Houseini ME, Amer MA, El-Din AS, El-Sherbiny M, Hussein TD, Mansour O. Evaluation of Serum Total Bile Acids in the Diagnosis of Hepatocellular Carcinoma. Cancer Inst. 2000;12:307-313.  [PubMed]  [DOI]  [Cited in This Article: ]
57.  Thompson J, Zhang J, Vanden Heuvel J. Activation of farnesoid-X-receptor (FXR) by bioactive lipids.  Available from:  [PubMed]  [DOI]  [Cited in This Article: ]
58.  Fujino T, Takeuchi A, Maruko-Ohtake A, Ohtake Y, Satoh J, Kobayashi T, Tanaka T, Ito H, Sakamaki R, Kashimura R. Critical role of farnesoid X receptor for hepatocellular carcinoma cell proliferation. J Biochem. 2012;152:577-586.  [PubMed]  [DOI]  [Cited in This Article: ]  [Cited by in Crossref: 20]  [Cited by in F6Publishing: 20]  [Article Influence: 1.8]  [Reference Citation Analysis (0)]
59.  Kumagai A, Fukushima J, Takikawa H, Fukuda T, Fukusato T. Enhanced expression of farnesoid X receptor in human hepatocellular carcinoma. Hepatol Res. 2013;43:959-969.  [PubMed]  [DOI]  [Cited in This Article: ]  [Cited by in Crossref: 13]  [Cited by in F6Publishing: 13]  [Article Influence: 1.3]  [Reference Citation Analysis (0)]
60.  Marra M, Sordelli IM, Lombardi A, Lamberti M, Tarantino L, Giudice A, Stiuso P, Abbruzzese A, Sperlongano R, Accardo M. Molecular targets and oxidative stress biomarkers in hepatocellular carcinoma: an overview. J Transl Med. 2011;9:171.  [PubMed]  [DOI]  [Cited in This Article: ]  [Cited by in Crossref: 158]  [Cited by in F6Publishing: 170]  [Article Influence: 14.2]  [Reference Citation Analysis (0)]
61.  Suzuki Y, Imai K, Takai K, Hanai T, Hayashi H, Naiki T, Nishigaki Y, Tomita E, Shimizu M, Moriwaki H. Hepatocellular carcinoma patients with increased oxidative stress levels are prone to recurrence after curative treatment: a prospective case series study using the d-ROM test. J Cancer Res Clin Oncol. 2013;139:845-852.  [PubMed]  [DOI]  [Cited in This Article: ]  [Cited by in Crossref: 37]  [Cited by in F6Publishing: 38]  [Article Influence: 3.8]  [Reference Citation Analysis (0)]
62.  Kim JW, Dang CV. Cancer’s molecular sweet tooth and the Warburg effect. Cancer Res. 2006;66:8927-8930.  [PubMed]  [DOI]  [Cited in This Article: ]  [Cited by in Crossref: 888]  [Cited by in F6Publishing: 940]  [Article Influence: 55.3]  [Reference Citation Analysis (0)]
63.  Luo J. Cancer’s sweet tooth for serine. Breast Cancer Res. 2011;13:317.  [PubMed]  [DOI]  [Cited in This Article: ]  [Cited by in Crossref: 24]  [Cited by in F6Publishing: 26]  [Article Influence: 2.2]  [Reference Citation Analysis (0)]
64.  Locasale JW, Grassian AR, Melman T, Lyssiotis CA, Mattaini KR, Bass AJ, Heffron G, Metallo CM, Muranen T, Sharfi H. Phosphoglycerate dehydrogenase diverts glycolytic flux and contributes to oncogenesis. Nat Genet. 2011;43:869-874.  [PubMed]  [DOI]  [Cited in This Article: ]  [Cited by in Crossref: 766]  [Cited by in F6Publishing: 801]  [Article Influence: 66.8]  [Reference Citation Analysis (0)]
65.  Possemato R, Marks KM, Shaul YD, Pacold ME, Kim D, Birsoy K, Sethumadhavan S, Woo HK, Jang HG, Jha AK. Functional genomics reveal that the serine synthesis pathway is essential in breast cancer. Nature. 2011;476:346-350.  [PubMed]  [DOI]  [Cited in This Article: ]  [Cited by in Crossref: 1068]  [Cited by in F6Publishing: 1124]  [Article Influence: 93.7]  [Reference Citation Analysis (0)]
66.  Jain M, Nilsson R, Sharma S, Madhusudhan N, Kitami T, Souza AL, Kafri R, Kirschner MW, Clish CB, Mootha VK. Metabolite profiling identifies a key role for glycine in rapid cancer cell proliferation. Science. 2012;336:1040-1044.  [PubMed]  [DOI]  [Cited in This Article: ]  [Cited by in Crossref: 1023]  [Cited by in F6Publishing: 1009]  [Article Influence: 91.7]  [Reference Citation Analysis (0)]
67.  Kim Y, Lin Q, Glazer PM, Yun Z. Hypoxic tumor microenvironment and cancer cell differentiation. Curr Mol Med. 2009;9:425-434.  [PubMed]  [DOI]  [Cited in This Article: ]  [Cited by in Crossref: 113]  [Cited by in F6Publishing: 127]  [Article Influence: 9.1]  [Reference Citation Analysis (0)]
68.  Chaneton B, Hillmann P, Zheng L, Martin AC, Maddocks OD, Chokkathukalam A, Coyle JE, Jankevics A, Holding FP, Vousden KH. Serine is a natural ligand and allosteric activator of pyruvate kinase M2. Nature. 2012;491:458-462.  [PubMed]  [DOI]  [Cited in This Article: ]  [Cited by in Crossref: 406]  [Cited by in F6Publishing: 424]  [Article Influence: 38.5]  [Reference Citation Analysis (0)]
69.  Hannelien V, Karel G, Jo VD, Sofie S. The role of CXC chemokines in the transition of chronic inflammation to esophageal and gastric cancer. Biochim Biophys Acta. 2012;1825:117-129.  [PubMed]  [DOI]  [Cited in This Article: ]
70.  Kong J, Kong L, Kong J, Ke S, Gao J, Ding X, Zheng L, Sun H, Sun W. After insufficient radiofrequency ablation, tumor-associated endothelial cells exhibit enhanced angiogenesis and promote invasiveness of residual hepatocellular carcinoma. J Transl Med. 2012;10:230.  [PubMed]  [DOI]  [Cited in This Article: ]  [Cited by in Crossref: 49]  [Cited by in F6Publishing: 56]  [Article Influence: 5.1]  [Reference Citation Analysis (0)]
71.  Bechara C, Chai H, Lin PH, Yao Q, Chen C. Growth related oncogene-alpha (GRO-alpha): roles in atherosclerosis, angiogenesis and other inflammatory conditions. Med Sci Monit. 2007;13:RA87-RA90.  [PubMed]  [DOI]  [Cited in This Article: ]
72.  Smith DF, Galkina E, Ley K, Huo Y. GRO family chemokines are specialized for monocyte arrest from flow. Am J Physiol Heart Circ Physiol. 2005;289:H1976-H1984.  [PubMed]  [DOI]  [Cited in This Article: ]  [Cited by in Crossref: 87]  [Cited by in F6Publishing: 95]  [Article Influence: 5.3]  [Reference Citation Analysis (0)]
73.  Zhao XL, Sun T, Che N, Sun D, Zhao N, Dong XY, Gu Q, Yao Z, Sun BC. Promotion of hepatocellular carcinoma metastasis through matrix metalloproteinase activation by epithelial-mesenchymal transition regulator Twist1. J Cell Mol Med. 2011;15:691-700.  [PubMed]  [DOI]  [Cited in This Article: ]  [Cited by in Crossref: 94]  [Cited by in F6Publishing: 103]  [Article Influence: 8.6]  [Reference Citation Analysis (0)]
74.  Ebert MP, Lamer S, Meuer J, Malfertheiner P, Reymond M, Buschmann T, Röcken C, Seibert V. Identification of the thrombin light chain a as the single best mass for differentiation of gastric cancer patients from individuals with dyspepsia by proteome analysis. J Proteome Res. 2005;4:586-590.  [PubMed]  [DOI]  [Cited in This Article: ]  [Cited by in Crossref: 30]  [Cited by in F6Publishing: 31]  [Article Influence: 1.7]  [Reference Citation Analysis (0)]
75.  Szutowicz A, Tomaszewicz M, Bielarczyk H. Disturbances of acetyl-CoA, energy and acetylcholine metabolism in some encephalopathies. Acta Neurobiol Exp (Wars). 1996;56:323-339.  [PubMed]  [DOI]  [Cited in This Article: ]
76.  Kreis R, Farrow N, Ross BD. Localized 1H NMR spectroscopy in patients with chronic hepatic encephalopathy. Analysis of changes in cerebral glutamine, choline and inositols. NMR Biomed. 1991;4:109-116.  [PubMed]  [DOI]  [Cited in This Article: ]
77.  Bedossa P, Carrat F. Liver biopsy: the best, not the gold standard. J Hepatol. 2009;50:1-3.  [PubMed]  [DOI]  [Cited in This Article: ]  [Cited by in Crossref: 228]  [Cited by in F6Publishing: 236]  [Article Influence: 16.9]  [Reference Citation Analysis (0)]