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

Investigation of pH-dependent 1H NMR urine metabolite profiles for diagnosis of obesity-related disordering

BACKGROUND

Human urine is highly favorable for 1H NMR metabolomics analyses of obesity-related diseases, such as non-alcoholic fatty liver, type 2 diabetes, and hyperlipidemia (HL), due to its non-invasiveness and ease of large-scale collection. However, the wide range of intrinsic urine pH (5.5-8.5) results in inevitably chemical shift and signal intensity modulations in the 1H NMR spectra. For patients where acidic urine pH is closely linked to obesity-related disease phenotypes, the pH-dependent modulations complicate the spectral analysis and deteriorate quantifications of urine metabolites.

METHODS

We characterized human urine metabolites by NMR at intrinsic urine pH, across urine pH 4.5 to 9.5, to account for pH-dependent modulations. A pH-dependent chemical shift database for quantifiable urine metabolites was generated and integrated into a "pH intelligence" program developed for quantifications of pH-dependent modulations at various pH. The 1H NMR spectra of urines collected from patients with Ob-HL and healthy controls were compared to uncover potential metabolic biomarkers of Ob-HL disease.

RESULTS

Three urine metabolites were unveiled by pH-dependent NMR approach, i.e., TMAO, glycine, and pyruvic acid, with VIP score >1.0 and significant q-value < 0.05, that represent as potential biomarkers for discriminating Ob-HL from healthy controls. Further ROC-AUC analyses revealed that TMAO alone achieved the highest diagnostic accuracy (AUC 0.902), surpassed to that obtained by neutralizing pH approach (AUC 0.549) and enabled better recovering potential urine metabolites from the Ob-HL disease phenotypes.

CONCLUSIONS

We concluded that 1H NMR-derived urine metabolite profile represents a snapshot that can reveal the physiological condition of humans in either a healthy or diseased state under intrinsic urine pH. We demonstrated a systematic analysis of pH-dependent modulations on the human urine metabolite signals and further developed software for quantification of urine metabolite profiles with high accuracy, enabling the uncovering of potential metabolite biomarkers in clinical diagnosis applications.

Interactions between the metabolic reprogramming of liver cancer and tumor microenvironment

Metabolic reprogramming is one of the major biological features of malignant tumors, playing a crucial role in the initiation and progression of cancer. The tumor microenvironment consists of various non-cancer cells, such as hepatic stellate cells, cancer-associated fibroblasts (CAFs), immune cells, as well as extracellular matrix and soluble substances. In liver cancer, metabolic reprogramming not only affects its own growth and survival but also interacts with other non-cancer cells by influencing the expression and release of metabolites and cytokines (such as lactate, PGE2, arginine). This interaction leads to acidification of the microenvironment and restricts the uptake of nutrients by other non-cancer cells, resulting in metabolic competition and symbiosis. At the same time, metabolic reprogramming in neighboring cells during proliferation and differentiation processes also impacts tumor immunity. This article provides a comprehensive overview of the metabolic crosstalk between liver cancer cells and their tumor microenvironment, deepening our understanding of relevant findings and pathways. This contributes to further understanding the regulation of cancer development and immune evasion mechanisms while providing assistance in advancing personalized therapies targeting metabolic pathways for anti-cancer treatment.

Porous Silicon Particle-Assisted Mass Spectrometry Technology Unlocks Serum Metabolic Fingerprints in the Progression From Chronic Hepatitis B to Hepatocellular Carcinoma

Hepatocellular carcinoma (HCC) is a common malignancy and generally develops from liver cirrhosis (LC), which is primarily caused by the chronic hepatitis B (CHB) virus. Reliable liquid biopsy methods for HCC screening in high-risk populations are urgently needed. Here, we establish a porous silicon-assisted laser desorption ionization mass spectrometry (PSALDI-MS) technology to profile metabolite information hidden in human serum in a high throughput manner. Serum metabolites can be captured in the pore channel of APTES-modified porous silicon (pSi) particles and well-preserved during storage or transportation. Furthermore, serum metabolites captured in the APTES-pSi particles can be directly detected on the LDI-MS without the addition of an organic matrix, thus greatly accelerating the acquisition of metabolic fingerprints of serum samples. The PSALDI-MS displays the capability of high throughput (5 min per 96 samples), high reproducibility (coefficient of variation <15%), high sensitivity (LOD ∼ 1 pmol), and high tolerance to background salt and proteins. In a multicenter cohort study, 1433 subjects including healthy controls (HC), CHB, LC, and HCC volunteers were enrolled and nontargeted serum metabolomic analysis was performed on the PSALDI-MS platform. After the selection of feature metabolites, a stepwise diagnostic model for the classification of different liver disease stages was constructed by the machine learning algorithm. In external testing, the accuracy of 91.2% for HC, 71.4% for CHB, 70.0% for LC, and 95.3% for HCC was achieved by chemometrics. Preliminary studies indicated that the diagnostic model constructed from serum metabolic fingerprint also displays good predictive performance in a prospective observation. We believe that the combination of PSALDI-MS technology and machine learning may serve as an efficient tool in clinical practice.

1H-NMR-based metabolomic profiling and proteomic analysis of soybean (Glycine max L.) in response to dicarboxylic acids (photon) application as a stress priming agent

Soybean (Glycine max L.) serves not only as food for humans, animals, and industrial purposes, but is also a plant that can be used to comprehend molecular mechanisms occurring in stress response to various development techniques. To reveal the effect of applying dicarboxylic acids as stress priming agents on a metabolic level in soybean leaf extracts, the chemical profile of methanolic extracts were collected at different time points (1 h, 2 h, 12 h, 24 h, 1 week, 2 weeks and 3 weeks) after spraying were analyzed using 1H-NMR based metabolomics by way of PCA and OPLS-DA. The OPLS-DA revealed several metabolites, including organic acids (fumarate, citrate and malate) and amino acids (asparagine, alanine and GABA), which accumulated in higher amounts, with fumarate accumulating the highest in Glycine max L. leaf extracts compared to untreated leaves. Denaturing 1DE gels were prepared for MS-based protein analysis and the presence of fatty acids (linolenic, oleic and α-linolenic acid) were confirmed by gas chromatography coupled with mass spectrometry (GC-MS), which served as jasmonic acid precursors. The MS-based profiling of proteins on the denaturing 1DE gels revealed several proteins that were differentiated between the treated and untreated leaf extracts. These proteins included ferritins, CaM, ferredoxin-thioredoxin reductase and chalcone-flavanone isomerase 1A. Following the treatment, fumarate was significantly elevated at 12 h to 3 weeks, compared to other compounds. It is, therefore, proposed that elevated quantities of fumarate could be related to the KEAP1-NRF2 metabolic pathway. This study represents the initial investigation of the effect of dicarboxylic acid application as a stress priming agent on Glycine max L. using 1H-NMR metabolomic analysis, GC-MS and proteomic analysis.

Nuclear magnetic resonance spectroscopy reveals biomarkers of stroke recovery in a mouse model of obesity-associated type 2 diabetes

Obesity and Type 2 diabetes (T2D) are known to exacerbate cerebral injury caused by stroke. Metabolomics can provide signatures of metabolic disease, and now we explored whether the analysis of plasma metabolites carries biomarkers of how obesity and T2D impact post-stroke recovery. Male mice were fed a high-fat diet (HFD) for 10 months leading to development of obesity with T2D or a standard diet (non-diabetic mice). Then, mice were subjected to either transient middle cerebral artery occlusion (tMCAO) or sham surgery and allowed to recover on standard diet for 2 months before serum samples were collected. Nuclear magnetic resonance (NMR) spectroscopy of serum samples was used to investigate metabolite signals and metabolic pathways that were associated with tMCAO recovery in either T2D or non-diabetic mice. Overall, after post-stroke recovery there were different serum metabolite profiles in T2D and non-diabetic mice. In non-diabetic mice, which show full neurological recovery after stroke, we observed a reduction of isovalerate, and an increase of kynurenate, uridine monophosphate, gluconate and N6-acetyllysine in tMCAO relative to sham mice. In contrast, in mice with T2D, which show impaired stroke recovery, there was a reduction of N,N-dimethylglycine, succinate and proline, and an increase of 2-oxocaproate in serum of tMCAO versus sham mice. Given the inability of T2D mice to recover from stroke, in contrast with non-diabetic mice, we propose that these specific metabolite changes following tMCAO might be used as biomarkers of neurophysiological recovery after stroke in T2D.

Screening of postoperative adjuvant chemotherapy-related serum metabolic markers in breast cancer patients based on 1H NMR metabonomics

Background

Breast cancer (BC) has the highest incidence rate among female malignant tumors. Adjuvant chemotherapy is commonly used to reduce micrometastasis in postoperative patients. However, monitoring the efficacy of chemotherapy in BC is a major challenge in clinical practice. In this study, 1H nuclear magnetic resonance (NMR) metabonomics was performed to explore the serum metabolic characteristics of BC patients before and after adjuvant chemotherapy.

Methods

In this study, we collected serum samples from 51 healthy controls and 61 BC patients before and after chemotherapy for 1H NMR metabolomic analysis, and tested the performance of each metabolite and combination segment by the receiver operating characteristic (ROC) curves.

Results

Nine metabolites, namely glutamine, citrate, creatine, glycerophosphatidylcholine/phosphatidylcholine, glycine, 1-methylhistidine, lactate, pyruvate and formate had significant changes in BC patients before chemotherapy compared with healthy controls. Lactate, pyruvate, 1-methylhistidine and formate were found to be inversely regulated by chemotherapy. ROC analysis showed that a combination of the four metabolites had good prediction for chemotherapy efficacy with area under the curve of 0.958, sensitivity of 98.36% and specificity of 91.30%. There was no significant correlation between chemotherapy-related metabolites and clinical indicators of cancer patients, indicating that they can be used to evaluate the chemotherapy efficacy of patients with different clinical indicators.

Conclusions

Effectively, dynamic and non-invasive metabolic markers for the evaluation of the efficacy of chemotherapy were identified in this study.

Identification of 1-Methylnicotinamide as a specific biomarker for the progression of cirrhosis to hepatocellular carcinoma

PURPOSE

Hepatocellular carcinoma (HCC) is a prevalent malignant tumor, often arising from hepatitis induced by the hepatitis B virus (HBV) in China. However, effective biomarkers for early diagnosis are lacking, leading to a 5-year overall survival rate of less than 20% among patients with advanced HCC. This study aims to identify serum biomarkers for early HCC diagnosis to enhance patient survival rates.

METHODS

We established an independent cohort comprising 27 healthy individuals, 13 patients with HBV-induced cirrhosis, 13 patients with hepatitis B-type HCC, and 8 patients who progressed from cirrhosis to hepatocellular carcinoma during follow-up. Serum metabolic abnormalities during the progression from cirrhosis to HCC were studied using untargeted metabolomics. Liquid chromatography-mass spectrometry-based metabolomics methods characterized the subjects' serum metabolic profiles. Partial least squares discriminant analysis (PLS-DA) was employed to elucidate metabolic profile changes during the progression from cirrhosis to HCC. Differentially expressed metabolites (DEMs) between cirrhosis and HCC groups were identified using the LIMMA package in the R language. Two machine learning algorithms, Least Absolute Shrinkage and Selection Operator (LASSO), and Random Forest Classifier (RF), were used to identify key metabolic biomarkers involved in the progression from cirrhosis to HCC. Key metabolic biomarkers were further validated using targeted metabolomics in a new independent validation cohort comprising 25 healthy individuals and 25 patients with early-stage hepatocellular carcinoma.

RESULTS

A total of 155 serum metabolites were identified, of which 21/54 metabolites exhibited significant changes in HCC patients compared with cirrhosis patients and healthy individuals, respectively. PLS-DA clustering results demonstrated a significant change trend in the serum metabolic profile of patients with HBV-induced cirrhosis during the progression to HCC. Utilizing LASSO regression and RF algorithms, we confirmed 10 key metabolic biomarkers. Notably, 1-Methylnicotinamide (1-MNAM) exhibited a persistent and significant decrease in healthy individuals, cirrhosis, and HCC patients. Moreover, 1-MNAM levels in developing patients were significantly higher during the cirrhosis stage than in the HCC stage. Targeted metabolomic validation in an external cohort further confirmed the good diagnostic performance of 1-MNAM in early HCC detection.

CONCLUSION

Our findings imply that 1-MNAM may be a specific biomarker for the progression of cirrhosis to HCC.

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].

Performance evaluate of different chemometrics formalisms used for prostate cancer diagnosis by NMR-based metabolomics

INTRODUCTION

In general, two characteristics are ever present in NMR-based metabolomics studies: (1) they are assays aiming to classify the samples in different groups, and (2) the number of samples is smaller than the feature (chemical shift) number. It is also common to observe imbalanced datasets due to the sampling method and/or inclusion criteria. These situations can cause overfitting. However, appropriate feature selection and classification methods can be useful to solve this issue.

OBJECTIVES

Investigate the performance of metabolomics models built from the association between feature selectors, the absence of feature selection, and classification algorithms, as well as use the best performance model as an NMR-based metabolomic method for prostate cancer diagnosis.

METHODS

We evaluated the performance of NMR-based metabolomics models for prostate cancer diagnosis using seven feature selectors and five classification formalisms. We also obtained metabolomics models without feature selection. In this study, thirty-eight volunteers with a positive diagnosis of prostate cancer and twenty-three healthy volunteers were enrolled.

RESULTS

Thirty-eight models obtained were evaluated using AUROC, accuracy, sensitivity, specificity, and kappa's index values. The best result was obtained when Genetic Algorithm was used with Linear Discriminant Analysis with 0.92 sensitivity, 0.83 specificity, and 0.88 accuracy.

CONCLUSION

The results show that the pick of a proper feature selection method and classification model, and a resampling method can avoid overfitting in a small metabolomic dataset. Furthermore, this approach would decrease the number of biopsies and optimize patient follow-up. 1H NMR-based metabolomics promises to be a non-invasive tool in prostate cancer diagnosis.

Recent Analytical Advances for Decoding Metabolic Reprogramming in Lung Cancer

Lung cancer is the leading cause of cancer-related death worldwide. Metabolic reprogramming is a fundamental trait associated with lung cancer development that fuels tumor proliferation and survival. Monitoring such metabolic pathways and their intermediate metabolites can provide new avenues concerning treatment strategies, and the identification of prognostic biomarkers that could be utilized to monitor drug responses in clinical practice. In this review, recent trends in the analytical techniques used for metabolome mapping of lung cancer are capitalized. These techniques include nuclear magnetic resonance (NMR), gas chromatography-mass spectrometry (GC-MS), liquid chromatography-mass spectrometry (LC-MS), and imaging mass spectrometry (MSI). The advantages and limitations of the application of each technique for monitoring the metabolite class or type are also highlighted. Moreover, their potential applications in the analysis of many biological samples will be evaluated.

Compatibility of Nucleobases Containing Pt(II) Complexes with Red Blood Cells for Possible Drug Delivery Applications

The therapeutic advantages of some platinum complexes as major anticancer chemotherapeutic agents and of nucleoside analogue-based compounds as essential antiviral/antitumor drugs are widely recognized. Red blood cells (RBCs) offer a potential new strategy for the targeted release of therapeutic agents due to their biocompatibility, which can protect loaded drugs from inactivation in the blood, thus improving biodistribution. In this study, we evaluated the feasibility of loading model nucleobase-containing Pt(II) complexes into human RBCs that were highly stabilized by four N-donors and susceptible to further modification for possible antitumor/antiviral applications. Specifically, platinum-based nucleoside derivatives [PtII(dien)(N7-Guo)]2+, [PtII(dien)(N7-dGuo)]2+, and [PtII(dien)(N7-dGTP)] (dien = diethylenetriamine; Guo = guanosine; dGuo = 2'-deoxy-guanosine; dGTP = 5'-(2'-deoxy)-guanosine-triphosphate) were investigated. These Pt(II) complexes were demonstrated to be stable species suitable for incorporation into RBCs. This result opens avenues for the possible incorporation of other metalated nucleobases analogues, with potential antitumor and/or antiviral activity, into RBCs.

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.

Glutamine metabolic reprogramming in hepatocellular carcinoma

Hepatocellular carcinoma (HCC) is a lethal disease with limited management strategies and poor prognosis. Metabolism alternations have been frequently unveiled in HCC, including glutamine metabolic reprogramming. The components of glutamine metabolism, such as glutamine synthetase, glutamate dehydrogenase, glutaminase, metabolites, and metabolite transporters, are validated to be potential biomarkers of HCC. Increased glutamine consumption is confirmed in HCC, which fuels proliferation by elevated glutamate dehydrogenase or upstream signals. Glutamine metabolism also serves as a nitrogen source for amino acid or nucleotide anabolism. In addition, more glutamine converts to glutathione as an antioxidant in HCC to protect HCC cells from oxidative stress. Moreover, glutamine metabolic reprogramming activates the mTORC signaling pathway to support tumor cell proliferation. Glutamine metabolism targeting therapy includes glutamine deprivation, related enzyme inhibitors, and transporters inhibitors. Together, glutamine metabolic reprogramming plays a pivotal role in HCC identification, proliferation, and progression.

Metabolite Profiling in a Diet-Induced Obesity Mouse Model and Individuals with Diabetes: A Combined Mass Spectrometry and Proton Nuclear Magnetic Resonance Spectroscopy Study

Mass spectrometry (MS) and nuclear magnetic resonance (NMR) spectroscopy techniques have been used extensively for metabolite profiling. Although combining these two analytical modalities has the potential of enhancing metabolite coverage, such studies are sparse. In this study we test the hypothesis that combining the metabolic information obtained using liquid chromatography (LC) MS and ¹H NMR spectroscopy improves the discrimination of metabolic disease development. We induced metabolic syndrome in male mice using a high-fat diet (HFD) exposure and performed LC-MS and NMR spectroscopy on plasma samples collected after 1 and 8 weeks of dietary intervention. In an orthogonal projection to latent structures (OPLS) analysis, we observed that combining MS and NMR was stronger than each analytical method alone at determining effects of both HFD feeding and time-on-diet. We then tested our metabolomics approach on plasma from 56 individuals from the Malmö Diet and Cancer Study (MDCS) cohort. All metabolic pathways impacted by HFD feeding in mice were confirmed to be affected by diabetes in the MDCS cohort, and most prominent HFD-induced metabolite concentration changes in mice were also associated with metabolic syndrome parameters in humans. The main drivers of metabolic disease discrimination emanating from the present study included plasma levels of xanthine, hippurate, 2-hydroxyisovalerate, S-adenosylhomocysteine and dimethylguanidino valeric acid. In conclusion, our combined NMR-MS approach provided a snapshot of metabolic imbalances in humans and a mouse model, which was improved over employment of each analytical method alone.

Cancer metabolites: promising biomarkers for cancer liquid biopsy

Cancer exerts a multitude of effects on metabolism, including the reprogramming of cellular metabolic pathways and alterations in metabolites that facilitate inappropriate proliferation of cancer cells and adaptation to the tumor microenvironment. There is a growing body of evidence suggesting that aberrant metabolites play pivotal roles in tumorigenesis and metastasis, and have the potential to serve as biomarkers for personalized cancer therapy. Importantly, high-throughput metabolomics detection techniques and machine learning approaches offer tremendous potential for clinical oncology by enabling the identification of cancer-specific metabolites. Emerging research indicates that circulating metabolites have great promise as noninvasive biomarkers for cancer detection. Therefore, this review summarizes reported abnormal cancer-related metabolites in the last decade and highlights the application of metabolomics in liquid biopsy, including detection specimens, technologies, methods, and challenges. The review provides insights into cancer metabolites as a promising tool for clinical applications.

Blood Metabolite Profiling of Antarctic Expedition Members: An 1H NMR Spectroscopy-Based Study

Serum samples from eight participants during the XV winter-over at Concordia base (Antarctic expedition) collected at defined time points, including predeparture, constituted the key substrates for a specific metabolomics study. To ascertain acute changes and chronic adaptation to hypoxia, the metabolic profiles of the serum samples were analyzed using NMR spectroscopy, with principal components analysis (PCA) followed by partial least squares and orthogonal partial least squares discriminant analyses (PLS-DA and OPLS-DA) used as supervised classification methods. Multivariate data analyses clearly highlighted an adaptation period characterized by an increase in the levels of circulating glutamine and lipids, mobilized to supply the body energy needs. At the same time, a reduction in the circulating levels of glutamate and N-acetyl glycoproteins, stress condition indicators, and proinflammatory markers were also found in the NMR data investigation. Subsequent pathway analysis showed possible perturbations in metabolic processes, potentially related to the physiological adaptation, predominantly found by comparing the baseline (at sea level, before mission onset), the base arrival, and the mission ending collected values.

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.

The potential value of LC-MS non-targeted metabonomics in the diagnosis of follicular thyroid carcinoma

BACKGROUND

To explore the metabolic differences of follicular thyroid carcinoma (FTC) by metabonomics, to find potential biomarkers for the diagnosis of FTC, and to explore the pathogenesis and diagnosis and treatment strategies of FTC.

METHOD

The metabonomics of 15 patients with FTC and 15 patients with follicular thyroid nodules(FTN) treated in Henan Cancer Hospital were analyzed by liquid chromatography-mass spectrometry (LC-MS).

RESULTS

The analysis showed that the metabolite profiles of FTC tissues could be well distinguished from those of control tissues, and 6 kinds of lipids were identified respectively, including lysophosphatidic acid(LysoPA) [LysoPA(0:0/18:0),LysoPA(0:0/18:2(9Z,12Z)],LysoPA[20:4(8Z,11Z,14Z,17Z)/0:0)]; phosphatidic acid(PA) [PA(20:3(8Z,11Z,14Z)/0:0),PA(20:4(5Z,8Z,11Z,14Z)/0:0),PA(20:5(5Z,8Z,11Z,14Z,17Z)/0:0)]; lysophosphatidylcholine(LPC) [LPC(18:1),LPC(16:0),LPC[16:1(9Z)/0:0],LPC(17:0),LPC[22:4(7Z,10Z,13Z,16Z),LPC(20:2(11Z,14Z); phosphatidylcholine(PC)(PC(14:0/0:0),PC(16:0/0:0); sphingomyelin(SM) (d18:0/12:0); fatty acid(FA)(18:1(OH3)]. There are 2 kinds of amino acids, including L-glutamate,L-glutamine.There are 3 other metabolites, including retinol,flavin adenine dinucleotide,androsterone glucuronide.Lipid metabolites are the main metabolites in these metabolites.The metabolic pathways related to FTC were analyzed by KEGG and HMDB, and 9 metabolic pathways were found, including 4 amino acid related metabolic pathways, 1 lipid metabolic pathways and 4 other related pathways.

CONCLUSION

There are significant differences in many metabonomic characteristics between FTC and FTN, suggesting that these metabolites can be used as potential biomarkers. Further study found that LysoPA and its analogues can be used as biomarkers in the early diagnosis of FTC.It may be related to the abnormal metabolism of phospholipase D (PLD), the key enzyme of LysoPA synthesis caused by RAS pathway. At the same time, it was found that the metabolic pathway of amino acids and lipids was the main metabolic pathway of FTC. The abnormality of LysoPA may be the cause of follicular tumor carcinogenesis caused by lipid metabolic pathway.

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.

Systematic Review of NMR-Based Metabolomics Practices in Human Disease Research

Nuclear magnetic resonance (NMR) spectroscopy is one of the principal analytical techniques for metabolomics. It has the advantages of minimal sample preparation and high reproducibility, making it an ideal technique for generating large amounts of metabolomics data for biobanks and large-scale studies. Metabolomics is a popular “omics” technology and has established itself as a comprehensive exploratory biomarker tool; however, it has yet to reach its collaborative potential in data collation due to the lack of standardisation of the metabolomics workflow seen across small-scale studies. This systematic review compiles the different NMR metabolomics methods used for serum, plasma, and urine studies, from sample collection to data analysis, that were most popularly employed over a two-year period in 2019 and 2020. It also outlines how these methods influence the raw data and the downstream interpretations, and the importance of reporting for reproducibility and result validation. This review can act as a valuable summary of NMR metabolomic workflows that are actively used in human biofluid research and will help guide the workflow choice for future research.

Precision Medicine Approaches with Metabolomics and Artificial Intelligence

Recent technological innovations in the field of mass spectrometry have supported the use of metabolomics analysis for precision medicine. This growth has been allowed also by the application of algorithms to data analysis, including multivariate and machine learning methods, which are fundamental to managing large number of variables and samples. In the present review, we reported and discussed the application of artificial intelligence (AI) strategies for metabolomics data analysis. Particularly, we focused on widely used non-linear machine learning classifiers, such as ANN, random forest, and support vector machine (SVM) algorithms. A discussion of recent studies and research focused on disease classification, biomarker identification and early diagnosis is presented. Challenges in the implementation of metabolomics-AI systems, limitations thereof and recent tools were also discussed.

Serum Amino Acid Profiles Predict the Development of Hepatocellular Carcinoma in Patients with Chronic HBV Infection

Background: The study aimed to find out the alterations in serum amino acid (AA) profiles and to detect their relationship with carcinoma formation. Methods: Targeted metabolomics based on ultraperformance liquid chromatography triple quadrupole mass spectrometry to quantitatively analyze serum AA levels in 136 hepatitis B (CHB) patients and 93 hepatitis B virus (HBV)-related hepatocellular carcinoma (HCC) patients. Results: It was shown that decreased serum levels of leucine, lysine, threonine, tryptophan, valine, serotonin, and taurine were observed in more HCC patients than CHB patients, but the serum phenylalanine level was increased. Serum valine and serotonin were lower in Class C than Class A and Class B in HCC patients. Accompanied with the higher score of Model for End-Stage Liver Disease, serum phenylalanine was increased not only in CHB patients but also in HCC patients. The serum level of phenylalanine increased in the decompensated stage more than in the compensated stage, while serum leucine and serotonin significantly decreased. Serum serotonin still had significant differences between CHB and HCC both in the HBV desoxyribonucleic acid (HBV-DNA) negative group and in the HBV-DNA positive group. Furthermore, it was shown that the tryptophan ratio, branched-chain amino acids (BCAA)/aromatic amino acids ratio, BCAAs/tyrosine ratio, Fischer's ratio, and serotonin-to-tryptophan ratio significantly decreased, while the tyrosine ratio and the kynurenine-to-tryptophan ratio increased in HCC patients more than those in CHB. Conclusions: A distinct metabolite signature of some specific serum amino acids was found between CHB and HCC patients, which may help predict the development of HCC at an early stage.

Assessment of periportal fibrosis in Schistosomiasis mansoni patients by proton nuclear magnetic resonance-based metabonomics models

BACKGROUND

The evaluation of periportal fibrosis (PPF) is essential for a prognostic assessment of patients with Schistosomiasis mansoni. The WHO Niamey Protocol defines patterns of fibrosis from abdominal ultrasonography, 1H-nuclear magnetic resonance (NMR)-based metabonomics has been employed to assess liver fibrosis in some diseases.

AIM

To build 1H-NMR-based metabonomics models (MM) to discriminate mild from significant periportal PPF and identify differences in the metabolite profiles.

METHODS

A prospective cross-sectional study was performed on schistosomiasis patients at a University Hospital in Northeastern Brazil. We evaluated 41 serum samples from 10 patients with mild PPF (C Niamey pattern) and 31 patients with significant PPF (D/E/F Niamey patterns). MM were built using partial least squares-discriminant analysis (PLS-DA) and orthogonal projections to latent structures discriminant analysis (OPLS-DA) formalisms.

RESULTS

PLS-DA and OPLS-DA resulted in discrimination between mild and significant PPF groups with R2 and Q2 values of 0.80 and 0.38 and 0.72 and 0.42 for each model, respectively. The OPLS-DA model presented accuracy, sensitivity, and specificity values of 92.7%, 90.3%, and 100% to discriminate significant PPF. The metabolites identified as responsible by discrimination were: N-acetylglucosamines, alanine, glycolaldehyde, carbohydrates, and valine.

CONCLUSION

MMs discriminated mild from significant PPF patterns in patients with Schistosomiasis mansoni through identification of differences in serum metabolites profiles.

1H-NMR-Based Metabolomic Study of Potato Cultivars, Markies and Fianna, Exposed to Different Water Regimes

This study investigated the effects of varying soil moisture conditions (through either flooding, drought, or provision of a moderate water supply) on the metabolomic profile of two potato cultivars, namely, Markies and Fianna. Representative tubers of the treated plants were collected 91 days after planting. The samples were freeze-dried, and ground to a fine powder in liquid nitrogen. The fine powder of the tuber samples was analyzed by nuclear magnetic resonance spectroscopy (NMR) to identify their metabolomic profiles. The NMR data was analyzed using principal component analysis and orthogonal partial least square-discriminant analysis to identify any variations between the treatments. In both models, plants exposed to drought clearly separated from the plants that received either excess or moderate water (control). The potato tubers that experienced drought and flood treatments had the highest quantities of aspartic acid, asparagine, and isoleucine. Furthermore, the potatoes exposed to either drought or flood had higher levels of valine and leucine (which are essential for plant defense and resistance against plant pathogens). Potato plants can respond metabolically to varying soil moisture stress.

Material basis research for Echinacea purpurea (L.) Moench against hepatocellular carcinoma in a mouse model through integration of metabonomics and molecular docking

BACKGROUND

Echinacea purpurea (L.) Moench (EP), a well-known "immunostimulant" in the West, is one of the most popular botanicals for patients with cancer. It has been proved to be effective against hepatocellular carcinoma (HCC), while the active ingredients remains unclear.

PURPOSE

This study aimed to investigate the inhibitory effect and interpret the material basis of EP against HCC through metabolomics and molecular docking.

METHODS

Tumor growth, biochemical analysis and pathological changes were detected in HCC-induced mice to evaluate the efficacy of EP. An integrative method combining molecular docking and LC-MS-based metabolomics was performed to evaluate the inhibitory role and screen the material basis of EP against HCC.

RESULTS

EP significantly suppressed tumor growth and decreased the levels of AFP. Histological analysis showed that wide areas of necrosis in the EP-treated tumors that were almost absent in those in model group. Serum metabolomics results revealed EP could significantly improve 12 serum different metabolites induced by HCC, which were involved into phenylalanine, tyrosine and tryptophan biosynthesis and phenylalanine metabolism. Then, 5 related genes were selected out to be the key targets of EP against HCC based on Metscape. 22 identified compounds were docked through Sybyl-X. The herb-compound-gene-metabolic pathways network (HCGMN) was constructed to reveal the associations between EP and HCC. Finally, 19 compounds were screened as promising active ingredients of EP against HCC.

CONCLUSION

The results showed that the approach integrating of metabonomics and molecular docking is a powerful strategy to obtain the active ingredients from plants.

1H-NMR-based metabolomics reveals the biomarker panel and molecular mechanism of hepatocellular carcinoma progression

Hepatocellular carcinoma (HCC) is one of the most extensive and most deadly cancers in the world. Biomarkers for early diagnosis of HCC are still lacking, and noninvasive and effective biomarkers are urgently needed. Metabolomics is committed to studying the changes of metabolites under stimulation, and provides a new approach for discovery of potential biomarkers. In the current work, 1H nuclear magnetic resonance (NMR) metabolomics approach was utilized to explore the potential biomarkers in HCC progression, and the biomarker panel was evaluated by receiver operating characteristic (ROC) curve analyses. Our results revealed that a biomarker panel consisting of hippurate, creatinine, putrescine, choline, and taurine might be involved in HCC progression. Functional pathway analysis showed that taurine and hypotaurine metabolism is markedly involved in the occurrence and development of HCC. Furthermore, our results indicated that the TPA activity and the level and expression of PKM2 were gradually increased in HCC progression. This research provides a scientific basis for screening potential biomarkers of HCC.

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.

Comparative metabolomic study of high-flux hemodialysis and high volume online hemodiafiltration in the removal of uremic toxins using 1H NMR spectroscopy

Uremic toxins (UTs) accumulate in the circulation of patients with chronic kidney disease (CKD). High volume hemodiafiltration (HDF) improves clearance of low and medium molecular weight UTs compared to HD. The present study is a post-hoc analysis comparing the metabolomic profile in serum from patients under high flux HD (hf-HD) and HDF in HDFIT, a multicentric randomized controlled trial (RCTs). Per protocol, serum samples were collected pre- and post- dialysis treatments at randomization (baseline) and at the end of the follow up (6 months) and stored in a biorepository. Random (pre- and post-dialysis) samples from nine patients in study arm were selected at baseline and at the end of the follow up. To compare the samples, 26 possibly matching metabolites were identified by a t-test among the four groups using ¹H nuclear magnetic resonance (NMR). To evaluate the comparison between the modalities is a single treatment session, the clearance rates (CRs) of each metabolite were calculated based on pre-dialysis and post-dialysis samples. In addition, to evaluate to effect of UT removal during the trial follow up period, the pre-dialysis metabolite concentrations at the baseline and at 6 months were compared among the two arms of the study. There was no significant difference between in the single session CRs of metabolites when hf-HD and HDF were compared. On the other hand, the comparison between baseline and 6-month (long-term evolution) led to the identification of 16 metabolites that differentiated the hf-HD and the HDF evolutions. Most of these 16 metabolites are involved in several important metabolic pathways, such as metabolism of phenylalanine and biosynthesis of phenylalanine, tyrosine, and tryptophan, which are related to UTs and cardiovascular disease development. Although no difference was observed between hf-HD and HDF samples before and after a single session, concentrations of CKD-relevant metabolites and associated pathologies were stable in the HDF samples, but not in the hf-HD samples, over the six-month period, suggesting that HDF enhances long-term stability.

Comprehensive evaluation integrating omics strategy and machine learning algorithms for consistency of calculus bovis from different sources

In the clinical application of Traditional Chinese Medicine (TCM) substitutes, the consistency evaluation of TCM substitutes from different sources is recognized as the main bottleneck. As the most widely used analytical method in TCM consistency evaluation, fingerprint similarity evaluation suffers from insufficient method sensitivity and poor conformity with the actual characteristics of TCM, which is difficult to adapt to the analytical needs of complex substance systems of TCM. This work aims to develop an effective and more accurate method for consistency evaluation using omics strategy and machine learning algorithms. The natural calculus bovis (NCB) were graded into three groups according to the similarity to in vitro cultured bovis (IVCB), and chemical markers between samples of each grade were screened out. Support vector machine (SVM) models with different kernels were then constructed by using the chemical markers as feature variables. The results showed that the classification accuracy of the SVM classifier of NCB and the consistency evaluation SVM model classifier was 95.74% and 100.0%, respectively. The approach demonstrated in the study presented a good analytical performance with higher sensitivity, accuracy for consistency evaluation of TCM.

The Application Value of Lipoprotein Particle Numbers in the Diagnosis of HBV-Related Hepatocellular Carcinoma with BCLC Stage 0-A

Early diagnosis is essential for improving the prognosis and survival of patients with hepatocellular carcinoma (HCC). This study aims to explore the clinical value of lipoprotein subfractions in the diagnosis of hepatitis B virus (HBV)-related HCC. Lipoprotein subfractions were detected by ¹H-NMR spectroscopy, and the pattern-recognition method and binary logistic regression were performed to classify distinct serum profiles and construct prediction models for HCC diagnosis. Differentially expressed proteins associated with lipid metabolism were detected by LC-MS/MS, and the potential prognostic significance of the mRNA expression was evaluated by Kaplan–Meier survival analysis. The diagnostic panel constructed from the serum particle number of very-low-density lipoprotein (VLDL), intermediate-density lipoprotein (IDL), and low-density lipoprotein (LDL-1~LDL-6) achieved higher accuracy for the diagnosis of HBV-related HCC and HBV-related benign liver disease (LD) than that constructed from serum alpha-fetoprotein (AFP) alone in the training set (AUC: 0.850 vs. AUC: 0.831) and validation set (AUC: 0.926 vs. AUC: 0.833). Furthermore, the panel achieved good diagnostic performance in distinguishing AFP-negative HCC from AFP-negative LD (AUC: 0.773). We also found that lipoprotein lipase (LPL) transcript levels showed a significant increase in cancerous tissue and that high expression was significantly positively correlated with the poor prognosis of patients. Our research provides new insight for the development of diagnostic biomarkers for HCC, and abnormal lipid metabolism and LPL-mediated abnormal serum lipoprotein metabolism may be important factors in promoting HCC development.

Aqueous Metabolite Trends for the Progression of Nonalcoholic Fatty Liver Disease in Female Bariatric Surgery Patients by Targeted 1H-NMR Metabolomics

Determining biomarkers and better characterizing the biochemical progression of nonalcoholic fatty liver disease (NAFLD) remains a clinical challenge. A targeted ¹H-NMR study of serum, combined with clinical variables, detected and localized biomarkers to stages of NAFLD in morbidly obese females. Pre-surgery serum samples from 100 middle-aged, morbidly obese female subjects, grouped on gold-standard liver wedge biopsies (non-NAFLD; steatosis; and fibrosis) were collected, extracted, and analyzed in aqueous (D₂O) buffer (¹H, 600 MHz). Profiled concentrations were subjected to exploratory statistical analysis. Metabolites varying significantly between the non-NAFLD and steatosis groups included the ketone bodies 3-hydroxybutyrate (↓; p = 0.035) and acetone (↓; p = 0.012), and also alanine (↑; p = 0.004) and a putative pyruvate signal (↑; p = 0.003). In contrast, the steatosis and fibrosis groups were characterized by 2-hydroxyisovalerate (↑; p = 0.023), betaine (↓; p = 0.008), hypoxanthine (↓; p = 0.003), taurine (↓; p = 0.001), 2-hydroxybutyrate (↑; p = 0.045), 3-hydroxyisobutyrate (↑; p = 0.046), and increasing medium chain fatty acids. Exploratory classification models with and without clinical variables exhibited overall success rates ca. 75–85%. In the study conditions, inhibition of fatty acid oxidation and disruption of the hepatic urea cycle are supported as early features of NAFLD that continue in fibrosis. In fibrosis, markers support inflammation, hepatocyte damage, and decreased liver function. Complementarity of NMR concentrations and clinical information in classification models is shown. A broader hypothesis that standard-of-care sera can yield metabolomic information is supported.

A Cross-Platform Metabolomics Comparison Identifies Serum Metabolite Signatures of Liver Fibrosis Progression in Chronic Hepatitis C Patients

Metabolomics offers new insights into disease mechanisms that is enhanced when adopting orthogonal instrumental platforms to expand metabolome coverage, while also reducing false discoveries by independent replication. Herein, we report the first inter-method comparison when using multisegment injection-capillary electrophoresis-mass spectrometry (MSI-CE-MS) and nuclear magnetic resonance (NMR) spectroscopy for characterizing the serum metabolome of patients with liver fibrosis in chronic hepatitis C virus (HCV) infection (n = 20) and non-HCV controls (n = 14). In this study, 60 and 30 serum metabolites were detected frequently (>75%) with good technical precision (median CV < 10%) from serum filtrate samples (n = 34) when using standardized protocols for MSI-CE-MS and NMR, respectively. Also, 20 serum metabolite concentrations were consistently measured by both methods over a 500-fold concentration range with an overall mean bias of 9.5% (n = 660). Multivariate and univariate statistical analyses independently confirmed that serum choline and histidine were consistently elevated (p < 0.05) in HCV patients with late-stage (F2-F4) as compared to early-stage (F0-F1) liver fibrosis. Overall, the ratio of serum choline to uric acid provided optimal differentiation of liver disease severity (AUC = 0.848, p = 0.00766) using a receiver operating characteristic curve, which was positively correlated with liver stiffness measurements by ultrasound imaging (r = 0.606, p = 0.0047). Moreover, serum 5-oxo-proline concentrations were higher in HCV patients as compared to non-HCV controls (F = 4.29, p = 0.0240) after adjustment for covariates (age, sex, BMI), indicative of elevated oxidative stress from glutathione depletion with the onset and progression of liver fibrosis. Both instrumental techniques enable rapid yet reliable quantification of serum metabolites in large-scale metabolomic studies with good overlap for biomarker replication. Advantages of MSI-CE-MS include greater metabolome coverage, lower operating costs, and smaller sample volume requirements, whereas NMR offers a robust platform supported by automated spectral and data processing software.

Sweat metabolome and proteome: Recent trends in analytical advances and potential biological functions

Metabolome and proteome profiling of biofluids, e.g., urine, plasma, has generated vast and ever-increasing amounts of knowledge over the last few decades. Paradoxically, omics analyses of sweat, one of the most readily available human biofluids, have lagged behind. This review capitalizes on the current knowledge and state of the art analytical advances of sweat metabolomics and proteomics. Moreover, current applications of sweat omics such as the discovery of disease biomarkers and monitoring athletic performance are also presented in this review. Another area of emerging knowledge that has been highlighted herein lies in the role of skin host-microbiome interactions in shaping the sweat metabolite-protein profiles. Discussion of future research directions describes the need to have a better grasp of sweat chemicals and to better understand how they function as aided by advances in omics tools. Overall, the role of sweat as an information-rich biofluid that could complement the exploration of the skin metabolome/proteome is emphasized.

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.

1 H NMR-based metabolomics of paired esophageal tumor tissues and serum samples identifies specific serum biomarkers for esophageal cancer

Serum metabolites of healthy controls and esophageal cancer (EC) patients have previously been compared to predict cancer-specific profiles. However, the association between metabolic alterations in serum samples and esophageal tissues in EC patients remains unclear. Here, we analyzed 50 pairs of EC tissues and distant noncancerous tissues, together with patient-matched serum samples, using ¹ H NMR spectroscopy and pattern recognition algorithms. EC patients could be differentiated from the controls based on the metabolic profiles at tissue and serum levels. Some overlapping discriminatory metabolites, including valine, alanine, glucose, acetate, citrate, succinate and glutamate, were identified in both matrices. These results suggested deregulation of metabolic pathways, and potentially revealed the links between EC and several metabolic pathways, such as the tricarboxylic acid cycle, glutaminolysis, short-chain fatty acid metabolism, lipometabolism and pyruvate metabolism. Perturbation of the pyruvate metabolism was most strongly associated with EC progression. Consequently, an optimal serum metabolite biomarker panel comprising acetate and pyruvate was developed, as these two metabolites are involved in pyruvate metabolism, and changes in their serum levels were significantly correlated with alterations in the levels of some other esophageal tissue metabolites. In comparison with individual biomarkers, this panel exhibited better diagnostic efficiency for EC, with an AUC of 0.948 in the test set, and a good predictive ability of 82.5% in the validation set. Analysis of key genes related to pyruvate metabolism in EC patients revealed patterns corresponding to the changes in serum pyruvate and acetate levels. These correlation analyses demonstrate that there were distinct metabolic characteristics and pathway aberrations in the esophageal tumor tissue and in the serum. Changes in the serum metabolic signatures could reflect the alterations in the esophageal tumor profile, thereby emphasizing the importance of distinct serum metabolic profiles as potential noninvasive biomarkers for EC.

Urinary Metabolic Biomarkers in Cancer Patients: An Overview

The pathogenesis of cancer involves multiple molecular alterations at the level of genome, epigenome, and stromal environment, resulting in several deregulated signal transduction pathways. Metabolites are not only end products of gene and protein expression but also a consequence of the mutual relationship between the genome and the internal environment. Considering that metabolites serve as a comprehensive chemical fingerprint of cell metabolism, metabolomics is emerging as the method able to discover metabolite biomarkers that can be developed for early cancer detection, prognosis, and response to treatment. Urine represents a noninvasive source, available and rich in metabolites, useful for cancer diagnosis, prognosis, and treatment monitoring. In this chapter, we reported the main published evidences on urinary metabolic biomarkers in the studied cancers related to hepatopancreatic and urinary tract with the aim at discussing their promising role in clinical practice.

1H-NMR metabolomics reveals a multitarget action of Crithmum maritimum ethyl acetate extract in inhibiting hepatocellular carcinoma cell growth

Hepatocellular carcinoma (HCC) is nowadays the sixth cause of tumour-related deceases worldwide, estimated to become the third in Western countries by 2030. New drugs for HCC treatment still have many adverse effects. Several lines of evidence indicate that plant metabolites offer concrete opportunities for developing new therapeutic strategies for many diseases, including cancer. We previously reported that ethyl acetate extract of a spontaneous edible plant harvested in Apulia, Crithmum maritimum, significantly inhibited cell growth in HCC cells. By ¹H-NMR spectroscopy, here we show that Crithmum maritimum ethyl acetate extract counteracts the Warburg effect, by reducing intracellular lactate, inhibits protein anabolism, by decreasing amino acid level, and affects membrane biosynthesis by lowering choline and phosphocholine. Also, we observed an effect on lipid homeostasis, with a reduction in triglycerides, cholesterol, monounsaturated fatty acids (MUFA), and diunsaturated fatty acids (DUFA), and an increase in polyunsaturated fatty acids (PUFA). Taken together, these data demonstrate that Crithmum maritimum-induced cytostasis is exerted through a multi-effect action, targeting key metabolic processes in HCC cells. Overall, our findings highlight the role of Crithmum maritimum as a promising tool for the prevention and the improvement of the therapeutic options for HCC and other types of tumours.

Decanoic Acid and Not Octanoic Acid Stimulates Fatty Acid Synthesis in U87MG Glioblastoma Cells: A Metabolomics Study

Medium-chain fatty acids (MCFA) are dietary components with a chain length ranging from 6 to 12 carbon atoms. MCFA can cross the blood-brain barrier and in the brain can be oxidized through mitochondrial β-oxidation. As components of ketogenic diets, MCFA have demonstrated beneficial effects on different brain diseases, such as traumatic brain injury, Alzheimer’s disease, drug-resistant epilepsy, diabetes, and cancer. Despite the interest in MCFA effects, not much information is available about MCFA metabolism in the brain. In this study, with a gas chromatography-mass spectrometry (GC-MS)-based metabolomics approach, coupled with multivariate data analyses, we followed the metabolic changes of U87MG glioblastoma cells after the addition of octanoic (C8), or decanoic (C10) acids for 24 h. Our analysis highlighted significant differences in the metabolism of U87MG cells after the addition of C8 or C10 and identified several metabolites whose amount changed between the two groups of treated cells. Overall, metabolic pathway analyses suggested the citric acid cycle, Warburg effect, glutamine/glutamate metabolism, and ketone body metabolism as pathways influenced by C8 or C10 addition to U87MG cells. Our data demonstrated that, while C8 affected mitochondrial metabolism resulting in increased ketone body production, C10 mainly influenced cytosolic pathways by stimulating fatty acid synthesis. Moreover, glutamine might be the main substrate to support fatty acids synthesis in C10-treated cells. In conclusion, we identified a metabolic signature associated with C8 or C10 addition to U87MG cells that can be used to decipher metabolic responses of glioblastoma cells to MCFA treatment.

Sorafenib for the Treatment of Unresectable Hepatocellular Carcinoma: Preliminary Toxicity and Activity Data in Dogs

Unresectable nodular and diffuse hepatocellular carcinoma (HCC) have a poor prognosis with limited treatment options. Systemic traditional chemotherapy has been only rarely reported, with unsatisfactory results. The aim of this prospective, non-randomized, non-blinded, single center clinical trial was to investigate safety profile, objective response rate, time to progression and overall survival of sorafenib in comparison with metronomic chemotherapy (MC) consisting of thalidomide, piroxicam and cyclophosphamide in dogs with advanced, unresectable HCC. Between December 2011 and June 2017, 13 dogs were enrolled: seven received sorafenib, and six were treated with MC. Median time to progression was 363 days (95% CI, 191–535) in dogs treated with sorafenib versus 27 days (95% CI, 0–68) in dogs treated with MC (p = 0.044). Median overall survival was 361 days (95% CI, 0–909) in dogs receiving sorafenib, while 32 days (95% CI, 0–235) in those receiving MC (p = 0.079). Sorafenib seems to be a good candidate for the treatment of dogs with advanced HCC, due to a benefit in disease control and an acceptable safety profile, offering a good basis on which new randomized prospective clinical trials should be undertaken to compare the efficacy and drawback of sorafenib versus MC or traditional chemotherapy.

Shenling Baizhu San improves functional dyspepsia in rats as revealed by 1H-NMR based metabolomics

Functional dyspepsia (FD), a common gastrointestinal disorder around the world, is driven by multiple factors, making prevention and treatment a major challenge. Shenling Baizhu San (SBS), a classical prescription of traditional Chinese medicine, has been proven to be effective in gastrointestinal disorders. However, studies on SBS improving FD are few. Thus, our study aimed to evaluate the effect of SBS on FD and further to explore the mechanism underlying the interactions between FD and SBS by the metabolomics approach. A FD rat model was induced by multiple forms of mild stimulation, and proton nuclear magnetic resonance (1H-NMR) spectroscopy and multivariate data analysis were used to profile the fecal and urinary metabolome in the FD rats during SBS intervention. Significant dyspeptic symptoms such as weight loss, poor appetite, reduced gastrointestinal motility and decreased absorptive capacity were observed in the FD rats, which were subsequently improved by SBS. Additionally, the levels of citrate, branched chain acids and pyruvate decreased, and the levels of choline, trimethylamine and taurine increased in the FD rats. Furthermore, the metabolic disorders were amended with SBS intervention mainly by modulating the metabolic pathways involved in energy metabolism, amino acid metabolism, and gut microbiota and host co-metabolism. Overall, our study highlighted the effect of SBS on the disturbed metabolic pathways in the FD rats, providing new insight into the mechanism of SBS treatment for FD from the perspective of metabolomics.