Metabolic perturbations prior to hepatocellular carcinoma diagnosis: Findings from a prospective observational cohort study

Optimal transport for automatic alignment of untargeted metabolomic data

Untargeted metabolomic profiling through liquid chromatography-mass spectrometry (LC-MS) measures a vast array of metabolites within biospecimens, advancing drug development, disease diagnosis, and risk prediction. However, the low throughput of LC-MS poses a major challenge for biomarker discovery, annotation, and experimental comparison, necessitating the merging of multiple datasets. Current data pooling methods encounter practical limitations due to their vulnerability to data variations and hyperparameter dependence. Here, we introduce GromovMatcher, a flexible and user-friendly algorithm that automatically combines LC-MS datasets using optimal transport. By capitalizing on feature intensity correlation structures, GromovMatcher delivers superior alignment accuracy and robustness compared to existing approaches. This algorithm scales to thousands of features requiring minimal hyperparameter tuning. Manually curated datasets for validating alignment algorithms are limited in the field of untargeted metabolomics, and hence we develop a dataset split procedure to generate pairs of validation datasets to test the alignments produced by GromovMatcher and other methods. Applying our method to experimental patient studies of liver and pancreatic cancer, we discover shared metabolic features related to patient alcohol intake, demonstrating how GromovMatcher facilitates the search for biomarkers associated with lifestyle risk factors linked to several cancer types.

Documento de consenso sobre tabaquismo y riesgo vascular

Consensus statement on smoking and vascular risk About 22% of the Spanish population are daily smokers. Men are more likely to smoke than women. In Spain, women between 15-25 years of age smoke as much or more than men. Every smoker should be assessed for: physical dependence on nicotine (Fagerström test), social and psychological dependence (Glover Nilsson test), level of motivation to quit (Richmond test), probability of therapy success (Henri-Mondor and Michael-Fiore tests), and stage of behavioral change development (Prochaska and DiClementi). Advice on smoking cessation is highly cost-effective and should always be provided. Smoking is an enhancer of cardiovascular risk because it acts as a pathogen agent in the development of arteriosclerosis and is associated with ischemic heart disease, stroke, and peripheral artery disease. Smoking increases the risk of chronic lung diseases (COPD) and is related to cancers of the lung, female genitalia, larynx, oropharynx, bladder, mouth, esophagus, liver and biliary tract, and stomach, among others. Combined oral contraceptives should be avoided in women smokers older than 35 years of age due to the risk of thromboembolism. In smoking cessation, the involvement of physicians, nurses, psychologists, etc. is important, and their multidisciplinary collaboration is needed. Effective pharmacological treatments for smoking cessation are available. Combined treatments are recommended when smoker's dependence is high. For individuals who are unable to quit smoking, a strategy based on tobacco damage management with a total switch to smokeless products could be a less dangerous alternative for their health than continuing to smoke.

Metabolomic biomarkers in liquid biopsy: accurate cancer diagnosis and prognosis monitoring

Liquid biopsy, a novel detection method, has recently become an active research area in clinical cancer owing to its unique advantages. Studies on circulating free DNA, circulating tumor cells, and exosomes obtained by liquid biopsy have shown great advances and they have entered clinical practice as new cancer biomarkers. The metabolism of the body is dynamic as cancer originates and progresses. Metabolic abnormalities caused by cancer can be detected in the blood, sputum, urine, and other biological fluids via systemic or local circulation. A considerable number of recent studies have focused on the roles of metabolic molecules in cancer. The purpose of this review is to provide an overview of metabolic markers from various biological fluids in the latest clinical studies, which may contribute to cancer screening and diagnosis, differentiation of cancer typing, grading and staging, and prediction of therapeutic response and prognosis.

Prediagnostic plasma metabolite concentrations and liver cancer risk: a population-based study of Chinese men

BACKGROUND

Previous metabolic profiling of liver cancer has mostly used untargeted metabolomic approaches and was unable to quantitate the absolute concentrations of metabolites. In this study, we examined the association between the concentrations of 186 targeted metabolites and liver cancer risk using prediagnostic plasma samples collected up to 14 years prior to the clinical diagnosis of liver cancer.

METHODS

We conducted a nested case-control study (n = 322 liver cancer cases, n = 322 matched controls) within the Shanghai Men's Health Study. Conditional logistic regression models adjusted for demographics, lifestyle factors, dietary habits, and related medical histories were used to estimate the odds ratios. Restricted cubic spline functions were used to characterise the dose-response relationships between metabolite concentrations and liver cancer risk.

FINDINGS

After adjusting for potential confounders and correcting for multiple testing, 28 metabolites were associated with liver cancer risk. Significant non-linear relationships were observed for 22 metabolites. The primary bile acid biosynthesis and phenylalanine, tyrosine and tryptophan biosynthesis were found to be important pathways involved in the aetiology of liver cancer. A metabolic score consisting of 10 metabolites significantly improved the predictive ability of traditional epidemiological risk factors for liver cancer, with an optimism-corrected AUC increased from 0.84 (95% CI: 0.81-0.87) to 0.89 (95% CI: 0.86-0.91).

INTERPRETATION

This study characterised the dose-response relationships between metabolites and liver cancer risk, providing insights into the complex metabolic perturbations prior to the clinical diagnosis of liver cancer. The metabolic score may serve as a candidate risk predictor for liver cancer.

FUNDING

National Key Project of Research and Development Program of China [2021YFC2500404, 2021YFC2500405]; US National Institutes of Health [subcontract of UM1 CA173640].

Identification of pre-diagnostic lipid sets associated with liver cancer risk using untargeted lipidomics and chemical set analysis: A nested case-control study within the ATBC cohort

In pre-disposed individuals, a reprogramming of the hepatic lipid metabolism may support liver cancer initiation. We conducted a high-resolution mass spectrometry based untargeted lipidomics analysis of pre-diagnostic serum samples from a nested case-control study (219 liver cancer cases and 219 controls) within the Alpha-Tocopherol, Beta-Carotene Cancer Prevention (ATBC) Study. Out of 462 annotated lipids, 158 (34.2%) were associated with liver cancer risk in a conditional logistic regression analysis at a false discovery rate (FDR) <0.05. A chemical set enrichment analysis (ChemRICH) and co-regulatory set analysis suggested that 22/28 lipid classes and 47/83 correlation modules were significantly associated with liver cancer risk (FDR <0.05). Strong positive associations were observed for monounsaturated fatty acids (MUFA), triacylglycerols (TAGs) and phosphatidylcholines (PCs) having MUFA acyl chains. Negative associations were observed for sphingolipids (ceramides and sphingomyelins), lysophosphatidylcholines, cholesterol esters and polyunsaturated fatty acids (PUFA) containing TAGs and PCs. Stearoyl-CoA desaturase enzyme 1 (SCD1), a rate limiting enzyme in fatty acid metabolism and ceramidases seems to be critical in this reprogramming. In conclusion, our study reports pre-diagnostic lipid changes that provide novel insights into hepatic lipid metabolism reprogramming may contribute to a pro-cell growth and anti-apoptotic tissue environment and, in turn, support liver cancer initiation.

Reversal of cholestatic liver disease by the inhibition of sphingosine 1-phosphate receptor 2 signaling

Aims

The objective of this study is to examine the impact of inhibiting Sphingosine 1-phosphate receptor 2 (S1PR2) on liver inflammation, fibrogenesis, and changes of gut microbiome in the context of cholestasis-induced conditions.

Methods

The cholestatic liver injury model was developed by common bile duct ligation (CBDL). Sprague-Dawley rats were randomly allocated to three groups, sham operation, CBDL group and JTE-013 treated CBDL group. Biochemical and histological assessments were conducted to investigate the influence of S1PR2 on the modulation of fibrogenic factors and inflammatory infiltration. We conducted an analysis of the fecal microbiome by using 16S rRNA sequencing. Serum bile acid composition was evaluated through the utilization of liquid chromatography-mass spectrometry techniques.

Results

In the BDL rat model, the study findings revealed a significant increase in serum levels of conjugated bile acids, accompanied by an overexpression of S1PR2. Treatment with the specific inhibitor of S1PR2, known as JTE-013, resulted in a range of specific effects on the BDL rats. These effects included the improvement of liver function, reduction of liver inflammation, inhibition of hepatocyte apoptosis, and suppression of NETosis. These effects are likely mediated through the TCA/S1PR2/NOX2/NLRP3 pathway. Furthermore, the administration of JTE-013 resulted in an augmentation of the diversity of the bacterial community's diversity, facilitating the proliferation of advantageous species while concurrently inhibiting the prevalence of detrimental bacteria.

Conclusions

The results of our study suggest that the administration of JTE-013 may have a beneficial effect in alleviating cholestatic liver disease and restoring the balance of intestinal flora.

Chemical components and protective effects of Atractylodes japonica Koidz. ex Kitam against acetic acid-induced gastric ulcer in rats

BACKGROUND

Atractylodes japonica Koidz. ex Kitam. (A. japonica, Chinese name: Guan-Cangzhu, Japanese name: Byaku-jutsu), a perennial herb, which is mainly distributed in northeast area of China, it’s often used to treat digestive system diseases such as gastric ulcer (GU). However, the mechanism of its potential protective effects against GU remains unclear.

AIM

To investigate the protective effects of A. japonica on acetic acid-induced GU rats.

METHODS

The chemical constituents of A. japonica were determined by ultra performance liquid chromatography tandem mass spectrometry (UPLC-MS/MS) analysis. The rat model of GU was simulated by acetic acid method. The pathological changes of gastric tissues were evaluated by hematoxylin-eosin stain, the levels of epidermal growth factor (EGF), EGF receptor (EGFR), nuclear factor kappa-B (NF-κB), interleukin-1β (IL-1β), IL-10, Na⁺-K⁺-ATPase (NKA) in serum and gastric tissues were determined by enzyme-linked immunosorbent assay, and the mRNA expressions of EGFR, NF-κBp65, IkappaBalpha (IκBα) and Zonula Occludens-1 (ZO-1) in gastric tissues were determined by real-time reverse transcription polymerase chain reaction, and the efficacy was observed. Then, plasma metabolomic analysis was performed by UPLC-MS/MS to screen the specific potential biomarkers, metabolic pathways and to explore the possible mechanisms.

RESULTS

48 chemical constituents were identified. Many of them have strong pharmacological activity, the results also revealed that A. japonica significantly improved the pathological damage of gastric tissues, increased the expression levels of IL-10, IκBα related to anti-inflammatory factors, decreased the expression levels of IL-1β, NF-κB, NF-κBp65, related to proinflammatory factors, restored the levels of factors about EGF, EGFR, ZO-1 associated with ulcer healing and the levels of factors about NKA associated with energy metabolism. Metabolomic analysis identified 10 potential differential metabolites and enriched 7 related metabolic pathways.

CONCLUSION

These findings contribute to the understanding of the potential mechanism of A. japonica to improve acetic acid-induced GU, and will be of great importance for the development and clinical application of natural drugs related to A. japonica.

Molecular and Metabolic Phenotyping of Hepatocellular Carcinoma for Biomarker Discovery: A Meta-Analysis

Identifying and translating hepatocellular carcinoma (HCC) biomarkers from bench to bedside using mass spectrometry-based metabolomics and lipidomics is hampered by inconsistent findings. Here, we investigated HCC at systemic and metabolism-centric multiomics levels by conducting a meta-analysis of quantitative evidence from 68 cohorts. Blood transcript biomarkers linked to the HCC metabolic phenotype were externally validated and prioritized. In the studies under investigation, about 600 metabolites were reported as putative HCC-associated biomarkers; 39, 20, and 10 metabolites and 52, 12, and 12 lipids were reported in three or more studies in HCC vs. Control, HCC vs. liver cirrhosis (LC), and LC vs. Control groups, respectively. Amino acids, fatty acids (increased 18:1), bile acids, and lysophosphatidylcholine were the most frequently reported biomarkers in HCC. BAX and RAC1 showed a good correlation and were associated with poor prognosis. Our study proposes robust HCC biomarkers across diverse cohorts using a data-driven knowledge-based approach that is versatile and affordable for studying other diseases.

Metabolomic epidemiology offers insights into disease aetiology

Metabolomic epidemiology is the high-throughput study of the relationship between metabolites and health-related traits. This emerging and rapidly growing field has improved our understanding of disease aetiology and contributed to advances in precision medicine. As the field continues to develop, metabolomic epidemiology could lead to the discovery of diagnostic biomarkers predictive of disease risk, aiding in earlier disease detection and better prognosis. In this Review, we discuss key advances facilitated by the field of metabolomic epidemiology for a range of conditions, including cardiometabolic diseases, cancer, Alzheimer's disease and COVID-19, with a focus on potential clinical utility. Core principles in metabolomic epidemiology, including study design, causal inference methods and multi-omic integration, are briefly discussed. Future directions required for clinical translation of metabolomic epidemiology findings are summarized, emphasizing public health implications. Further work is needed to establish which metabolites reproducibly improve clinical risk prediction in diverse populations and are causally related to disease progression.

UPLC-Q-TOF-MS/MS-based urine metabolomics studies on the toxicity and detoxication of Tripterygium wilfordii Hook. f. after roasting

Tripterygium wilfordii (TW), a well-known traditional Chinese medicine, was widely used in the treatment of autoimmune disorders and inflammatory diseases. However, the clinical use of TW was limited by severe toxicities, such as hepatotoxicity and nephrotoxicity. Our previous studies indicated that roasting was an effective approach for reducing TW-induced toxicity. After roasting, celastrol was completely decomposed, partially converted into 1-hydroxy-2,5,8-trimethyl-9-fluorenone and the total alkaloids content were significantly reduced. However, the detoxication mechanisms of roasting on TW were poorly unknown. This study aimed to explore the toxicity and detoxification mechanisms of TW after roasting based on urine metabolomics. Promising biomarkers were evaluated by multiple comparison analyses. Sixteen toxicity biomarkers were identified between control group and total extract group. Twelve toxicity biomarkers were identified between control group and total alkaloids group. Eight toxicity biomarkers were identified between control group and celastrol group. These metabolites were mainly involved in seven metabolic pathways, summarized as pentose and glucuronate interconversions, lipid metabolism (sphingolipid metabolism, glycerophospholipid metabolisms, fatty acid biosynthesis and steroid hormone biosynthesis) and amino acid metabolism (taurine and hypotaurine metabolism, tryptophan metabolism). After roasting, the toxicities of total extract, total alkaloids and celastrol were relieved by ameliorative serum parameters and pathological changes in hepatic and renal tissues which revealed that the reduction of celastrol and total alkaloids played important roles in the detoxification of roasting on TW. Furthermore, roasting regulated the levels of fourteen potential biomarkers in the total extract group, ten potential biomarkers in the total alkaloids group and seven candidate biomarkers in the celastrol group to normal levels. Biological pathway analysis revealed that roasting may ameliorate TW-induced metabolic disorders in pentose and glucuronate interconversions, lipid metabolism and amino acid metabolism. This study provided evidence for the application of roasting in TW.

Characteristics of Cancer Epidemiology Studies That Employ Metabolomics: A Scoping Review

An increasing number of cancer epidemiology studies use metabolomics assays. This scoping review characterizes trends in the literature in terms of study design, population characteristics, and metabolomics approaches and identifies opportunities for future growth and improvement. We searched PubMed/MEDLINE, Embase, Scopus, and Web of Science: Core Collection databases and included research articles that used metabolomics to primarily study cancer, contained a minimum of 100 cases in each main analysis stratum, used an epidemiologic study design, and were published in English from 1998 to June 2021. A total of 2,048 articles were screened, of which 314 full texts were further assessed resulting in 77 included articles. The most well-studied cancers were colorectal (19.5%), prostate (19.5%), and breast (19.5%). Most studies used a nested case-control design to estimate associations between individual metabolites and cancer risk and a liquid chromatography-tandem mass spectrometry untargeted or semi-targeted approach to measure metabolites in blood. Studies were geographically diverse, including countries in Asia, Europe, and North America; 27.3% of studies reported on participant race, the majority reporting White participants. Most studies (70.2%) included fewer than 300 cancer cases in their main analysis. This scoping review identified key areas for improvement, including needs for standardized race and ethnicity reporting, more diverse study populations, and larger studies.

Assessments of TP53 and CTNNB1 gene hotspot mutations in circulating tumour DNA of hepatitis B virus-induced hepatocellular carcinoma

Background: Hepatitis B virus (HBV) infection is one of the major causes of chronic liver disease, which progresses from chronic hepatitis B (CHB) to fibrosis, cirrhosis, and hepatocellular carcinoma (HCC). Early detection and laboratory-based screening of hepatocellular carcinoma are still major challenges. This study was undertaken to determine whether the cancer hallmark gene signatures that are released into circulation as circulating tumour DNA (ctDNA) can be used as a liquid biopsy marker for screening, early detection, and prognosis of HCC. Methods: A total of 130 subjects, including HBV-HCC (n = 80), HBV-cirrhotic and non-cirrhotic (n = 35), and healthy (n = 15) controls, were evaluated for TP53 and beta-catenin (CTNNB1) gene hotspot mutations in ctDNA by Sanger-based cycle sequencing and droplet digital PCR (ddPCR) assays. Mutation detection frequency, percentage mutant fractions, and their association with tumour stage, mortality, and smoking habits were determined. Results: Sanger-based cycle sequencing was carried out for 32 HCC patients. Predict SNP Tools analysis indicated several pathogenic driver mutations in the ctDNA sequence, which include p.D228N, p.C229R, p.H233R, p.Y234D, p.S240T, p.G245S, and p.R249M for TP53 gene exon 7 and p.S33T for CTNNB1 gene exon 3. The TP53 c.746G>T (p.R249M) mutation was detected predominately (25% cases) by sequencing, but there was no dominant mutation at position c.747G>T (p.R249S) that was reported for HBV-HCC patients. A dual-probe ddPCR assay was developed to determine mutant and wild-type copy numbers of TP53 (p.R249M and p.R249S) and CTNNB1 (p.S45P) and their percentage mutant fraction in all 130 subjects. The TP53 R249M and CTNNB1 S45P mutations were detected in 31.25% and 26.25% of HCC patients, respectively, with a high mutant-to-wild-type fraction percentage (1.81% and 1.73%), which is significant as compared to cirrhotic and non-cirrhotic patients. Poor survival was observed in HCC patients with combined TP53 and CTNNB1 gene driver mutations. The TP53 R249M mutation was also significantly (p < 0.0001) associated with smoking habits (OR, 11.77; 95% CI, 3.219-36.20), but not the same for the TP53 R249S mutation. Conclusion: Screening of ctDNA TP53 and CTNNB1 gene mutations by ddPCR may be helpful for early detection and identifying the risk of HCC progression.

Endogenous sex steroid hormones and risk of liver cancer among US men: Results from the Liver Cancer Pooling Project

Background & Aims

Incidence rates of liver cancer in most populations are two to three times higher among men than women. The higher rates among men have led to the suggestion that androgens are related to increased risk whereas oestrogens are related to decreased risk. This hypothesis was investigated in the present study via a nested case-control analysis of pre-diagnostic sex steroid hormone levels among men in five US cohorts.

Methods

Concentrations of sex steroid hormones and sex hormone-binding globulin were quantitated using gas chromatography-mass spectrometry and a competitive electrochemiluminescence immunoassay, respectively. Multivariable conditional logistic regression was used to calculate odds ratios (ORs) and 95% CIs for associations between hormones and liver cancer among 275 men who subsequently developed liver cancer and 768 comparison men.

Results

Higher concentrations of total testosterone (OR per one-unit increase in log₂ = 1.77, 95% CI = 1.38-2.29), dihydrotestosterone (OR = 1.76, 95% CI = 1.21-2.57), oestrone (OR = 1.74, 95% CI = 1.08-2.79), total oestradiol (OR = 1.58, 95% CI=1.22-20.05), and sex hormone-binding globulin (OR = 1.63, 95% CI = 1.27-2.11) were associated with increased risk. Higher concentrations of dehydroepiandrosterone (DHEA), however, were associated with a 53% decreased risk (OR = 0.47, 95% CI = 0.33-0.68).

Conclusions

Higher concentrations of both androgens (testosterone, dihydrotestosterone) and their aromatised oestrogenic metabolites (oestrone, oestradiol) were observed among men who subsequently developed liver cancer compared with men who did not. As DHEA is an adrenal precursor of both androgens and oestrogens, these results may suggest that a lower capacity to convert DHEA to androgens, and their subsequent conversion to oestrogens, confers a lower risk of liver cancer, whereas a greater capacity to convert DHEA confers a greater risk.

Impact and implications

This study does not fully support the current hormone hypothesis as both androgen and oestrogen levels were associated with increased risk of liver cancer among men. The study also found that higher DHEA levels were associated with lower risk, thus suggesting the hypothesis that greater capacity to convert DHEA could be associated with increased liver cancer risk among men.

Risk stratification and early detection biomarkers for precision HCC screening

Hepatocellular carcinoma (HCC) mortality remains high primarily due to late diagnosis as a consequence of failed early detection. Professional societies recommend semi-annual HCC screening in at-risk patients with chronic liver disease to increase the likelihood of curative treatment receipt and improve survival. However, recent dynamic shift of HCC etiologies from viral to metabolic liver diseases has significantly increased the potential target population for the screening, whereas annual incidence rate has become substantially lower. Thus, with the contemporary HCC etiologies, the traditional screening approach might not be practical and cost-effective. HCC screening consists of (i) definition of rational at-risk population, and subsequent (ii) repeated application of early detection tests to the population at regular intervals. The suboptimal performance of the currently available HCC screening tests highlights an urgent need for new modalities and strategies to improve early HCC detection. In this review, we overview recent developments of clinical, molecular, and imaging-based tools to address the current challenge, and discuss conceptual framework and approaches of their clinical translation and implementation. These encouraging progresses are expected to transform the current "one-size-fits-all" HCC screening into individualized precision approaches to early HCC detection and ultimately improve the poor HCC prognosis in the foreseeable future.

Serum metabolomic analyses reveal the potential metabolic biomarkers for prediction of amatoxin poisoning

Amatoxin poisoning leads to over 90% of deaths in mushroom poisoning. The objective of present study was to identify the potential metabolic biomarkers for early diagnosis of amatoxin poisoning. Serum samples were collected from 61 patients with amatoxin poisoning and 61 healthy controls. An untargeted metabolomics analysis was performed using the ultra-high-performance liquid chromatography-quadrupole time-of-flight tandem mass spectrometry (UPLC-QTOF-MS/MS). Multivariate statistical analysis revealed that the patients with amatoxin poisoning could be clearly separated from healthy controls on the basis of their metabolic fingerprints. There were 33 differential metabolites including 15 metabolites up-regulated metabolites and 18 down-regulated metabolites in patients with amatoxin poisoning compared to healthy controls. These metabolites mainly enriched in the lipid metabolism and amino acid metabolism pathways, such as Glycerophospholipid metabolism, Sphingolipid metabolism, Phenylalanine tyrosine and typtophan biosynthesis, Tyrosine metabolism, Arginine and proline metabolism, which may serve important roles in the amatoxin poisoning. Among the differential metabolites, a total of 8 significant metabolic markers were identified for discriminating patients with amatoxin poisoning from healthy controls, including Glycochenodeoxycholate-3-sulfate (GCDCA-S), 11-Oxo-androsterone glucuronide, Neomenthol-glucuronide, Dehydroisoandrosterone 3-glucuronide, Glucose 6-phosphate (G6P), Lanthionine ketimine, Glycerophosphocholine (GPC) and Nicotinamide ribotide, which achieved satisfactory diagnostic accuracy (AUC>0.8) in both discovery and validation cohorts. Strikingly, the Pearson's correlation analysis indicated that 11-Oxo-androsterone glucuronide, G6P and GCDCA-S were positively correlated with the liver injury induced by amatoxin poisoning. The findings of the current study may provide insight into the pathological mechanism of amatoxin poisoning and screened out the reliable metabolic biomarkers to contribute the clinical early diagnosis of amatoxin poisoning.

The role of the gut microbiome in the development of hepatobiliary cancers

Hepatobiliary cancers, including hepatocellular carcinoma and cancers of the biliary tract, share high mortality and rising incidence rates. They may also share several risk factors related to unhealthy western-type dietary and lifestyle patterns as well as increasing body weights and rates of obesity. Recent data also suggest a role for the gut microbiome in the development of hepatobiliary cancer and other liver pathologies. The gut microbiome and the liver interact bidirectionally through the "gut-liver axis," which describes the interactive relationship between the gut, its microbiota, and the liver. Here, we review the gut-liver interactions within the context of hepatobiliary carcinogenesis by outlining the experimental and observational evidence for the roles of gut microbiome dysbiosis, reduced gut barrier function, and exposure to inflammatory compounds as well as metabolic dysfunction as contributors to hepatobiliary cancer development. We also outline the latest findings regarding the impact of dietary and lifestyle factors on liver pathologies as mediated by the gut microbiome. Finally, we highlight some emerging gut microbiome editing techniques currently being investigated in the context of hepatobiliary diseases. Although much work remains to be done in determining the relationships between the gut microbiome and hepatobiliary cancers, emerging mechanistic insights are informing treatments, such as potential microbiota manipulation strategies and guiding public health advice on dietary/lifestyle patterns for the prevention of these lethal tumors.

Identification of a novel plasma metabolite panel as diagnostic biomarker for hepatocellular carcinoma

BACKGROUND AND AIMS

Metabolic reprogramming is one of the hallmarks of cancer. Hepatocellular carcinoma (HCC) is one of the most lethal malignancy camcer, but the early diagnosis of HCC remains difficult. In this study, we searched for potential plasma metabolite biomarkers of HCC.

METHODS

A total of plasma samples of 104 HCC, 76 cirrhosis and 10 healthy subjects were assessed and validated through Gas chromatography-Mass spectrometry. Receiver-operating characteristic curves (ROC) combined with multivariate statistical analyses were used to assess the diagnostic performance of metabolites and combinations.

RESULTS

10 metabolites in screening cohort were significantly changed in the plasma of HCC patients. Multivariate logistic regression analysis of candidate metabolites in validation cohort showed that N-formylglycine, oxoglutaric acid, citrulline and heptaethylene glycol could distinguish HCC from cirrhosis. The combination of these four metabolites showed a better performance than AFP with the Area Under the Curve (AUC), sensitivity, specificity as 0.940, 84.00%, 97.56%, respectively. In further, the panel of N-formylglycine, heptaethylene glycol and citrulline can more effectively discriminate early stage HCC from cirrhosis than AFP (AUC: 0.835 vs. 0.634). Finally, heptaethylene glycol could significantly inhibit the proliferation, migration and invasion of HCC cells in vitro.

CONCLUSION

The combination of plasma N-formylglycine, oxoglutaric acid, citrulline, and heptaethylene glycol can be an efficient novel diagnostic biomarker for HCC.

Metabolic Pathway Analysis: Advantages and Pitfalls for the Functional Interpretation of Metabolomics and Lipidomics Data

Over the past decades, pathway analysis has become one of the most commonly used approaches for the functional interpretation of metabolomics data. Although the approach is widely used, it is not well standardized and the impact of different methodologies on the functional outcome is not well understood. Using four publicly available datasets, we investigated two main aspects of topological pathway analysis, namely the consideration of non-human native enzymatic reactions (e.g., from microbiota) and the interconnectivity of individual pathways. The exclusion of non-human native reactions led to detached and poorly represented reaction networks and to loss of information. The consideration of connectivity between pathways led to better emphasis of certain central metabolites in the network; however, it occasionally overemphasized the hub compounds. We proposed and examined a penalization scheme to diminish the effect of such compounds in the pathway evaluation. In order to compare and assess the results between different methodologies, we also performed over-representation analysis of the same datasets. We believe that our findings will raise awareness on both the capabilities and shortcomings of the currently used pathway analysis practices in metabolomics. Additionally, it will provide insights on various methodologies and strategies that should be considered for the analysis and interpretation of metabolomics data.

Circulating Bile Acids as Biomarkers for Disease Diagnosis and Prevention

CONTEXT

Bile acids (BAs) are pivotal signaling molecules that regulate energy metabolism and inflammation. Recent epidemiological studies have reported specific alterations in circulating BA profiles in certain disease states, including obesity, type 2 diabetes mellitus (T2DM), nonalcoholic fatty liver disease (NAFLD), and Alzheimer disease (AD). In the past decade, breakthroughs have been made regarding the translation of BA profiling into clinical use for disease prediction. In this review, we summarize and synthesize recent data on variation in circulating BA profiles in patients with various diseases to evaluate the value of these biomarkers in human plasma for early diagnosis.

EVIDENCE ACQUISITION

This review is based on a collection of primary and review literature gathered from a PubMed search for BAs, obesity, T2DM, insulin resistance (IR), NAFLD, hepatocellular carcinoma (HCC), cholangiocarcinoma (CCA), colon cancer, and AD, among other keywords.

EVIDENCE SYNTHESIS

Individuals with obesity, T2DM, HCC, CCA, or AD showed specific alterations in circulating BA profiles. These alterations may have existed long before the initial diagnosis of these diseases. The intricate relationship between obesity, IR, and NAFLD complicates the establishment of clear and independent associations between BA profiles and nonalcoholic steatohepatitis. Alterations in the levels of total BAs and several BA species were seen across the entire spectrum of NAFLD, demonstrating significant increases with the worsening of histological features.

CONCLUSIONS

Aberrant circulating BA profiles are an early event in the onset and progression of obesity, T2DM, HCC, and AD. The pleiotropic effects of BAs explain these broad connections. Circulating BA profiles could provide a basis for the development of biomarkers for the diagnosis and prevention of a wide range of diseases.

Circulating Metabolic Markers Related to the Diagnosis of Hepatocellular Carcinoma

Primary liver carcinoma is the sixth most common cancer worldwide, while hepatocellular carcinoma (HCC) is the most dominant cancer type. Chronic hepatitis B and C virus infections and aflatoxin exposure are the main risk factors, while nonalcoholic fatty liver disease caused by obesity, diabetes, and metabolic syndrome are the more common risk factors for HCC. Metabolic disorders caused by these high-risk factors are closely related to the tumor microenvironment of HCC, revealing a possible cause-and-effect relationship between the two. These metabolic disorders involve many complex metabolic pathways, such as carbohydrate, lipid, lipid derivative, amino acid, and amino acid derivative metabolic processes. The resulting metabolites with significant abnormal changes in the concentration level in circulating blood may be used as biomarkers to guide the diagnosis, treatment, or prognosis of HCC. At present, there are high-throughput technologies that can quickly detect small molecular metabolites in many samples. Compared to tissue biopsy, blood samples are easier to obtain, and patients’ willingness to participate is higher, which makes it possible to study blood HCC biomarkers. Over the past few years, a substantial body of research has been performed worldwide, and other potential biomarkers have been identified. Unfortunately, due to the limitations of each study, only a few markers have been widely verified and are suitable for clinical use. This review briefly summarizes the potential blood metabolic markers related to the diagnosis of HCC, mainly focusing on amino acids and their derivative metabolism, lipids and their derivative metabolism, and other possible related metabolisms.

Dysbiosis: The first hit for digestive system cancer

Gastrointestinal cancer may be associated with dysbiosis, which is characterized by an alteration of the gut microbiota. Understanding the role of gut microbiota in the development of gastrointestinal cancer is useful for cancer prevention and gut microbiota-based therapy. However, the potential role of dysbiosis in the onset of tumorigenesis is not fully understood. While accumulating evidence has demonstrated the presence of dysbiosis in the intestinal microbiota of both healthy individuals and patients with various digestive system diseases, severe dysbiosis is often present in patients with digestive system cancer. Importantly, specific bacteria have been isolated from the fecal samples of these patients. Thus, the association between dysbiosis and the development of digestive system cancer cannot be ignored. A new model describing this relationship must be established. In this review, we postulate that dysbiosis serves as the first hit for the development of digestive system cancer. Dysbiosis-induced alterations, including inflammation, aberrant immune response, bacteria-produced genotoxins, and cellular stress response associated with genetic, epigenetic, and/or neoplastic changes, are second hits that speed carcinogenesis. This review explains the mechanisms for these four pathways and discusses gut microbiota-based therapies. The content included in this review will shed light on gut microbiota-based strategies for cancer prevention and therapy.

Pan-cancer analysis of pre-diagnostic blood metabolite concentrations in the European Prospective Investigation into Cancer and Nutrition

BACKGROUND

Epidemiological studies of associations between metabolites and cancer risk have typically focused on specific cancer types separately. Here, we designed a multivariate pan-cancer analysis to identify metabolites potentially associated with multiple cancer types, while also allowing the investigation of cancer type-specific associations.

METHODS

We analysed targeted metabolomics data available for 5828 matched case-control pairs from cancer-specific case-control studies on breast, colorectal, endometrial, gallbladder, kidney, localized and advanced prostate cancer, and hepatocellular carcinoma nested within the European Prospective Investigation into Cancer and Nutrition (EPIC) cohort. From pre-diagnostic blood levels of an initial set of 117 metabolites, 33 cluster representatives of strongly correlated metabolites and 17 single metabolites were derived by hierarchical clustering. The mutually adjusted associations of the resulting 50 metabolites with cancer risk were examined in penalized conditional logistic regression models adjusted for body mass index, using the data-shared lasso penalty.

RESULTS

Out of the 50 studied metabolites, (i) six were inversely associated with the risk of most cancer types: glutamine, butyrylcarnitine, lysophosphatidylcholine a C18:2, and three clusters of phosphatidylcholines (PCs); (ii) three were positively associated with most cancer types: proline, decanoylcarnitine, and one cluster of PCs; and (iii) 10 were specifically associated with particular cancer types, including histidine that was inversely associated with colorectal cancer risk and one cluster of sphingomyelins that was inversely associated with risk of hepatocellular carcinoma and positively with endometrial cancer risk.

CONCLUSIONS

These results could provide novel insights for the identification of pathways for cancer development, in particular those shared across different cancer types.

Plasma metabolomics provides new insights into the relationship between metabolites and outcomes and left ventricular remodeling of coronary artery disease

BACKGROUND

Coronary artery disease (CAD) is a metabolically perturbed pathological condition. However, the knowledge of metabolic signatures on outcomes of CAD and their potential causal effects and impacts on left ventricular remodeling remains limited. We aim to assess the contribution of plasma metabolites to the risk of death and major adverse cardiovascular events (MACE) as well as left ventricular remodeling.

RESULTS

In a prospective study with 1606 Chinese patients with CAD, we have identified and validated several independent metabolic signatures through widely-targeted metabolomics. The predictive model respectively integrating four metabolic signatures (dulcitol, β-pseudouridine, 3,3',5-Triiodo-L-thyronine, and kynurenine) for death (AUC of 83.7% vs. 76.6%, positive IDI of 0.096) and metabolic signatures (kynurenine, lysoPC 20:2, 5-methyluridine, and L-tryptophan) for MACE (AUC of 67.4% vs. 59.8%, IDI of 0.068) yielded better predictive value than trimethylamine N-oxide plus clinical model, which were successfully applied to predict patients with high risks of death (P = 0.0014) and MACE (P = 0.0008) in the multicenter validation cohort. Mendelian randomisation analysis showed that 11 genetically inferred metabolic signatures were significantly associated with risks of death or MACE, such as 4-acetamidobutyric acid, phenylacetyl-L-glutamine, tryptophan metabolites (kynurenine, kynurenic acid), and modified nucleosides (β-pseudouridine, 2-(dimethylamino) guanosine). Mediation analyses show that the association of these metabolites with the outcomes could be partly explained by their roles in promoting left ventricular dysfunction.

CONCLUSIONS

This study provided new insights into the relationship between plasma metabolites and clinical outcomes and its intermediate pathological process left ventricular dysfunction in CAD. The predictive model integrating metabolites can help to improve the risk stratification for death and MACE in CAD. The metabolic signatures appear to increase death or MACE risks partly by promoting adverse left ventricular dysfunction, supporting potential therapeutic targets of CAD for further investigation.

Bile acids as drivers and biomarkers of hepatocellular carcinoma

The prevalence of hepatocellular carcinoma (HCC) is rapidly increasing, driven not least in part by the escalating prevalence of non-alcoholic fatty liver disease. Bile acid (BA) profiles are altered in patients with HCC and there is a developing body of evidence from in vitro human cellular models as well as rodent data suggesting that BA are able to modulate fundamental processes that impact on cellular phenotype predisposing to the development of HCC including senescence, proliferation and epithelial-mesenchymal transition. Changes in BA profiles associated with HCC have the potential to be exploited clinically. Whilst excellent diagnostic and imaging tools are available, their use to screen populations with advanced liver disease at risk of HCC is limited by high cost and low availability. The mainstay for HCC screening among subjects with cirrhosis remains frequent interval ultrasound scanning. Importantly, currently available serum biomarkers add little to diagnostic accuracy. Here, we review the current literature on the use of BA measurements as predictors of HCC incidence in addition to their use as a potential screening method for the early detection of HCC. Whilst these approaches do show early promise, there are limitations including the relatively small cohort sizes, the lack of a standardized approach to BA measurement, and the use of inappropriate control comparator samples.

Identification of Warning Transition Points from Hepatitis B to Hepatocellular Carcinoma Based on Mutation Accumulation for the Early Diagnosis and Potential Drug Treatment of HBV-HCC

The accumulation of multiple genetic mutations is essential during the occurrence and development of hepatocellular carcinoma induced by hepatitis B (HBV-HCC), but understanding their cooperative effects and identifying the warning transition point from hepatitis B to HCC are challenges. In the genomic analysis of somatic mutations of the patient with HBV-HCC in a patient-specific protein-protein interaction (ps-PPI) network, we find mutation influence can propagate along the ps-PPI network. Therefore, in the article, we got the mutation cluster as a new research unit using the Random Walks with Restarts algorithm that is used to describe the efficient boundary of mutation influences. The connection of mutation cluster leads to dysregulation of signaling pathways corresponding to HCC, while dysregulated signaling pathways accumulate gradually and experience a process from quantitative to qualitative changes including a critical mutation cluster called transition point (TP) from hepatitis B to HCC. Moreover, two subtypes of HCC patients with different prognosis and their corresponding biological and clinical characteristics were identified according to TP. The poor prognosis HCC subtype was associated with significant metabolic pathway dysregulation and lower immune cell infiltration, while we also identified several preventive drugs to block the transformation of hepatitis B to hepatocellular carcinoma. The network-level study integrated multiomics data not only showed the sequence of multiple somatic mutations and their cooperative effect but also identified the warning transition point in HCC tumorigenesis for each patient. Our study provides new insight into exploring the cooperative molecular mechanism of chronic inflammatory malignancy in the liver and lays the foundation for the development of new approaches for early prediction and diagnosis of hepatocellular carcinoma and personalized targeted therapy.

Metabolic signatures of hepatolithiasis using ultra-high performance liquid chromatography-tandem mass spectrometry

BACKGROUND & AIMS

A metabolomic study of hepatolithiasis has yet to be performed. The purpose of the present study was to characterize the metabolite profile and identify potential biomarkers of hepatolithiasis using a metabolomic approach.

METHODS

We comprehensively analyzed the serum metabolites from 30 patients with hepatolithiasis and 20 healthy individuals using ultra-high performance liquid chromatography-tandem mass spectrometry operated in negative and positive ionization modes. Statistical analyses were performed using univariate (Student's t-test) and multivariate (orthogonal partial least-squares discriminant analysis) statistics and R language. Receiver operator characteristic (ROC) curve analysis was performed to identify potential predictors of hepatolithiasis.

RESULTS

We identified 277 metabolites that were significantly different between hepatolithiasis serum group and healthy control serum group. These metabolites were principally lipids and lipid-like molecules and amino acid metabolites. The steroid hormone biosynthesis pathway was enriched in hepatolithiasis serum group. In all specific metabolites, 75 metabolites were over-expressed in hepatolithiasis serum group. The AUC values for 60 metabolites exceeded 0.70, 4 metabolites including 18-β-Glycyrrhetinic acid, FMH, Rifampicin and PC (4:0/16:2) exceeded 0.90.

CONCLUSIONS

We have identified serum metabolites that are associated with hepatolithiasis for the first time. 60 potential metabolic biomarkers were identified, 18-β-Glycyrrhetinic acid, FMH, Rifampicin and PC (4:0/16:2) may have the potential clinical utility in hepatolithiasis.

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.

A Serum Metabolite Classifier for the Early Detection of Type 2 Diabetes Mellitus-Positive Hepatocellular Cancer

Type 2 diabetes mellitus (T2DM) has been identified as an independent risk factor for hepatocellular cancer (HCC). However, there are no ideal biomarkers for the surveillance and early detection of HCC in the T2DM population at present. In this study, we aimed to explore novel metabolite biomarkers for T2DM-positive [T2DM(+)] HCC by metabolomic analysis. At first, many serum metabolites were found dysregulated in T2DM(+) HCC patients in untargeted metabolomic analyses. Targeted metabolite analyses confirmed that serum benzoic acid and citrulline were increased, and creatine was decreased in T2DM(+) HCC compared to the T2DM group. A metabolite classifier including benzoic acid, creatine, and citrulline was identified as a novel biomarker for the diagnosis of T2DM(+) HCC, with an area under the ROC curve (AUC) of 0.93 for discriminating T2DM(+) HCC patients from T2DM patients. In addition, the metabolite classifier detected small-size (AUC = 0.94), early-stage (AUC = 0.94), and AFP-negative (AUC = 0.96) tumors with high sensitivity and specificity. The combination of this metabolite classifier and AFP might be useful in the surveillance and early detection of HCC in the T2DM population. In conclusion, this study establishes a novel diagnostic tool for T2DM(+) HCC.

A prospective investigation of serum bile acids with risk of liver cancer, fatal liver disease, and biliary tract cancer

Bile acids (BAs), major regulators of the gut microbiota, may play an important role in hepatobiliary cancer etiology. However, few epidemiologic studies have comprehensively examined associations between BAs and liver or biliary tract cancer. In the Alpha-Tocopherol, Beta-Carotene Cancer Prevention (ATBC) study, we designed 1:1 matched, nested, case-control studies of primary liver cancer (n = 201 cases), fatal liver disease (n = 261 cases), and primary biliary tract cancer (n = 138 cases). Using baseline serum collected ≤30 years before diagnosis or death, we measured concentrations of 15 BAs with liquid chromatography-tandem mass spectrometry. We estimated odds ratios (ORs) and 95% confidence intervals (CIs) using multivariable conditional logistic regression models, adjusted for age, education, diabetes status, smoking, alcohol intake, and body mass index. We accounted for multiple comparisons using a false discovery rate (FDR) correction. Comparing the highest to the lowest quartile, seven BAs were positively associated with liver cancer risk, including taurocholic acid (TCA) (OR, 5.62; 95% CI, 2.74-11.52; Q trend < 0.0001), taurochenodeoxycholic acid (TCDCA) (OR, 4.77; 95% CI, 2.26-10.08; Q trend < 0.0001), and glycocholic acid (GCA) OR, 5.30; 95% CI, 2.41-11.66; Q trend < 0.0001), and 11 were positively associated with fatal liver disease risk, including TCDCA (OR, 9.65; 95% CI, 4.41-21.14; Q trend < 0.0001), TCA (OR, 7.45; 95% CI, 3.70-14.97; Q trend < 0.0001), and GCA (OR, 6.98; 95% CI, 3.32-14.68; Q trend < 0.0001). For biliary tract cancer, associations were generally >1 but not significant after FDR correction. Conjugated BAs were strongly associated with increased risk of liver cancer and fatal liver disease, suggesting mechanistic links between BA metabolism and liver cancer or death from liver disease.

Prediagnostic alterations in circulating bile acid profiles in the development of hepatocellular carcinoma

Bile acids (BAs) play different roles in cancer development. Some are carcinogenic and BA signaling is also involved in various metabolic, inflammatory and immune-related processes. The liver is the primary site of BA synthesis. Liver dysfunction and microbiome compositional changes, such as during hepatocellular carcinoma (HCC) development, may modulate BA metabolism increasing concentration of carcinogenic BAs. Observations from prospective cohorts are sparse. We conducted a study (233 HCC case-control pairs) nested within a large observational prospective cohort with blood samples taken at recruitment when healthy with follow-up over time for later cancer development. A targeted metabolomics method was used to quantify 17 BAs (primary/secondary/tertiary; conjugated/unconjugated) in prediagnostic plasma. Odd ratios (OR) for HCC risk associations were calculated by multivariable conditional logistic regression models. Positive HCC risk associations were observed for the molar sum of all BAs (ORdoubling  = 2.30, 95% confidence intervals [CI]: 1.76-3.00), and choline- and taurine-conjugated BAs. Relative concentrations of BAs showed positive HCC risk associations for glycoholic acid and most taurine-conjugated BAs. We observe an association between increased HCC risk and higher levels of major circulating BAs, from several years prior to tumor diagnosis and after multivariable adjustment for confounders and liver functionality. Increase in BA concentration is accompanied by a shift in BA profile toward higher proportions of taurine-conjugated BAs, indicating early alterations of BA metabolism with HCC development. Future studies are needed to assess BA profiles for improved stratification of patients at high HCC risk and to determine whether supplementation with certain BAs may ameliorate liver dysfunction.

Liver cancer risk-predictive molecular biomarkers specific to clinico-epidemiological contexts

Hepatocellular carcinoma (HCC) risk prediction is increasingly important because of the low annual HCC incidence in patients with the rapidly emerging non-alcoholic fatty liver disease or cured HCV infection. To date, numerous clinical HCC risk biomarkers and scores have been reported in literature. However, heterogeneity in clinico-epidemiological context, e.g., liver disease etiology, patient race/ethnicity, regional environmental exposure, and lifestyle-related factors, obscure their real clinical utility and applicability. Proper characterization of these factors will help refine HCC risk prediction according to certain clinical context/scenarios and contribute to improved early HCC detection. Molecular factors underlying the clinical heterogeneity encompass various features in host genetics, hepatic and systemic molecular dysregulations, and cross-organ interactions, which may serve as clinical-context-specific biomarkers and/or therapeutic targets. Toward the goal to enable individual-risk-based HCC screening by incorporating the HCC risk biomarkers/scores, their assessment in patient with well-defined clinical context/scenario is critical to gauge their real value and to maximize benefit of the tailored patient management for substantial improvement of the poor HCC prognosis.

Novel Biomarkers of Habitual Alcohol Intake and Associations With Risk of Pancreatic and Liver Cancers and Liver Disease Mortality

BACKGROUND

Alcohol is an established risk factor for several cancers, but modest alcohol-cancer associations may be missed because of measurement error in self-reported assessments. Biomarkers of habitual alcohol intake may provide novel insight into the relationship between alcohol and cancer risk.

METHODS

Untargeted metabolomics was used to identify metabolites correlated with self-reported habitual alcohol intake in a discovery dataset from the European Prospective Investigation into Cancer and Nutrition (EPIC; n = 454). Statistically significant correlations were tested in independent datasets of controls from case-control studies nested within EPIC (n = 280) and the Alpha-Tocopherol, Beta-Carotene Cancer Prevention (ATBC; n = 438) study. Conditional logistic regression was used to estimate odds ratios (ORs) and 95% confidence intervals (CIs) for associations of alcohol-associated metabolites and self-reported alcohol intake with risk of pancreatic cancer, hepatocellular carcinoma (HCC), liver cancer, and liver disease mortality in the contributing studies.

RESULTS

Two metabolites displayed a dose-response association with self-reported alcohol intake: 2-hydroxy-3-methylbutyric acid and an unidentified compound. A 1-SD (log2) increase in levels of 2-hydroxy-3-methylbutyric acid was associated with risk of HCC (OR = 2.54, 95% CI = 1.51 to 4.27) and pancreatic cancer (OR = 1.43, 95% CI = 1.03 to 1.99) in EPIC and liver cancer (OR = 2.00, 95% CI = 1.44 to 2.77) and liver disease mortality (OR = 2.16, 95% CI = 1.63 to 2.86) in ATBC. Conversely, a 1-SD (log2) increase in questionnaire-derived alcohol intake was not associated with HCC or pancreatic cancer in EPIC or liver cancer in ATBC but was associated with liver disease mortality (OR = 2.19, 95% CI = 1.60 to 2.98) in ATBC.

CONCLUSIONS

2-hydroxy-3-methylbutyric acid is a candidate biomarker of habitual alcohol intake that may advance the study of alcohol and cancer risk in population-based studies.

Circulating acetylated polyamines correlate with Covid-19 severity in cancer patients

Cancer patients are particularly susceptible to the development of severe Covid-19, prompting us to investigate the serum metabolome of 204 cancer patients enrolled in the ONCOVID trial. We previously described that the immunosuppressive tryptophan/kynurenine metabolite anthranilic acid correlates with poor prognosis in non-cancer patients. In cancer patients, we observed an elevation of anthranilic acid at baseline (without Covid-19 diagnosis) and no further increase with mild or severe Covid-19. We found that, in cancer patients, Covid-19 severity was associated with the depletion of two bacterial metabolites, indole-3-proprionate and 3-phenylproprionate, that both positively correlated with the levels of several inflammatory cytokines. Most importantly, we observed that the levels of acetylated polyamines (in particular N1-acetylspermidine, N1,N8-diacetylspermidine and N1,N12-diacetylspermine), alone or in aggregate, were elevated in severe Covid-19 cancer patients requiring hospitalization as compared to uninfected cancer patients or cancer patients with mild Covid-19. N1-acetylspermidine and N1,N8-diacetylspermidine were also increased in patients exhibiting prolonged viral shedding (>40 days). An abundant literature indicates that such acetylated polyamines increase in the serum from patients with cancer, cardiovascular disease or neurodegeneration, associated with poor prognosis. Our present work supports the contention that acetylated polyamines are associated with severe Covid-19, both in the general population and in patients with malignant disease. Severe Covid-19 is characterized by a specific metabolomic signature suggestive of the overactivation of spermine/spermidine N1-acetyl transferase-1 (SAT1), which catalyzes the first step of polyamine catabolism.

Tobacco, Cigarettes, and the Liver: The Smoking Gun

The association between alcohol and liver disease, including alcoholic hepatitis, cirrhosis, acute-on-chronic liver failure, and hepatocellular carcinoma, has been well described, but the same cannot be said for the association between smoking, water pipe or tobacco chewing. A review of cumulative evidence suggests that smoking is independently a risk factor for liver fibrosis and contributes to carcinogenesis in HCC. Smoking-related fibrosis has been reported in patients with nonalcoholic fatty liver disease, primary biliary cholangitis, alcoholic liver disease and chronic viral hepatitis. Heavy smoking leads to systemic inflammation, oxidative stress, insulin resistance, and results in tissue hypoxia, as well as free radical damage. Other than damaging the liver, patients also suffer from the systemic effects of the 4000 chemicals associated with tobacco, which include nitrosamines, aromatic hydrocarbons, nicotine, nornicotine, and other alkaloids. These include respiratory ailments, cancer of the lungs, oral cavity, esophagus, pancreas and colon, atherosclerotic vascular disease, and stroke.

Metabolic Profiling Revealed Prediction Biomarkers for Infantile Hemangioma in Umbilical Cord Blood Sera: A Prospective Study

Infantile hemangioma (IH), the most common benign tumor in infancy, mostly arises and has rapid growth before 3 months of age. Because irreversible skin changes occur in the early proliferative stage, early medical treatment is essential to reduce the permanent sequelae caused by IH. Yet there are still no early screening biomarkers for IH before its visible emergence. This study aimed to explore prediction biomarkers using noninvasive umbilical cord blood (UCB). A prospective study of the metabolic profiling approach was performed on UCB sera from 28 infants with IH and 132 matched healthy controls from a UCB population comprising over 1500 infants (PeptideAtlas: PASS01675) using liquid chromatography-mass spectrometry. The metabolic profiling results exhibited the characteristic metabolic aberrance of IH. Machine learning suggested a panel of biomarkers to predict the occurrence of IH, with the area under curve (AUC) values in the receiver operating characteristic analysis all >0.943. Phenylacetic acid had potential to predict infants with large IH (diameter >2 cm) from those with small IH (diameter <2 cm), with an AUC of 0.756. The novel biomarkers in noninvasive UCB sera for predicting IH before its emergence might lead to a revolutionary clinical utility.

Quantitative Analysis of Methylated Adenosine Modifications Revealed Increased Levels of N 6-Methyladenosine (m6A) and N 6,2'-O-Dimethyladenosine (m6Am) in Serum From Colorectal Cancer and Gastric Cancer Patients

Colorectal cancer and gastric cancer are the most prevalent gastrointestinal malignancies worldwide, and early detection of these cancers is crucial to reduce their incidence and mortality. RNA methylation plays an important regulatory role in a variety of physiological activities, and it has drawn great attention in recent years. Methylated adenosine (A) modifications such as N⁶-methyladenosine (m⁶A), N¹-methyladenosine (m¹A), 2′-O-methyladenosine (Am), N⁶,2′-O-dimethyladenosine (m⁶Am), and N⁶,N⁶-dimethyladenosine (m⁶₂A) are typical epigenetic markers of RNA, and they are closely correlated to various diseases including cancer. Serum is a valuable source of biofluid for biomarker discovery, and determination of these adenosine modifications in human serum is desirable since they are emerging biomarkers for detection of diseases. In this work, a targeted quantitative analysis method using hydrophilic interaction liquid chromatography–tandem mass spectrometry (HILIC-MS/MS) was developed and utilized to analyze these methylated adenosine modifications in serum samples. The concentration differences between the healthy volunteers and cancer patients were evaluated by Mann–Whitney test, and receiver operator characteristic (ROC) curve analysis was performed to access the potential of these nucleosides as biomarkers. We demonstrated the presence of the m⁶Am in human serum for the first time, and we successfully quantified the concentrations of A, m⁶A, m¹A, and m⁶Am in serum samples from 99 healthy controls, 51 colorectal cancer patients, and 27 gastric cancer patients. We found that the levels of m⁶A and m⁶Am in serum were both increased in colorectal cancer or gastric cancer patients, compared to that in healthy controls. These results indicate that m⁶A and m⁶Am in serum may act as potential biomarkers for early detection and prognosis of colorectal cancer and gastric cancer. In addition, the present work will stimulate investigations on the effects of adenosine methylation on the initiation and progression of colorectal cancer and gastric cancer.

Lipidomic Profiles of Plasma Exosomes Identify Candidate Biomarkers for Early Detection of Hepatocellular Carcinoma in Patients with Cirrhosis

Novel biomarkers for HCC surveillance in cirrhotic patients are urgently needed. Exosomes and their lipid content in particular represent potentially valuable noninvasive diagnostic biomarkers. We isolated exosomes from plasma of 72 cirrhotic patients, including 31 with HCC. Exosomes and unfractionated plasma were processed for untargeted lipidomics using ultra-high-resolution mass spectrometry. A total of 2,864 lipid species, belonging to 52 classes, were identified. Both exosome fractionation and HCC diagnosis had significant impact on the lipid profiles. Ten lipid classes were enriched in HCC exosomes compared with non-HCC exosomes. Dilysocardiolipins were detected in 35% of the HCC exosomes but in none of the non-HCC exosomes (P < 0.001). Cardiolipins and sphingosines had the highest differential effects (fold change of 133.08, q = 0.001 and 38.57, q < 0.001, respectively). In logistic regression analysis, high abundances of exosomal sphingosines, dilysocardiolipins, lysophosphatidylserines, and (O-acyl)-1-hydroxy fatty acids were strongly associated with HCC [OR (95% confidence interval (CI)), 271.1 (14.0-5,251.9), P < 0.001; 46.5 (2.3-939.9), P = 0.012; 14.9 (4.3-51.2), P < 0.001; 10.3 (3.2-33.1), P < 0.001]. Four lipid classes were depleted in HCC exosomes compared with non-HCC exosomes. In logistic regression analysis, lack of detection of sulfatides and acylGlcSitosterol esters was strongly associated with HCC [OR (95% CI): 215.5 (11.5-4,035.9), P < 0.001; 26.7 (1.4-528.4), P = 0.031]. These HCC-associated changes in lipid composition of exosomes reflected alterations in glycerophospholipid metabolism, retrograde endocannabinoid signaling, and ferroptosis. In conclusion, this study identified candidate biomarkers for early detection of HCC as well as altered pathways in exosomes that may contribute to tumor development and progression. PREVENTION RELEVANCE: This study identifies lipids in circulating exosomes, that could serve as biomarkers for the early detection of hepatocellular carcinoma as well as altered pathways in exosomes that may contribute to tumor development and progression.

Knockout of Putative Tumor Suppressor Aldh1l1 in Mice Reprograms Metabolism to Accelerate Growth of Tumors in a Diethylnitrosamine (DEN) Model of Liver Carcinogenesis

Simple Summary

Cancers often loose the enzyme of folate metabolism ALDH1L1. We proposed that such loss is advantageous for the malignant tumor growth and tested this hypothesis in mice proficient or deficient (gene knockout) in ALDH1L1 expression. Liver cancer in both groups was induced by injection of chemical carcinogen diethylnitrosamine. While the number of tumors observed in ALDH1L1 proficient and deficient mice was similar, tumors grew faster and to a larger size in the knockout mice. We conclude that the ALDH1L1 loss promotes liver tumor growth without affecting tumor initiation or multiplicity. Accelerated growth of tumors lacking the enzyme was linked to several metabolic pathways, which are beneficial for rapid proliferation.

Abstract

Cytosolic 10-formyltetrahydrofolate dehydrogenase (ALDH1L1) is commonly downregulated in human cancers through promoter methylation. We proposed that ALDH1L1 loss promotes malignant tumor growth. Here, we investigated the effect of the Aldh1l1 mouse knockout (Aldh1l1^−/−) on hepatocellular carcinoma using a chemical carcinogenesis model. Fifteen-day-old male Aldh1l1 knockout mice and their wild-type littermate controls (Aldh1l1^+/+) were injected intraperitoneally with 20 μg/g body weight of DEN (diethylnitrosamine). Mice were sacrificed 10, 20, 28, and 36 weeks post-DEN injection, and livers were examined for tumor multiplicity and size. We observed that while tumor multiplicity did not differ between Aldh1l1^−/− and Aldh1l1^+/+ animals, larger tumors grew in Aldh1l1^−/− compared to Aldh1l1^+/+ mice at 28 and 36 weeks. Profound differences between Aldh1l1^−/− and Aldh1l1^+/+ mice in the expression of inflammation-related genes were seen at 10 and 20 weeks. Of note, large tumors from wild-type mice showed a strong decrease of ALDH1L1 protein at 36 weeks. Metabolomic analysis of liver tissues at 20 weeks showed stronger differences in Aldh1l1^+/+ versus Aldh1l1^−/− metabotypes than at 10 weeks, which underscores metabolic pathways that respond to DEN in an ALDH1L1-dependent manner. Our study indicates that Aldh1l1 knockout promoted liver tumor growth without affecting tumor initiation or multiplicity.

Data Processing Thresholds for Abundance and Sparsity and Missed Biological Insights in an Untargeted Chemical Analysis of Blood Specimens for Exposomics

Background: An untargeted chemical analysis of bio-fluids provides semi-quantitative data for thousands of chemicals for expanding our understanding about relationships among metabolic pathways, diseases, phenotypes and exposures. During the processing of mass spectral and chromatography data, various signal thresholds are used to control the number of peaks in the final data matrix that is used for statistical analyses. However, commonly used stringent thresholds generate constrained data matrices which may under-represent the detected chemical space, leading to missed biological insights in the exposome research. Methods: We have re-analyzed a liquid chromatography high resolution mass spectrometry data set for a publicly available epidemiology study (n = 499) of human cord blood samples using the MS-DIAL software with minimally possible thresholds during the data processing steps. Peak list for individual files and the data matrix after alignment and gap-filling steps were summarized for different peak height and detection frequency thresholds. Correlations between birth weight and LC/MS peaks in the newly generated data matrix were computed using the spearman correlation coefficient. Results: MS-DIAL software detected on average 23,156 peaks for individual LC/MS file and 63,393 peaks in the aligned peak table. A combination of peak height and detection frequency thresholds that was used in the original publication at the individual file and the peak alignment levels can reject 90% peaks from the untargeted chemical analysis dataset that was generated by MS-DIAL. Correlation analysis for birth weight data suggested that up to 80% of the significantly associated peaks were rejected by the data processing thresholds that were used in the original publication. The re-analysis with minimum possible thresholds recovered metabolic insights about C19 steroids and hydroxy-acyl-carnitines and their relationships with birth weight. Conclusions: Data processing thresholds for peak height and detection frequencies at individual data file and at the alignment level should be used at minimal possible level or completely avoided for mining untargeted chemical analysis data in the exposome research for discovering new biomarkers and mechanisms.

Biomarkers for hepatocellular carcinoma based on body fluids and feces

Novel non-/minimally-invasive and effective approaches are urgently needed to supplement and improve current strategies for diagnosis and management of hepatocellular carcinoma (HCC). Overwhelming evidence from published studies on HCC has documented that multiple molecular biomarkers detected in body fluids and feces can be utilized in early-diagnosis, predicting responses to specific therapies, evaluating prognosis before or after therapy, as well as serving as novel therapeutic targets. Detection and analysis of proteins, metabolites, circulating nucleic acids, circulating tumor cells, and extracellular vesicles in body fluids (e.g., blood and urine) and gut microbiota (e.g., in feces) have excellent capabilities to improve different aspects of management of HCC. Numerous studies have been devoted in identifying more promising candidate biomarkers and therapeutic targets for diagnosis, treatment, and monitoring responses of HCC to conventional therapies, most of which may improve diagnosis and management of HCC in the future. This review aimed to summarize recent advances in utilizing these biomarkers in HCC and discuss their clinical significance.

Metabolic Profiling Identified a Novel Biomarker Panel for Metabolic Syndrome-Positive Hepatocellular Cancer

Metabolic syndrome (MetS) is an independent risk factor for hepatocellular cancer (HCC). Currently, there is no highly sensitive and specific biomarkers for HCC surveillance in MetS population. Metabolomics has been reported as a powerful technology for biomarker discovery. In the present study, we aimed to explore novel biomarkers with high sensitivity and specificity for MetS-positive [MetS(+)] HCC by metabolomic analysis. At first, many serum metabolites were found dysregulated in MetS(+) HCC individuals. Validation of the dysregulated metabolites by targeted metabolite analyses revealed that serum L-glutamic acid (L-glu), pipecolic acid (PA) and 7-methylguanine (7-mG) were increased in MetS(+) HCC compared to MetS group. Then a biomarker panel including L-glu, PA and alpha-fetoprotein (AFP) was identified as a novel biomarker for the diagnosis of MetS(+) HCC. Receiver operating characteristic (ROC) curve was drawn and the area under the ROC curve (AUC) was 0.87 for discriminating MetS(+) HCC from MetS group. The biomarker panel was capable of detecting small (AUC = 0.82) and early-stage (AUC = 0.78) tumors as well. Moreover, it exhibited great diagnostic performance (AUC = 0.93) for discriminating MetS(+) HCC from other MetS-associated cancers, including colorectal cancer and gastric cancer. Collectively, our study establishes a novel diagnostic tool for MetS(+) HCC.

Clinical and Molecular Prediction of Hepatocellular Carcinoma Risk

Prediction of hepatocellular carcinoma (HCC) risk becomes increasingly important with recently emerging HCC-predisposing conditions, namely non-alcoholic fatty liver disease and cured hepatitis C virus infection. These etiologies are accompanied with a relatively low HCC incidence rate (~1% per year or less), while affecting a large patient population. Hepatitis B virus infection remains a major HCC risk factor, but a majority of the patients are now on antiviral therapy, which substantially lowers, but does not eliminate, HCC risk. Thus, it is critically important to identify a small subset of patients who have elevated likelihood of developing HCC, to optimize the allocation of limited HCC screening resources to those who need it most and enable cost-effective early HCC diagnosis to prolong patient survival. To date, numerous clinical-variable-based HCC risk scores have been developed for specific clinical contexts defined by liver disease etiology, severity, and other factors. In parallel, various molecular features have been reported as potential HCC risk biomarkers, utilizing both tissue and body-fluid specimens. Deep-learning-based risk modeling is an emerging strategy. Although none of them has been widely incorporated in clinical care of liver disease patients yet, some have been undergoing the process of validation and clinical development. In this review, these risk scores and biomarker candidates are overviewed, and strategic issues in their validation and clinical translation are discussed.