Integrated metabolomic profiling of hepatocellular carcinoma in hepatitis C cirrhosis through GC/MS and UPLC/MS-MS

Reliability of Serum-Derived Connectome Indicators in Identifying Cirrhosis

Patients with cirrhosis face a heightened risk of complications, underscoring the importance of identification. We have developed a Connectome strategy that combines metabolites with peptide spectral matching (PSM) in proteomics to integrate metabolomics and proteomics, identifying specific metabolites bound to blood proteins in cirrhosis using open search proteomics methods. Analysis methods including Partial Least Squares Discriminant Analysis (PLS-DA), Uniform Manifold Approximation and Projection (UMAP), and hierarchical clustering were used to distinguish significant differences among the Cirrhosis group, Chronic Hepatitis B (CHB) group, and Healthy group. In this study, we identified 81 cirrhosis-associated connectomes and established an effective model distinctly distinguishing cirrhosis from chronic hepatitis B and healthy samples, confirmed by PLS-DA, hierarchical clustering analysis, and UMAP analysis, and further validated using six new cirrhosis samples. We established a Unified Indicator for Identifying cirrhosis, including tyrosine, Unnamed_189.2, thiazolidine, etc., which not only enables accurate identification of cirrhosis groups but was also further validated using six new cirrhosis samples and extensively supported by other cirrhosis research data (PXD035024). Our study reveals that characteristic cirrhosis connectomes can reliably distinguish cirrhosis from CHB and healthy groups. The established unified cirrhotic indicator facilitates the identification of cirrhosis cases in both this study and additional research data.

Biomarker Discovery in Liver Disease Using Untargeted Metabolomics in Plasma and Saliva

Chronic liver diseases, including non-alcoholic fatty liver disease (NAFLD), cirrhosis, and hepatocellular carcinoma (HCC), continue to be a global health burden with a rise in incidence and mortality, necessitating a need for the discovery of novel biomarkers for HCC detection. This study aimed to identify novel non-invasive biomarkers for these different liver disease states. We performed untargeted metabolomics in plasma (Healthy = 9, NAFLD = 14, Cirrhosis = 10, HCC = 34) and saliva samples (Healthy = 9, NAFLD = 14, Cirrhosis = 10, HCC = 22) to test for significant metabolite associations with each disease state. Additionally, we identified enriched biochemical pathways and analyzed correlations of metabolites between, and within, the two biofluids. We identified two salivary metabolites and 28 plasma metabolites significantly associated with at least one liver disease state. No metabolites were significantly correlated between biofluids, but we did identify numerous metabolites correlated within saliva and plasma, respectively. Pathway analysis revealed significant pathways enriched within plasma metabolites for several disease states. Our work provides a detailed analysis of the altered metabolome at various stages of liver disease while providing some context to altered pathways and relationships between metabolites.

Differences in Prediagnostic Serum Metabolomic and Lipidomic Profiles Between Cirrhosis Patients with and without Incident Hepatocellular Carcinoma

Background

Early detection of hepatocellular carcinoma (HCC) is crucial for improving patient outcomes, but we lack robust clinical biomarkers. This study aimed to identify a metabolite and/or lipid panel for early HCC detection.

Methods

We developed a high-resolution liquid chromatography mass spectrometry (LC-MS)-based profiling platform and evaluated differences in the global metabolome and lipidome between 28 pre-diagnostic serum samples from patients with cirrhosis who subsequently developed HCC (cases) and 30 samples from patients with cirrhosis and no HCC (controls). We linked differentially expressed metabolites and lipids to their associated genes, proteins, and transcriptomic signatures in publicly available datasets. We used machine learning models to identify a minimal panel to distinguish between cases and controls.

Results

Among cases compared with controls, 124 metabolites and 246 lipids were upregulated, while 208 metabolites and 73 lipids were downregulated. The top upregulated metabolites were glycoursodeoxycholic acid, 5-methyltetrahydrofolic acid, octanoyl-coenzyme A, and glycocholic acid. Elevated lipids comprised glycerol lipids, cardiolipin, and phosphatidylethanolamine, whereas suppressed lipids included oxidized phosphatidylcholine and lysophospholipids. There was an overlap between differentially expressed metabolites and lipids and previously published transcriptomic signatures, illustrating an association with liver disease severity. A panel of 12 metabolites that distinguished between cases and controls with an area under the receiver operating curve of 0.98 for the support vector machine (interquartile range, 0.9-1).

Conclusion

Using prediagnostic serum samples, we identified a promising metabolites panel that accurately identifies patients with cirrhosis who progressed to HCC. Further validation of this panel is required.

Chlordecone-induced hepatotoxicity and fibrosis are mediated by the proteasomal degradation of septins

Chlordecone (CLD) is a pesticide persisting in soils and contaminating food webs. CLD is sequestered in the liver and poorly metabolized into chlordecol (CLDOH). In vitro liver cell models were used to investigate the fate and mechanistic effects of CLD and CLDOH using multiomics. A 3D-cell model was used to investigate whether CLD and CLDOH can affect susceptibility to the metabolic dysfunction-associated steatotic liver disease (MASLD). Hepatocytes were more sensitive to CLD than CLDOH. CLDOH was intensively metabolized into a glucuronide conjugate, whereas CLD was sequestered. CLD but not CLDOH induced a depletion of Septin-2,- 7,- 9,- 10,- 11 due to proteasomal degradation. Septin binding with CLD and CLDOH was confirmed by surface plasmon resonance. CLD disrupted lipid droplet size and increased saturated long-chain dicarboxylic acid production by inhibiting stearoyl-CoA desaturase (SCD) abundance. Neither CLD nor CLDOH induced steatosis, but CLD induced fibrosis in the 3D model of MASLD. To conclude, CLD hepatoxicity is specifically driven by the degradation of septins. CLDOH, was too rapidly metabolized to induce septin degradation. We show that the conversion of CLD to CLDOH reduced hepatotoxicity and fibrosis in liver organoids. This suggests that protective strategies could be explored to reduce the hepatotoxicity of CLD.

Sophora flavescens-Astragalus mongholicus herb pair in the progression of hepatitis, cirrhosis, and hepatocellular carcinoma: a possible mechanisms and relevant therapeutic substances

Background

Both Sophora flavescens (SF) and Astragalus mongholicus (AM) are known for their anti-inflammatory, antifibrotic, and anticancer activities. However, the efficacy, multi-target mechanisms, and therapeutic substances of SF-AM herb pair on the progression of hepatitis-cirrhosis-hepatocellular carcinoma hepatocellular carcinoma (HCC) remain unclear.

Purpose

To investigate the efficacy, mechanisms, and potential therapeutic substances of SF-AM herb pair in the progression of hepatitis-cirrhosis-HCC.

Methods

Firstly, diethylnitrosamine was used to establish the hepatitis-cirrhosis-HCC model. HE staining and non-targeted metabolomics were used to evaluate the efficacy of SF-AM herb pair. Subsequently, the absorbed components of SF-AM herb pair in the plasma of rats were determined through HPLC-Q-TOF-MS/MS analysis. Flow cytometry, Western blot, and qRT-PCR were then employed to assess CD4+ and CD8+ T lymphocytes, PI3K/Akt signaling pathway-related proteins, and their corresponding mRNAs. Simultaneously, the efficacy and mechanism of SF-AM herb pair on HCC were confirmed by in vitro experiments. Finally, Pearson correlation analysis was performed between pharmacodynamic indicators and in vivo components to identify the potential therapeutic substances of SF-AM herb pair.

Results

SF-AM herb pair can alleviate the pathological damage and reverse metabolic abnormalities in hepatitis, cirrhosis, and HCC rats, particularly during the hepatitis and cirrhosis stages. Pharmacological researches have demonstrated that SF-AM herb pair can increase the proportion of CD8+ T lymphocytes, inhibit the expression of PI3K, Akt, p-Akt, NF-κB p65, NF-κB pp65, and Bcl-2, as well as increase the expression of IκBα, Bax, and cleaved caspase-3. These findings suggest that SF-AM herb pair has the ability to enhance immunity, anti-inflammation and promote apoptosis. Cell experiments have shown that SF-AM herb pair can inhibit the proliferation of HepG2 cell and regulate the PI3K/Akt signaling pathway. Moreover, 23 absorbed prototypical components and 53 metabolites of SF-AM herb pair were identified at different stages of HCC rats. Pearson correlation analysis revealed that matrine, cytisine, wogonoside, and isoastragaloside are potential therapeutic substances in SF-AM herb pair for the prevention and treatment of hepatitis, cirrhosis, and HCC.

Conclusion

In summary, this study revealed the efficacy, mechanisms, and potential therapeutic substances of SF-AM herb pair in the hepatitis-cirrhosis-HCC axis and provided a reference for its clinical application.

Comprehensive spectroscopic, metabolomic, and proteomic liquid biopsy in the diagnostics of hepatocellular carcinoma

Liquid biopsy is a very topical issue in clinical diagnostics research nowadays. In this study, we explored and compared various analytical approaches to blood plasma analysis. Finally, we proposed a comprehensive procedure, which, thanks to the utilization of multiple analytical techniques, allowed the targeting of various biomolecules in blood plasma reflecting diverse biological processes underlying disease development. The potential of such an approach, combining proteomics, metabolomics, and vibrational spectroscopy along with preceding blood plasma fractionation, was demonstrated on blood plasma samples of patients suffering from hepatocellular carcinoma in cirrhotic terrain (n = 20) and control subjects with liver cirrhosis (n = 20) as well as healthy subjects (n = 20). Most of the applied methods allowed the classification of the samples with an accuracy exceeding 80.0 % and therefore have the potential to be used as a stand-alone method in clinical diagnostics. Moreover, a final panel of 48 variables obtained by a combination of the utilized analytical methods enabled the discrimination of the hepatocellular carcinoma samples from cirrhosis with 94.3 % cross-validated accuracy. Thus, this study, although limited by the cohort size, clearly demonstrated the benefit of the multimethod approach in clinical diagnosis.

Metabolomics in liver diseases: A novel alternative for liver biopsy?

Hepatitis C virus (HCV) remains a significant public health problem as it can cause acute and chronic hepatitis. Chronic HCV infection is a major cause of liver fibrosis, and evaluation of liver fibrosis is essential because the prognosis of patients with chronic HCV infection is closely related to the stage of fibrosis. Liver fibrosis is traditionally evaluated based on pathological analysis of biopsy specimens, which is considered the gold standard. Nevertheless, liver biopsy is invasive and susceptible to sampling error and inter- and intraobserver variation in pathological interpretation; it is also costly. Therefore, noninvasive diagnostic investigations have been developed, including the use of fibrotic markers, scoring systems based on routine blood tests, and transient elastography with magnetic resonance imaging or ultrasonography. Recently, metabolomics, an emerging technology, has been used to detect the fibrosis stage. In this editorial, I comment on the article titled "Metabolomics in chronic hepatitis C: Decoding fibrosis grading and underlying pathways" by Ferrasi et al published in the recent issue of the World Journal of Hepatology. I discuss previous studies on the use of metabolome analysis for the diagnosis of HCV-related liver fibrosis and the potential development of biopsy-free diagnostic techniques.

Study on toxicity/efficacy related substances and metabolic mechanism of Tripterygium wilfordii Hook. f based on O2LPS correlation analysis

ETHNOPHARMACOLOGICAL RELEVANCE

Tripterygium wilfordii Hook. f (TwHF) has been used as a traditional Chinese medicine for the treatment of rheumatoid arthritis and nephritis for hundreds of years.

AIM OF THE STUDY

Although the efficacy of TwHF in the treatment of RA is definite, its serious side effects and toxicity have also received close attention from domestic and international researchers, so the clinical application of TwHF has been controversial. Most of the current TwHF toxicity studies have been conducted with animals in normal body states, but ignore the effects in pathological states. In this study, we aimed to find out the material basis and metabolic mechanism of the "toxicity/effectiveness" of TwHF on rat kidneys in different body states by using two-way orthogonal partial least squares (O2PLS) method.

MATERIALS AND METHODS

In the present study, TwHF was extracted by reflux extraction method using ethanol as the extraction solvent. Firstly, the effects of TwHF on rat kidneys in different body states were first evaluated by detecting creatinine and urea nitrogen levels and morphological changes in kidney pathology identified the components of TwHF in rats in different body states using UPLC-Q-TOF/MS technique. Serum and urine metabolomics were used to search for biomarkers and metabolic pathways by which TwHF exerts renal injury and protection, and finally, O2PLS correlation analysis was used to correlate the components with renal protective and injury biomarkers.

RESULTS

TwHF was found to have a protective effect on the kidney of RA rats and an injurious effect on the kidney of normal rats at a dose of 11.25 g/kg/d. The UPLC-Q-TOF/MS technique was used to identify 34 components in TwHF extracts; 23 components and 57 metabolites were identified in the administered rats. O2PLS screened three substances as both toxic and pharmacodynamic components of TwHF, namely 3,5-dimethoxyphenyl-2-propenl-ol, kaurane-16,19,20-triol, and demethylzeylasteral + O, and found that these three components may exert nephrotoxic effects via the nicotinic acid and nicotinamide metabolic pathways and nephroprotective effects via the tryptophan metabolic pathway.

CONCLUSION

In this study, O2PLS analysis was used for the first time to combine biomarkers and components in vivo and found the material basis and metabolic mechanism of nephrotoxicity and efficacy of TwHF, which provided key clues for further study on the biological mechanism of toxicity and efficacy of TwHF.

Arachidonic acid metabolism CYP450 pathway is deregulated in hepatocellular carcinoma and associated with microvascular invasion

Arachidonic acid metabolism plays a crucial role in the development and progression of inflammatory and metabolic liver diseases. However, its role in hepatocellular carcinoma (HCC) remains unclear. In this study, we investigated the expression of key genes involved in the arachidonic acid metabolism pathway in HCC using a combination of bioinformatics, proteomics and immunohistochemistry analyses. Through a comprehensive analysis of publicly available datasets, clinical HCC tissues, and tissue microarrays, we compared the expression of hepatic arachidonic acid metabolic genes. We observed significant downregulation of cytochrome P450 (CYP450) pathway genes at both the messenger RNA and protein levels in HCC tissues compared to normal liver tissues. Furthermore, we observed a strong correlation between the deregulation of the arachidonic acid metabolism CYP450 pathway and the pathological features and prognosis of HCC. Specifically, the expression of CYP2C8/9/18/19 was significantly correlated with pathological grade (r = -.484, p < .0001), vascular invasion (r = -.402, p < .0001), aspartate transaminase (r = -.246, p = .025), gamma-glutamyl transpeptidase (r = -.252, p = .022), alkaline phosphatase (r = -.342, p = .002), alpha-fetoprotein (r = -.311, p = .004) and carbohydrate antigen 19-9 (r = -.227, p = .047). Moreover, we discovered a significant association between CYP450 pathway activity and vascular invasion in HCC. Collectively, these data indicate that arachidonic acid CYP450 metabolic pathway deregulation is implicated in HCC progression and may be a potential predictive factor for early recurrence in patients with HCC.

Targeting metabolic reprogramming in hepatocellular carcinoma to overcome therapeutic resistance: A comprehensive review

Hepatocellular carcinoma (HCC) poses a heavy burden on human health with high morbidity and mortality rates. Systematic therapy is crucial for advanced and mid-term HCC, but faces a significant challenge from therapeutic resistance, weakening drug effectiveness. Metabolic reprogramming has gained attention as a key contributor to therapeutic resistance. Cells change their metabolism to meet energy demands, adapt to growth needs, or resist environmental pressures. Understanding key enzyme expression patterns and metabolic pathway interactions is vital to comprehend HCC occurrence, development, and treatment resistance. Exploring metabolic enzyme reprogramming and pathways is essential to identify breakthrough points for HCC treatment. Targeting metabolic enzymes with inhibitors is key to addressing these points. Inhibitors, combined with systemic therapeutic drugs, can alleviate resistance, prolong overall survival for advanced HCC, and offer mid-term HCC patients a chance for radical resection. Advances in metabolic research methods, from genomics to metabolomics and cells to organoids, help build the HCC metabolic reprogramming network. Recent progress in biomaterials and nanotechnology impacts drug targeting and effectiveness, providing new solutions for systemic therapeutic drug resistance. This review focuses on metabolic enzyme changes, pathway interactions, enzyme inhibitors, research methods, and drug delivery targeting metabolic reprogramming, offering valuable references for metabolic approaches to HCC treatment.

Characterization of Salivary and Plasma Metabolites as Biomarkers for HCC: A Pilot Study

(1) Background: The incidence of hepatocellular carcinoma (HCC) is rising, and current screening methods lack sensitivity. This study aimed to identify distinct and overlapping metabolites in saliva and plasma that are significantly associated with HCC. (2) Methods: Saliva samples were collected from 42 individuals (HCC = 16, cirrhosis = 12, healthy = 14), with plasma samples from 22 (HCC = 14, cirrhosis = 2, healthy = 6). We performed untargeted mass spectrometry on blood and plasma, tested metabolites for associations with HCC or cirrhosis using a logistic regression, and identified enriched pathways with Metaboanalyst. Pearson's correlation was employed to test for correlations between salivary and plasma metabolites. (3) Results: Six salivary metabolites (1-hexadecanol, isooctanol, malonic acid, N-acetyl-valine, octadecanol, and succinic acid) and ten plasma metabolites (glycine, 3-(4-hydroxyphenyl)propionic acid, aconitic acid, isocitric acid, tagatose, cellobiose, fucose, glyceric acid, isocitric acid, isothreonic acid, and phenylacetic acid) were associated with HCC. Malonic acid was correlated between the paired saliva and plasma samples. Pathway analysis highlighted deregulation of the 'The Citric Acid Cycle' in both biospecimens. (4) Conclusions: Our study suggests that salivary and plasma metabolites may serve as independent sources for HCC detection. Despite the lack of correlation between individual metabolites, they converge on 'The Citric Acid Cycle' pathway, implicated in HCC pathogenesis.

Metabolomics Approaches for the Diagnosis, Treatment, and Better Disease Management of Viral Infections

Metabolomics is an analytical approach that involves profiling and comparing the metabolites present in biological samples. This scoping review article offers an overview of current metabolomics approaches and their utilization in evaluating metabolic changes in biological fluids that occur in response to viral infections. Here, we provide an overview of metabolomics methods including high-throughput analytical chemistry and multivariate data analysis to identify the specific metabolites associated with viral infections. This review also focuses on data interpretation and applications designed to improve our understanding of the pathogenesis of these viral diseases.

Measurement of plasma protein and whole body protein metabolism using [15N]glycine in a young adult man - a pilot study

A novel simplified method is presented for the estimation of the metabolism of plasma proteins (albumin, fibrinogen, α, β and γ-globulin, glycoprotein) with regard to the whole body protein metabolism in a young male volunteer (22 years, 81 kg body mass). This method is based on multiple oral administration of [15N]glycine followed by measurement of 15N in plasma proteins, total free amino acids, urea and excreted urinary N. The fractional synthesis rate of albumin was estimated to 6.8 % d-1 based on amino acids and 3.3 % d-1 based on urea, respectively. The fractional synthesis rate of the other plasma proteins ranged from 4.3 % d-1 (γ-globulin) to 26.4 % d-1 (α-globulin, fibrinogen). We conclude that the simplified approach using [15N]glycine provides results which are similar to results based on the simultaneously applied 131I-human serum albumin technique as 'gold standard' and to those reported in literature. The compartmental analysis considering comprehensive tracer kinetic data ensures reliable data treatment and enables statistical evaluation. The analytical effort is minimal because the 15N enrichment of plasma protein after chemical digestion may be directly used. Therefore, the novel stable isotope 15N method is suitable for studies in clinical and nutritional research and practice.

Metabolomics in viral hepatitis: advances and review

Viral hepatitis is a major worldwide public health issue, affecting hundreds of millions of people and causing substantial morbidity and mortality. The majority of the worldwide burden of viral hepatitis is caused by five biologically unrelated hepatotropic viruses: hepatitis A virus (HAV), hepatitis B virus (HBV), hepatitis C virus (HCV), hepatitis D virus (HDV), and hepatitis E virus (HEV). Metabolomics is an emerging technology that uses qualitative and quantitative analysis of easily accessible samples to provide information of the metabolic levels of biological systems and changes in metabolic and related regulatory pathways. Alterations in glucose, lipid, and amino acid levels are involved in glycolysis, the tricarboxylic acid cycle, the pentose phosphate pathway, and amino acid metabolism. These changes in metabolites and metabolic pathways are associated with the pathogenesis and medication mechanism of viral hepatitis and related diseases. Additionally, differential metabolites can be utilized as biomarkers for diagnosis, prognosis, and therapeutic responses. In this review, we present a thorough overview of developments in metabolomics for viral hepatitis.

Blood Oxylipin Profiles as Markers of Oncological Diseases

Oxylipins are signal lipid molecules formed from polyunsaturated fatty acids (PUFAs) in several multienzymatic metabolic pathways, such as cyclooxygenase (COX), lipoxygenase (LOX), epoxygenase (CYP), and anandamide pathways, as well as non-enzymatically. The pathways of PUFA transformation are activated in parallel, yielding a mixture of physiologically active substances. Although the association of oxylipins with carcinogenesis had been established a long time ago, only recently analytical methods have advanced to a degree allowing detection and quantification of oxylipins from different classes (oxylipin profiles). The review describes current approaches to the HPLC-MS/MS analysis of oxylipin profiles and compares oxylipin profiles from patients with oncological diseases (breast cancer, colorectal cancer, ovarian cancer, lung cancer, prostate cancer, liver cancer). The possibility of using blood oxylipin profiles as biomarkers in oncological diseases is discussed. Understanding the patterns of PUFA metabolism and physiological activity of combinations of oxylipins will improve early diagnostics of oncological diseases and evaluation of disease prognosis.

2 Hydroxybutyric Acid-Producing Bacteria in Gut Microbiome and Fusobacterium nucleatum Regulates 2 Hydroxybutyric Acid Level In Vivo

2-hydroxybutyric acid (2HB) serves as an important regulatory factor in a variety of diseases. The circulating level of 2HB in serum is significantly higher in multiple diseases, such as cancer and type 2 diabetes (T2D). However, there is currently no systematic study on 2HB-producing bacteria that demonstrates whether gut bacteria contribute to the circulating 2HB pool. To address this question, we used BLASTP to reveal the taxonomic profiling of 2HB-producing bacteria in the human microbiome, which are mainly distributed in the phylum Proteobacteria and Firmicutes. In vitro experiments showed that most gut bacteria (21/32) have at least one path to produce 2HB, which includes Aspartic acid, methionine, threonine, and 2-aminobutyric acid. Particularly, Fusobacterium nucleatum has the strongest ability to synthesize 2HB, which is sufficient to alter colon 2HB concentration in mice. Nevertheless, neither antibiotic (ABX) nor Fusobacterium nucleatum gavage significantly affected mouse serum 2HB levels during the time course of this study. Taken together, our study presents the profiles of 2HB-producing bacteria and demonstrates that gut microbiota was a major contributor to 2HB concentration in the intestinal lumen but a relatively minor contributor to serum 2HB concentration.

Metabolomic analysis of vascular cognitive impairment due to hepatocellular carcinoma

Introduction

Screening for metabolically relevant differentially expressed genes (DEGs) shared by hepatocellular carcinoma (HCC) and vascular cognitive impairment (VCI) to explore the possible mechanisms of HCC-induced VCI.

Methods

Based on metabolomic and gene expression data for HCC and VCI, 14 genes were identified as being associated with changes in HCC metabolites, and 71 genes were associated with changes in VCI metabolites. Multi-omics analysis was used to screen 360 DEGs associated with HCC metabolism and 63 DEGs associated with VCI metabolism.

Results

According to the Cancer Genome Atlas (TCGA) database, 882 HCC-associated DEGs were identified and 343 VCI-associated DEGs were identified. Eight genes were found at the intersection of these two gene sets: NNMT, PHGDH, NR1I2, CYP2J2, PON1, APOC2, CCL2, and SOCS3. The HCC metabolomics prognostic model was constructed and proved to have a good prognostic effect. The HCC metabolomics prognostic model was constructed and proved to have a good prognostic effect. Following principal component analyses (PCA), functional enrichment analyses, immune function analyses, and TMB analyses, these eight DEGs were identified as possibly affecting HCC-induced VCI and the immune microenvironment. As well as gene expression and gene set enrichment analyses (GSEA), a potential drug screen was conducted to investigate the possible mechanisms involved in HCC-induced VCI. The drug screening revealed the potential clinical efficacy of A-443654, A-770041, AP-24534, BI-2536, BMS- 509744, CGP-60474, and CGP-082996.

Conclusion

HCC-associated metabolic DEGs may influence the development of VCI in HCC patients.

Eicosanoids and other oxylipins in liver injury, inflammation and liver cancer development

Liver cancer is a malignancy developed from underlying liver disease that encompasses liver injury and metabolic disorders. The progression from these underlying liver disease to cancer is accompanied by chronic inflammatory conditions in which liver macrophages play important roles in orchestrating the inflammatory response. During this process, bioactive lipids produced by hepatocytes and macrophages mediate the inflammatory responses by acting as pro-inflammatory factors, as well as, playing roles in the resolution of inflammation conditions. Here, we review the literature discussing the roles of bioactive lipids in acute and chronic hepatic inflammation and progression to cancer.

Simultaneous Quantification of Serum Lipids and Their Association with Type 2 Diabetes Mellitus-Positive Hepatocellular Cancer

Type 2 diabetes mellitus (T2DM) has been recognized as one of the most important and independent risk factors for hepatocellular cancer (HCC). However, there is still a lack of ideal tumor markers for HCC detection in the T2DM population. Serum lipids have been revealed as potential tumor markers for HCC. In this study, our objective was to develop a novel liquid chromatography-tandem mass spectrometry (LC-MS/MS) method to detect several lipids including 8,15-dihydroxy-5,9,11,13-eicosatetraenoic acid (8,15-DiHETE), hexadecanedioic acid (HDA), 15-keto-13,14-dihydroprostaglandin A2 (DHK-PGA2), ricinoleic acid (RCL), octadecanedioic acid (OA) and 16-hydroxy hexadecanoic acid (16OHHA) in serum and explore their diagnostic potential for T2DM-positive [T2DM(+)] HCC. A robust LC-MS/MS method was established for the measurement of 8,15-DiHETE, HDA, DHK-PGA2, RCL, OA, and 16OHHA. The methodology validation was conducted, and the results suggested the reliability of this LC-MS/MS method for targeted lipids. Several serum lipids, including 8,15-DiHETE, HDA, DHK-PGA2, and OA were increased in T2DM(+) HCC patients. A biomarker signature that incorporated HDA, DHK-PGA2, and AFP was established and showed good diagnostic potential for T2DM(+) HCC, and the area under the ROC curve (AUC) was 0.87 for diagnosing T2DM(+) HCC from T2DM individuals. Additionally, the biomarker signature diagnosed small-size (AUC = 0.88) and early-stage (AUC = 0.79) tumors with high efficacy. Moreover, the biomarker signature could differentiate T2DM(+) HCC from other T2DM(+) tumors, including pancreatic, gastric and colorectal cancer (AUC = 0.88) as well. In conclusion, our study develops a novel tool for early diagnosis of T2DM(+) HCC in T2DM patients.

Metabolomics in hepatocellular carcinoma: From biomarker discovery to precision medicine

Hepatocellular carcinoma (HCC) remains a global health burden, and is mostly diagnosed at late and advanced stages. Currently, limited and insensitive diagnostic modalities continue to be the bottleneck of effective and tailored therapy for HCC patients. Moreover, the complex reprogramming of metabolic patterns during HCC initiation and progression has been obstructing the precision medicine in clinical practice. As a noninvasive and global screening approach, metabolomics serves as a powerful tool to dynamically monitor metabolic patterns and identify promising metabolite biomarkers, therefore holds a great potential for the development of tailored therapy for HCC patients. In this review, we summarize the recent advances in HCC metabolomics studies, including metabolic alterations associated with HCC progression, as well as novel metabolite biomarkers for HCC diagnosis, monitor, and prognostic evaluation. Moreover, we highlight the application of multi-omics strategies containing metabolomics in biomarker discovery for HCC. Notably, we also discuss the opportunities and challenges of metabolomics in nowadays HCC precision medicine. As technologies improving and metabolite biomarkers discovering, metabolomics has made a major step toward more timely and effective precision medicine for HCC patients.

Serum metabolomics-based heterogeneities and screening strategy for metabolic dysfunction-associated fatty liver disease (MAFLD)

BACKGROUND AND AIMS

Metabolic dysfunction-associated fatty liver disease (MAFLD) brings heavy clinical and economic burdens to society, while understandings on heterogeneities are limited.

MATERIALS AND METHODS

We conducted a serum metabolomics study to reveal the metabolic heterogeneities and develop a diagnostic strategy for MAFLD using a discovery set consisting of 122 biopsy-proven MAFLD patients [lean (n = 12), overweight (n = 20), obese (n = 74), type 2 diabetes mellitus (T2DM, n = 16)] and 35 controls, and a validation set containing 60 biopsy-proven MAFLD patients (20 lean, 20 obese and 20 T2DM) and 20 controls.

RESULTS

Mitochondrial dysfunction, destructed phospholipids homeostasis, and folate deficiency were most severe in MAFLD concurrent T2DM patients. Formiminoglutamate, sphinganine and sphingosine correlated positively with HbA1c, while glycoursodeoxycholicacidsulfate correlated positively with AST. Additionally, the linear discriminant analysis (LDA) model using metabolites 5-hydroxyhexanoate, ribitol and formiminoglutamate demonstrated pretty good performance in screening for MAFLD patients, with AUC for validation samples being 0.94 (CI: 0.88-1.0). For easier clinical applications, an M-index based on the three metabolites was further designed.

CONCLUSION

Our study supports that MAFLD concurrent T2DM patients deserve particular attentions in clinical follow-up, and paves the way for developing more effective diagnostic options in future studies.

Essential amino acids as diagnostic biomarkers of hepatocellular carcinoma based on metabolic analysis

Metabolomics, defined as the comprehensive identification of all small metabolites in a biological sample, has the power to shed light on phenotypic changes associated with various diseases, including cancer. To discover potential metabolomic biomarkers of hepatocellular carcinoma (HCC), we investigated the metabolomes of tumor and non-tumor tissue in 20 patients with primary HCC using capillary electrophoresis-time-of-flight mass spectrometry. We also analyzed blood samples taken immediately before and 14 days after hepatectomy to identify associated changes in the serum metabolome. Marked changes were detected in the different quantity of 61 metabolites that could discriminate between HCC tumor and paired non-tumor tissue and additionally between HCC primary tumors and colorectal liver metastases. Among the 30 metabolites significantly upregulated in HCC tumors compared with non-tumor tissues, 10 were amino acids, and 7 were essential amino acids (leucine, valine, tryptophan, isoleucine, methionine, lysine, and phenylalanine). Similarly, the serum metabolomes of HCC patients before hepatectomy revealed a significant increase in 16 metabolites, including leucine, valine, and tryptophan. Our results reveal striking differences in the metabolomes of HCC tumor tissue compared with non-tumor tissue, and identify the essential amino acids leucine, valine, and tryptophan as potential metabolic biomarkers for HCC.

Advances in multi-omics research on viral hepatitis

Viral hepatitis is a major global public health problem that affects hundreds of millions of people and is associated with significant morbidity and mortality. Five biologically unrelated hepatotropic viruses account for the majority of the global burden of viral hepatitis, including hepatitis A virus (HAV), hepatitis B virus (HBV), hepatitis C virus (HCV), hepatitis D virus (HDV), and hepatitis E virus (HEV). Omics is defined as the comprehensive study of the functions, relationships and roles of various types of molecules in biological cells. The multi-omics analysis has been proposed and considered key to advancing clinical precision medicine, mainly including genomics, transcriptomics and proteomics, metabolomics. Overall, the applications of multi-omics can show the origin of hepatitis viruses, explore the diagnostic and prognostics biomarkers and screen out the therapeutic targets for viral hepatitis and related diseases. To better understand the pathogenesis of viral hepatitis and related diseases, comprehensive multi-omics analysis has been widely carried out. This review mainly summarizes the applications of multi-omics in different types of viral hepatitis and related diseases, aiming to provide new insight into these diseases.

Untargeted plasma metabolomics for risk prediction of hepatocellular carcinoma: A prospective study in two Chinese cohorts

Characterization of metabolic perturbation prior to hepatocellular carcinoma (HCC) may deepen the understanding of causal pathways and identify novel biomarkers for early prevention. We conducted two 1:1 matched nested case-control studies (108 and 55 pairs) to examine the association of plasma metabolome (profiled using LC-MS) with the risk of HCC based on two prospective cohorts in China. Differential metabolites were identified by paired t-tests and orthogonal partial least-squares discriminant analysis (OPLS-DA). Weighted gene co-expression network analysis (WGCNA) was performed to classify metabolites into modules for identifying biological pathways involved in hepatocarcinogenesis. We assessed the risk predictivity of metabolites using multivariable logistic regression models. Among 612 named metabolites, 44 differential metabolites were identified between cases and controls, including 12 androgenic/progestin steroid hormones, 8 bile acids, 10 amino acids, 6 phospholipids, and 8 others. These metabolites were associated with HCC in the multivariable logistic regression analyses, with odds ratios ranging from 0.19 (95% CI: 0.11-0.35) to 5.09 (95% CI: 2.73-9.50). WGCNA including 612 metabolites showed 8 significant modules related to HCC risk, including those representing metabolic pathways of androgen and progestin, primary and secondary bile acids, and amino acids. A combination of 18 metabolites of independent effects showed the potential to predict HCC risk, with an AUC of 0.87 (95% CI: 0.82-0.92) and 0.86 (95% CI: 0.80-0.93) in the training and validation sets, respectively. In conclusion, we identified a panel of plasma metabolites that could be implicated in hepatocellular carcinogenesis and have the potential to predict HCC risk.

Optimized Systematic Review Tool: Application to Candidate Biomarkers for the Diagnosis of Hepatocellular Carcinoma

This review aims to develop an appropriate review tool for systematically collating metabolites that are dysregulated in disease and applies the method to identify novel diagnostic biomarkers for hepatocellular carcinoma (HCC). Studies that analyzed metabolites in blood or urine samples where HCC was compared with comparison groups (healthy, precirrhotic liver disease, cirrhosis) were eligible. Tumor tissue was included to help differentiate primary and secondary biomarkers. Searches were conducted on Medline and EMBASE. A bespoke "risk of bias" tool for metabolomic studies was developed adjusting for analytic quality. Discriminant metabolites for each sample type were ranked using a weighted score accounting for the direction and extent of change and the risk of bias of the reporting publication. A total of 84 eligible studies were included in the review (54 blood, 9 urine, and 15 tissue), with six studying multiple sample types. High-ranking metabolites, based on their weighted score, comprised energy metabolites, bile acids, acylcarnitines, and lysophosphocholines. This new review tool addresses an unmet need for incorporating quality of study design and analysis to overcome the gaps in standardization of reporting of metabolomic data. Validation studies, standardized study designs, and publications meeting minimal reporting standards are crucial for advancing the field beyond exploratory studies.

Predictive value of serum bile acids as metabolite biomarkers for liver cirrhosis: a systematic review and meta-analysis

INTRODUCTION

A large number of studies have explored the potential biomarkers for detecting liver cirrhosis in an early stage, yet consistent conclusions are still warranted.

OBJECTIVES

To conduct a review and a meta-analysis of the existing studies that test the serum level of bile acids in cirrhosis as the potential biomarkers to predict cirrhosis.

METHODS

Six databases had been searched from inception date to April 12, 2021. Screening and selection of the records were based on the inclusion criteria. The risk of bias was assessed with the Newcastle-Ottawa quality assessment scale (NOS). Mean difference (MD) and confidence intervals 95% (95% CI) were calculated by using the random effect model for the concentrations of bile acids in the meta-analysis, and I² statistic was used to measure studies heterogeneity. This study was registered on PROSPERO.

RESULTS

A total of 1583 records were identified and 31 studies with 2679 participants (1263 in the cirrhosis group, 1416 in the healthy control group) were included. The quality of included studies was generally high, with 25 studies (80.6%) rated over 7 stars. A total of 45 bile acids or their ratios in included studies were extracted. 36 increased in the cirrhosis group compared with those of the healthy controls by a qualitative summary, 5 decreased and 4 presented with mixing results. The result of meta-analysis among 12 studies showed that 13 bile acids increased, among which four primary conjugated bile acids showed the most significant elevation in the cirrhosis group: GCDCA (MD = 11.38 μmol/L, 95% CI 8.21-14.55, P < 0.0001), GCA (MD = 5.72 μmol/L, 95% CI 3.47-7.97, P < 0.0001), TCDCA (MD = 3.57 μmol/L, 95% CI 2.64-4.49, P < 0.0001) and TCA (MD = 2.14 μmol/L, 95% CI 1.56-2.72, P < 0.0001). No significant differences were found between the two groups in terms of DCA (MD = - 0.1 μmol/L, 95% CI - 0.18 to - 0.01, P < 0.0001) and LCA (MD = - 0.01 μmol/L, 95% CI - 0.01 to - 0.02, P < 0.0001), UDCA (MD = - 0.14 μmol/L, 95% CI - 0.04 to - 0.32, P < 0.0001), and TLCA (MD = 0 μmol/L, 95% CI 0-0.01, P < 0.0001). Subgroup analysis in patients with hepatitis B cirrhosis showed similar results.

CONCLUSION

Altered serum bile acids profile seems to be associated with cirrhosis. Some specific bile acids (GCA, GCDCA, TCA, and TCDCA) may increase with the development of cirrhosis, which possibly underlay their potential role as predictive biomarkers for cirrhosis. Yet this predictive value still needs further investigation and validation in larger prospective cohort studies.

DeepHisCoM: deep learning pathway analysis using hierarchical structural component models

Many statistical methods for pathway analysis have been used to identify pathways associated with the disease along with biological factors such as genes and proteins. However, most pathway analysis methods neglect the complex nonlinear relationship between biological factors and pathways. In this study, we propose a Deep-learning pathway analysis using Hierarchical structured CoMponent models (DeepHisCoM) that utilize deep learning to consider a nonlinear complex contribution of biological factors to pathways by constructing a multilayered model which accounts for hierarchical biological structure. Through simulation studies, DeepHisCoM was shown to have a higher power in the nonlinear pathway effect and comparable power for the linear pathway effect when compared to the conventional pathway methods. Application to hepatocellular carcinoma (HCC) omics datasets, including metabolomic, transcriptomic and metagenomic datasets, demonstrated that DeepHisCoM successfully identified three well-known pathways that are highly associated with HCC, such as lysine degradation, valine, leucine and isoleucine biosynthesis and phenylalanine, tyrosine and tryptophan. Application to the coronavirus disease-2019 (COVID-19) single-nucleotide polymorphism (SNP) dataset also showed that DeepHisCoM identified four pathways that are highly associated with the severity of COVID-19, such as mitogen-activated protein kinase (MAPK) signaling pathway, gonadotropin-releasing hormone (GnRH) signaling pathway, hypertrophic cardiomyopathy and dilated cardiomyopathy. Codes are available at https://github.com/chanwoo-park-official/DeepHisCoM.

Metabolomic Approaches for Detection and Identification of Biomarkers and Altered Pathways in Bladder Cancer

Metabolomic analysis has proven to be a useful tool in biomarker discovery and the molecular classification of cancers. In order to find new biomarkers, and to better understand its pathological behavior, bladder cancer also has been studied using a metabolomics approach. In this article, we review the literature on metabolomic studies of bladder cancer, focusing on the different available samples (urine, blood, tissue samples) used to perform the studies and their relative findings. Moreover, the multi-omic approach in bladder cancer research has found novel insights into its metabolic behavior, providing excellent start-points for new diagnostic and therapeutic strategies. Metabolomics data analysis can lead to the discovery of a “signature pathway” associated with the progression of bladder cancer; this aspect could be potentially valuable in predictions of clinical outcomes and the introduction of new treatments. However, further studies are needed to give stronger evidence and to make these tools feasible for use in clinical practice.

Alcohol triggered bile acid disequilibrium by suppressing BSEP to sustain hepatocellular carcinoma progression

Bile acids (BAs), the most important components of bile, attribute predominately to maintain metabolic homeostasis. In hepatocellular carcinoma (HCC) patients, the BAs homeostasis was seriously disturbed, especially in those patients with alcohol-intake history. However, whether alcohol consumption could promote HCC progression via influencing BAs homeostasis and the precise mechanism underlying are still unclear. In our study, by collecting HCC specimens from both alcohol-drinkers (n = 15) and non-alcohol drinkers (n = 22), we found that compared to non-alcohol intake HCC patients, BAs homeostasis was disturbed in HCC patients who drank alcohol. Furthermore, ethanol treatment was also found to promote HCC progression by markedly activating oncogenes (RAS, MYC, MET, and HER2), while remarkably suppressing tumor suppressor genes (BRCA2 and APC). We evaluated 14 key functional genes that maintain the homeostasis of BAs and found that either in alcohol-intake HCC patients (n = 15), or in ethanol-treated mice, BSEP, rate-limiting transporter governing excreting BAs from liver into bile duct, was remarkably decreased when exposed to alcohol. Moreover, by screening for changes in the epigenetic landscape of liver cancer cells exposed to alcohol, we strikingly found that histone methyltransferases (RBBP-5, Suv39h1, ASH2L, and SET7/9) were increased, and KMT3B, KMT4, and KMT7 gene expression was also elevated, while histone demethyltransferases (JARID1a, JARID1b, JARID1c) were decreased. In summary, we found that alcohol could trigger BAs disequilibrium to initiate and promote HCC progression. Our study provided a novel and supplementary mechanism to determine the important role of alcohol-intake in HCC development regarding from the perspective of BAs homeostasis.

Metabolomic biomarkers for hepatocellular carcinoma: A systematic review

Hepatocellular carcinoma (HCC) is a highly malignant cancer which lack of effective diagnosis and prognosis biomarkers, therefore surging studies focused on the metabolite candidates for HCC. The current study was designed to systematically review the metabolic studies for HCC, summarize the current available evidence and provide implication for further studies within this area. By systematically screening Pubmed and Embase, and eligibility assessment, we eventually included 55 pieces of studies. After summarized their characteristics, we reviewed them by 3 parts, regarding to the different biofluid they carried out the experiments. By collecting the candidates from all the included studies, we carried out pathway enrichment to see the representative of the reported candidates, as expected the pathway consistent with the current knowledge of HCC. Next, we conduct quality assessment on the included studies. Only 36% of the current evidence grouped as high quality, indicating the quality of metabolic studies needs further improvement.

Salivary Metabolites are Promising Non-Invasive Biomarkers of Hepatocellular Carcinoma and Chronic Liver Disease

Background

Hepatocellular carcinoma (HCC) is a leading causes of cancer mortality worldwide. Improved tools are needed for detecting HCC so that treatment can begin as early as possible. Current diagnostic approaches and existing biomarkers, such as alpha-fetoprotein (AFP) lack sensitivity, resulting in too many false negative diagnoses. Machine-learning may be able to identify combinations of biomarkers that provide more robust predictions and improve sensitivity for detecting HCC. We sought to evaluate whether metabolites in patient saliva could distinguish those with HCC, cirrhosis, and those with no documented liver disease.

Methods and Results

We tested 125 salivary metabolites from 110 individuals (43 healthy, 37 HCC, 30 cirrhosis) and identified 4 metabolites that displayed significantly different abundance between groups (FDR P <.2). We also developed four tree-based, machine-learning models, optimized to include different numbers of metabolites, that were trained using cross-validation on 99 patients and validated on a withheld test set of 11 patients. A model using 12 metabolites -octadecanol, acetophenone, lauric acid, 1-monopalmitin, dodecanol, salicylaldehyde, glycyl-proline, 1-monostearin, creatinine, glutamine, serine and 4-hydroxybutyric acid- had a cross-validated sensitivity of 84.8%, specificity of 92.4% and correctly classified 90% of the HCC patients in the test cohort. This model outperformed previously reported sensitivities and specificities for AFP (20-100ng/ml) (61%, 86%) and AFP plus ultrasound (62%, 88%).

Conclusions and Impact

Metabolites detectable in saliva may represent products of disease pathology or a breakdown in liver function. Notably, combinations of salivary metabolites derived from machine-learning may serve as promising non-invasive biomarkers for the detection of HCC.

The role of metabolomics in hepatocellular carcinoma

Background

Hepatocellular carcinoma is the most common primary liver malignancy, with the highest incidence in the developing world, including Egypt. Hepatocellular carcinoma is usually diagnosed in the terminal stage of the disease because of the low sensitivity of the available screening tests. During the process of carcinogenesis, the cellular metabolism is altered to allow cancer cells to adapt to the hypoxic environment and therefore increase anabolic synthesis and survival and avoid the apoptotic death signals. These changes in metabolic status can be tracked by metabolomics analysis.

Main body

Metabolomics is a comprehensive approach for identifying metabolic signatures towards the screening, prediction, and earlier diagnosis of hepatocellular carcinoma with greater efficiency than the conventional diagnostic biomarker. The identification of metabolic changes associated with hepatocellular carcinoma is essential to the understanding of disease pathophysiology and enables better monitoring of high-risk individuals. However, due to the complexity of the metabolic pathways associated with hepatocellular carcinoma, the details of these perturbations are still not adequately characterized. The current status of biomarkers for hepatocellular carcinoma and their insufficiencies and metabolic pathways linked to hepatocellular carcinogenesis are briefly addressed in this mini-review. The review focused on the significantly changed metabolites and pathways associated with hepatocellular carcinoma such as phospholipids, bile acids, amino acids, reactive oxygen species metabolism, and the metabolic changes related to energy production in a cancer cell. The review briefly discusses the sensitivity of metabolomics in the prediction and prognosis of hepatocellular carcinoma and the effect of coexisting multiple etiologies of the disease.

Conclusions

Metabolomics profiling is a potentially promising tool for better predicting, diagnosis, and prognosis of hepatocellular carcinoma.

Recent Advances of Microbiome-Associated Metabolomics Profiling in Liver Disease: Principles, Mechanisms, and Applications

Advances in high-throughput screening of metabolic stability in liver and gut microbiota are able to identify and quantify small-molecule metabolites (metabolome) in different cellular microenvironments that are closest to their phenotypes. Metagenomics and metabolomics are largely recognized to be the “-omics” disciplines for clinical therapeutic screening. Here, metabolomics activity screening in liver disease (LD) and gut microbiomes has significantly delivered the integration of metabolomics data (i.e., a set of endogenous metabolites) with metabolic pathways in cellular environments that can be tested for biological functions (i.e., phenotypes). A growing literature in LD and gut microbiomes reports the use of metabolites as therapeutic targets or biomarkers. Although growing evidence connects liver fibrosis, cirrhosis, and hepatocellular carcinoma, the genetic and metabolic factors are still mainly unknown. Herein, we reviewed proof-of-concept mechanisms for metabolomics-based LD and gut microbiotas’ role from several studies (nuclear magnetic resonance, gas/lipid chromatography, spectroscopy coupled with mass spectrometry, and capillary electrophoresis). A deeper understanding of these axes is a prerequisite for optimizing therapeutic strategies to improve liver health.

Pathway-Based Integrative Analysis of Metabolome and Microbiome Data from Hepatocellular Carcinoma and Liver Cirrhosis Patients

Aberrations of the human microbiome are associated with diverse liver diseases, including hepatocellular carcinoma (HCC). Even if we can associate specific microbes with particular diseases, it is difficult to know mechanistically how the microbe contributes to the pathophysiology. Here, we sought to reveal the functional potential of the HCC-associated microbiome with the human metabolome which is known to play a role in connecting host phenotype to microbiome function. To utilize both microbiome and metabolomic data sets, we propose an innovative, pathway-based analysis, Hierarchical structural Component Model for pathway analysis of Microbiome and Metabolome (HisCoM-MnM), for integrating microbiome and metabolomic data. In particular, we used pathway information to integrate these two omics data sets, thus providing insight into biological interactions between different biological layers, with regard to the host's phenotype. The application of HisCoM-MnM to data sets from 103 and 97 patients with HCC and liver cirrhosis (LC), respectively, showed that this approach could identify HCC-related pathways related to cancer metabolic reprogramming, in addition to the significant metabolome and metagenome that make up those pathways.

Plasma metabolomic profiles in liver cancer patients following stereotactic body radiotherapy

Background

Stereotactic body radiotherapy (SBRT) is an effective treatment for hepatocellular carcinoma (HCC). This study sought to identify differentially expressed plasma metabolites in HCC patients at baseline and early during SBRT, and to explore if changes in these metabolites early during SBRT may serve as biomarkers for radiation-induced liver injury and/or tumour response.

Methods

Forty-seven HCC patients were treated with SBRT on previously published prospective trials. Plasma samples were collected at baseline and after one to two fractions of SBRT, and analysed by GC/MS and LC/MS for untargeted and targeted metabolomics profiling, respectively.

Findings

Sixty-nine metabolites at baseline and 62 metabolites after one to two fractions of SBRT were differentially expressed, and strongly separated the Child Pugh (CP) B from the CP A HCC patients. These metabolites are associated with oxidative stress and alterations in hepatic cellular metabolism. Differential upregulation of serine, alanine, taurine, and lipid metabolites early during SBRT from baseline was noted in the HCC patients who demonstrated the greatest increase in CP scores at three months post SBRT, suggesting that high protein and lipid turnover early during SBRT may portend increased clinical liver toxicity. Twenty annotated metabolites including fatty acids, glycerophospholipids, and acylcarnitines were differentially upregulated early during SBRT from baseline and separated patients with complete/partial response from those with stable disease at three months post SBRT.

Interpretation

Dysregulation of amino acid and lipid metabolism detected early during SBRT are associated with subsequent clinical liver injury and tumour response in HCC.

Breath Metabolomics Provides an Accurate and Noninvasive Approach for Screening Cirrhosis, Primary, and Secondary Liver Tumors

Hepatocellular carcinoma (HCC) and secondary liver tumors, such as colorectal cancer liver metastases are significant contributors to the overall burden of cancer‐related morality. Current biomarkers, such as alpha‐fetoprotein (AFP) for HCC, result in too many false negatives, necessitating noninvasive approaches with improved sensitivity. Volatile organic compounds (VOCs) detected in the breath of patients can provide valuable insight into disease processes and can differentiate patients by disease status. Here, we investigate whether 22 VOCs from the breath of 296 patients can distinguish those with no liver disease (n = 54), cirrhosis (n = 30), HCC (n = 112), pulmonary hypertension (n = 49), or colorectal cancer liver metastases (n = 51). This work extends previous studies by evaluating the ability for VOC signatures to differentiate multiple diseases in a large cohort of patients. Pairwise disease comparisons demonstrated that most of the VOCs tested are present in significantly different relative abundances (false discovery rate P < 0.1), indicating broad impacts on the breath metabolome across diseases. A predictive model developed using random forest machine learning and cross validation classified patients with 85% classification accuracy and 75% balanced accuracy. Importantly, the model detected HCC with 73% sensitivity compared with 53% for AFP in the same cohort. An added value of this approach is that influential VOCs in the predictive model may provide insight into disease etiology. Acetaldehyde and acetone, both of which have roles in tumor promotion, were considered important VOCs for differentiating disease groups in the predictive model and were increased in patients with cirrhosis and HCC compared to patients with no liver disease (false discovery rate P < 0.1). Conclusion: The use of machine learning and breath VOCs shows promise as an approach to develop improved, noninvasive screening tools for chronic liver disease and primary and secondary liver tumors.

Sugar Alcohols Have a Key Role in Pathogenesis of Chronic Liver Disease and Hepatocellular Carcinoma in Whole Blood and Liver Tissues

The major risk factors for hepatocellular carcinoma (HCC) are hepatitis C and B viral infections that proceed to Chronic Liver Disease (CLD). Yet, the early diagnosis and treatment of HCC are challenging because the pathogenesis of HCC is not fully defined. To better understand the onset and development of HCC, untargeted GC-TOF MS metabolomics data were acquired from resected human HCC tissues and their paired non-tumor hepatic tissues (n = 46). Blood samples of the same HCC subjects (n = 23) were compared to CLD (n = 15) and healthy control (n = 15) blood samples. The participants were recruited from the National Liver Institute in Egypt. The GC-TOF MS data yielded 194 structurally annotated compounds. The most strikingly significant alteration was found for the class of sugar alcohols that were up-regulated in blood of HCC patients compared to CLD subjects (p < 2.4 × 10⁻¹²) and CLD compared to healthy controls (p = 4.1 × 10⁻⁷). In HCC tissues, sugar alcohols were the most significant (p < 1 × 10⁻⁶) class differentiating resected HCC tissues from non-malignant hepatic tissues for all HCC patients. Alteration of sugar alcohol levels in liver tissues also defined early-stage HCC from their paired non-malignant hepatic tissues (p = 2.7 × 10⁻⁶). In blood, sugar alcohols differentiated HCC from CLD subjects with an ROC-curve of 0.875 compared to 0.685 for the classic HCC biomarker alpha-fetoprotein. Blood sugar alcohol levels steadily increased from healthy controls to CLD to early stages of HCC and finally, to late-stage HCC patients. The increase in sugar alcohol levels indicates a role of aldo-keto reductases in the pathogenesis of HCC, possibly opening novel diagnostic and therapeutic options after in-depth validation.

Role of lipids in pathophysiology, diagnosis and therapy of hepatocellular carcinoma

Hepatocellular carcinoma (HCC) is an aggressive and widespread cancer. Patients with liver cirrhosis of different aetiologies are at a risk to develop HCC. It is important to know that in approximately 20% of cases primary liver tumors arise in a non-cirrhotic liver. Lipid metabolism is variable in patients with chronic liver diseases, and lipid metabolites involved therein do play a role in the development of HCC. Of note, lipid composition of carcinogenic tissues differs from non-affected liver tissues. High cholesterol and low ceramide levels in the tumors protect the cells from oxidative stress and apoptosis, and do also promote cell proliferation. So far, detailed characterization of the mechanisms by which lipids enable the development of HCC has received little attention. Evaluation of the complex roles of lipids in HCC is needed to better understand the pathophysiology of HCC, the later being of paramount importance for the development of urgently needed therapeutic interventions. Disturbed hepatic lipid homeostasis has systemic consequences and lipid species may emerge as promising biomarkers for early diagnosis of HCC. The challenge is to distinguish lipids specifically related to HCC from changes simply related to the underlying liver disease. This review article discusses aberrant lipid metabolism in patients with HCC.

1H-NMR Based Serum Metabolomics Highlights Different Specific Biomarkers between Early and Advanced Hepatocellular Carcinoma Stages

The application of non-targeted serum metabolomics profiling represents a noninvasive tool to identify new clinical biomarkers and to provide early diagnostic differentiation, and insight into the pathological mechanisms underlying hepatocellular carcinoma (HCC) progression. In this study, we used proton Nuclear Magnetic Resonance (¹H-NMR) Spectroscopy and multivariate data analysis to profile the serum metabolome of 64 HCC patients, in early (n = 28) and advanced (n = 36) disease stages. We found that ¹H-NMR metabolomics profiling could discriminate early from advanced HCC patients with a cross-validated accuracy close to 100%. Orthogonal partial least squares discriminant analysis (OPLS-DA) showed significant changes in serum glucose, lactate, lipids and some amino acids, such as alanine, glutamine, 1-methylhistidine, lysine and valine levels between advanced and early HCC patients. Moreover, in early HCC patients, Kaplan-Meier analysis highlighted the serum tyrosine level as a predictor for overall survival (OS). Overall, our analysis identified a set of metabolites with possible clinical and biological implication in HCC pathophysiology.

Prospective serum metabolomic profiling of lethal prostate cancer

Impaired metabolism may play an important role in the pathogenesis of lethal prostate cancer, yet there is a paucity of evidence regarding the association. We conducted a large prospective serum metabolomic analysis of lethal prostate cancer in 523 cases and 523 matched controls nested within the Alpha-Tocopherol, Beta-Carotene Cancer Prevention (ATBC) Study. Median time from baseline fasting serum collection to prostate cancer death was 18 years (maximum 30 years). We identified 860 known biochemicals through an ultrahigh-performance LC-MS/MS platform. Conditional logistic regression models estimated odds ratios (OR) and 95% confidence intervals of risk associated with 1-standard deviation (s.d.) increases in log-metabolite signals. We identified 34 metabolites associated with lethal prostate cancer with a false discovery rate (FDR) < 0.15. Notably, higher serum thioproline, and thioproline combined with two other cysteine-related amino acids and redox metabolites, cystine and cysteine, were associated with reduced risk (1-s.d. OR = 0.75 and 0.71, respectively; p ≤ 8.2 × 10-5 ). By contrast, the dipeptide leucylglycine (OR = 1.36, p = 8.2 × 10-5 ), and three gamma-glutamyl amino acids (OR = 1.28-1.30, p ≤ 4.6 × 10-4 ) were associated with increased risk of lethal prostate cancer. Cases with metastatic disease at diagnosis (n = 179) showed elevated risk for several lipids, including especially the ketone body 3-hydroxybutyrate (BHBA), acyl carnitines, and dicarboxylic fatty acids (1.37 ≤ OR ≤ 1.49, FDR < 0.15). These findings provide a prospective metabolomic profile of lethal prostate cancer characterized by altered biochemicals in the redox, dipeptide, pyrimidine, and gamma-glutamyl amino acid pathways, whereas ketone bodies and fatty acids were associated specifically with metastatic disease.

Ischemia reperfusion injury promotes recurrence of hepatocellular carcinoma in fatty liver via ALOX12-12HETE-GPR31 signaling axis

BACKGROUND

Ischemia reperfusion injury (IRI) has been shown to increase the risk of tumor recurrence after liver surgery. Also, nonalcoholic fatty liver disease (NAFLD) is associated with increased HCC recurrence. ALOX12-12-HETE pathway is activated both in liver IRI and NASH. Also, ALOX12-12-HETE has been shown to mediate tumorigenesis and progression. Therefore, our study aims to investigate whether the ALOX12-12-HETE-GPR31 pathway involved in IRI induced HCC recurrence in NAFLD.

METHODS

HCC mouse model was used to mimic the HCC recurrence in NAFLD. Western Blot, qPCR, Elisa and Immunofluorescence analysis were conducted to evaluate the changes of multiple signaling pathways during HCC recurrence, including ALOX12-12-HETE axis, EMT, MMPs and PI3K/AKT/NF-κB signaling pathway. We also measured the expression and functional changes of GPR31 by siRNA.

RESULTS

ALOX12-12-HETE pathway was activated in liver IRI and its activation was further enhanced in NAFLD, which induced more severe HCC recurrence in fatty livers than normal livers. Inhibition of ALOX12-12-HETE by ML355 reduced the HCC recurrence in fatty livers. In vitro studies showed that 12-HETE increased the expression of GPR31 and induced epithelial-mesenchymal transition (EMT) and matrix metalloprotein (MMPs) by activating PI3K/AKT/NF-κB pathway. Furthermore, knockdown of GPR31 in cancer cells inhibited the HCC recurrence in NAFLD.

CONCLUSIONS

ALOX12-12-HETE-GPR31 played an important role in HCC recurrence and might be a potential therapeutic target to reduce HCC recurrence after surgery in fatty livers.

Metabolic rearrangements in primary liver cancers: cause and consequences

Primary liver cancer (PLC) is the fourth most frequent cause of cancer-related death. The high mortality rates arise from late diagnosis and the limited accuracy of diagnostic and prognostic biomarkers. The liver is a major regulator, orchestrating the clearance of toxins, balancing glucose, lipid and amino acid uptake, managing whole-body metabolism and maintaining metabolic homeostasis. Tumour onset and progression is frequently accompanied by rearrangements of metabolic pathways, leading to dysregulation of metabolism. The limitation of current therapies targeting PLCs, such as hepatocellular carcinoma and cholangiocarcinoma, points towards the importance of deciphering this metabolic complexity. In this Review, we discuss the role of metabolic liver disruptions and the implications of these processes in PLCs, emphasizing their clinical relevance and value in early diagnosis and prognosis and as putative therapeutic targets. We also describe system biology approaches able to reconstruct the metabolic complexity of liver diseases. We also discuss whether metabolic rearrangements are a cause or consequence of PLCs, emphasizing the opportunity to clinically exploit the rewired metabolism. In line with this idea, we discuss circulating metabolites as promising biomarkers for PLCs.

Quantification of serum purine metabolites for distinguishing patients with hepatitis B from hepatocellular carcinoma

Aim: In order to differential diagnosis of chronic hepatitis B (HBV-I) and hepatocellular carcinoma (HCC), a UPLC-MS/MS method for measuring purine metabolites was developed. Methodology & results: serum samples from 26 HBV-I and 35 HCC patients were collected. Ten purine metabolites were simultaneously quantified by UPLC-MS/MS with tubercidin and uric acid-1,3-¹⁵N₂ as internal standards. The method was validated to meet the requirements of clinical sample analysis. A logistic equation was established for differential diagnosis of HBV-I and HCC by combination of xanthosine and guanine with the area under the receiver operating characteristic curve of 0.885. Conclusion: Guanine and xanthosine are intermediates in the metabolism of purine, which play an important role in gene synthesis, and metabolism regulation. The alteration of serum purine metabolite may contribute to differential diagnosis of HBV-I and HCC.

Liver Cirrhosis Patients Who Had Normal Liver Function Before Liver Cirrhosis Development Have the Altered Metabolic Profiles Before the Disease Occurrence Compared to Healthy Controls

Liver cirrhosis (LC) is the final usual outcome of liver damage induced by various chronic liver diseases. Because of asymptomatic nature of LC, it is usually diagnosed at late and advanced stages, and patients are easy to miss the best timing for treatment. Thus, the early detection of LC is needed. In the prospective Korean Cancer Prevention Study-II (K-II), we aimed to identify valuable biomarkers for LC using metabolomics to distinguish subjects with incident LC (LC group) from subjects free from LC (control group) during a mean 7-year follow-up period. Metabolic alterations were investigated using baseline serum specimens acquired from 94 subjects with incident LC and 180 age- and sex-matched LC-free subjects via ultra-performance liquid chromatography (UPLC)-linear-trap quardrupole (LTQ)-Orbitrap mass spectrometry (MS). As a result of the metabolic analysis, 46 metabolites were identified. Among them, 11 and 18 metabolite level showed a significant increase and decrease, respectively, in the LC group compared to the control group. Nine metabolic pathways, including glyoxylate and dicarboxylate metabolism, amino acid metabolism, fatty acid metabolism, linoleic acid metabolism, α-linolenic acid metabolism, and arachidonic acid metabolism, were significantly different between the two groups. Logistic regression demonstrated that the LC emergence was independently affected by serum levels of myristic acid, palmitic acid, linoleic acid, eicosapentaenoic acid (EPA), lysophosphatidic acid (LPA) (18:1), glycolic acid, lysophosphatidylcholine (lysoPC) (22:6), and succinylacetone (R 2 = 0.837, P < 0.001). This prospective study revealed that dysregulation of various metabolism had the clinical relevance on the LC development. Moreover, myristic acid, palmitic acid, linoleic acid, EPA, LPA (18:1), glycolic acid, lysoPC (22:6), and succinylacetone were emerged as independent variables influencing the incidence of LC. The results support that the early biomarkers found in this study may useful for predicting and remedying the risk of LC.

Metabolic Signature of Hepatic Fibrosis: From Individual Pathways to Systems Biology

Hepatic fibrosis is a major cause of morbidity and mortality worldwide, as it ultimately leads to cirrhosis, which is estimated to affect up to 2% of the global population. Hepatic fibrosis is confirmed by liver biopsy, and the erroneous nature of this technique necessitates the search for noninvasive alternatives. However, current biomarker algorithms for hepatic fibrosis have many limitations. Given that the liver is the largest organ and a major metabolic hub in the body, probing the metabolic signature of hepatic fibrosis holds promise for the discovery of new markers and therapeutic targets. Regarding individual metabolic pathways, accumulating evidence shows that hepatic fibrosis leads to alterations in carbohydrate metabolism, as aerobic glycolysis is aggravated in activated hepatic stellate cells (HSCs) and the whole fibrotic liver; in amino acid metabolism, as Fischer’s ratio (branched-chain amino acids/aromatic amino acids) decreases in patients with hepatic fibrosis; and in lipid metabolism, as HSCs lose vitamin A-containing lipid droplets during transdifferentiation, and cirrhotic patients have decreased serum lipids. The current review also summarizes recent findings of metabolic alterations relevant to hepatic fibrosis based on systems biology approaches, including transcriptomics, proteomics, and metabolomics in vitro, in animal models and in humans.

Profiling of plasma metabolomics in patients with hepatitis C-related liver cirrhosis and hepatocellular carcinoma

Aim of the study

The diagnosis of hepatocellular carcinoma (HCC) is usually late, due to the lack of early detection of biomarkers for HCC. Metabolomics analysis has emerged as a useful tool for studying human diseases. The objective of the study was to investigate the differences in plasma metabolites between hepatitis C virus (HCV)-induced cirrhosis and HCC.

Material and methods

22 subjects with HCV-related liver cirrhosis and 22 subjects with HCC were enrolled. Clinical, routine laboratory and imaging studies were done. Gas chromatography/mass spectrometry (GC/MS) was used for metabolomics analysis of patients' plasma samples.

Results

34 known metabolites were detected, of which five metabolites were identified to have the strongest discriminatory power for separation between HCC and cirrhosis groups: octanoic acid (caprylic acid), decanoic (capric acid), oleic acid, oxalic acid and glycine. These are 3 fatty acids (FA), a dicarboxylic acid and a glucogenic amino acid, respectively. No significant correlation was found between the relative intensities of the five metabolites and any of the patient or tumor characteristics (Child-Turcotte-Pugh (CTP) score, Barcelona Clinic Liver Cancer (BCLC) stage, number of focal lesions and size of largest focal lesion). ROC curve analysis was performed and area under the curve (AUC) was calculated, revealing that oleic acid, octanoic (caprylic) acid and glycine had higher positive predictive value than α-fetoprotein.

Conclusions

The study of metabolomics (particularly involving FA) may help define distinct metabolic patterns to distinguish HCV-induced liver cirrhosis from HCC patients. Future research in this field is still needed, particularly concerning HCC treatment strategies which target fatty acid-related metabolic pathways.

Comprehensive Metabolomic Search for Biomarkers to Differentiate Early Stage Hepatocellular Carcinoma from Cirrhosis

The established biomarker for hepatocellular carcinoma (HCC), serum α-fetoprotein (AFP), has suboptimal performance in early disease stages. This study aimed to develop a metabolite panel to differentiate early-stage HCC from cirrhosis. Cross-sectional metabolomic analyses of serum samples were performed for 53 and 47 patients with early HCC and cirrhosis, respectively, and 50 matched healthy controls. Results were validated in 82 and 80 patients with early HCC and cirrhosis, respectively. To retain a broad spectrum of metabolites, technically distinct analyses (global metabolomic profiling using gas chromatography time-of-flight mass spectrometry and targeted analyses using liquid chromatography with tandem mass spectrometry) were employed. Multivariate analyses classified distinct metabolites; logistic regression was employed to construct a prediction model for HCC diagnosis. Five metabolites (methionine, proline, ornithine, pimelylcarnitine, and octanoylcarnitine) were selected in a panel. The panel distinguished HCC from cirrhosis and normal controls, with an area under the receiver operating curve (AUC) of 0.82; this was significantly better than that of AFP (AUC: 0.75). During validation, the panel demonstrated significantly better predictability (AUC: 0.94) than did AFP (AUC: 0.78). Defects in ammonia recycling, the urea cycle, and amino acid metabolism, demonstrated on enrichment pathway analysis, may reliably distinguish HCC from cirrhosis. Compared with AFP alone, the metabolite panel substantially improved early-stage HCC detection.